model_id
stringlengths 7
105
| model_card
stringlengths 1
130k
| model_labels
listlengths 2
80k
|
|---|---|---|
XuanLoc/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0729
- Accuracy: 0.9752
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2762 | 1.0 | 190 | 0.1146 | 0.9626 |
| 0.1679 | 2.0 | 380 | 0.0770 | 0.9730 |
| 0.129 | 3.0 | 570 | 0.0729 | 0.9752 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
dingusagar/vit-base-avengers-v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-avengers-v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2542
- Accuracy: 0.9125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
" black widow",
" captain america",
" thor",
"iron man"
] |
ambarboza/vit-base-patch16-224-finetuned-flower
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.1.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
wucng/res18_flower_c5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# res18_flower_c5
This model is a fine-tuned version of [wucng/custom-resnet18](https://huggingface.co/wucng/custom-resnet18) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2866
- Accuracy: 0.8954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4384 | 1.0 | 46 | 0.2866 | 0.8954 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
enverkulahli/cat-sounds2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cat-sounds2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2381
- Accuracy: 0.9504
- F1: 0.9503
- Precision: 0.9510
- Recall: 0.9504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0794 | 1.0 | 297 | 0.3034 | 0.9277 | 0.9280 | 0.9297 | 0.9277 |
| 0.0764 | 2.0 | 594 | 0.2728 | 0.9386 | 0.9379 | 0.9391 | 0.9386 |
| 0.0331 | 3.0 | 891 | 0.2381 | 0.9504 | 0.9503 | 0.9510 | 0.9504 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angry",
"defence",
"fighting",
"happy",
"huntingmind",
"mating",
"mothercall",
"paining",
"resting",
"warning"
] |
Professor/CGIAR-Crop-disease
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CGIAR-Crop-disease
This model is a fine-tuned version of [gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease](https://huggingface.co/gianlab/swin-tiny-patch4-window7-224-finetuned-plantdisease) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7438
- Accuracy: 0.6964
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0386 | 1.0 | 652 | 0.9385 | 0.5669 |
| 0.9619 | 2.0 | 1304 | 0.9422 | 0.5811 |
| 0.9193 | 3.0 | 1956 | 0.8806 | 0.6348 |
| 0.8876 | 4.0 | 2608 | 0.8703 | 0.6488 |
| 0.8777 | 5.0 | 3260 | 0.8361 | 0.6607 |
| 0.863 | 6.0 | 3912 | 0.8543 | 0.6417 |
| 0.8316 | 7.0 | 4564 | 0.8101 | 0.6607 |
| 0.8301 | 8.0 | 5216 | 0.8197 | 0.6609 |
| 0.8264 | 9.0 | 5868 | 0.8111 | 0.6720 |
| 0.8283 | 10.0 | 6520 | 0.8065 | 0.6669 |
| 0.816 | 11.0 | 7172 | 0.8115 | 0.6578 |
| 0.8263 | 12.0 | 7824 | 0.8029 | 0.6753 |
| 0.8017 | 13.0 | 8476 | 0.7929 | 0.6707 |
| 0.8005 | 14.0 | 9128 | 0.8025 | 0.6661 |
| 0.7989 | 15.0 | 9780 | 0.8153 | 0.6594 |
| 0.7961 | 16.0 | 10432 | 0.8033 | 0.6720 |
| 0.7769 | 17.0 | 11084 | 0.7879 | 0.6682 |
| 0.7757 | 18.0 | 11736 | 0.7868 | 0.6732 |
| 0.7713 | 19.0 | 12388 | 0.7773 | 0.6747 |
| 0.7638 | 20.0 | 13040 | 0.7678 | 0.6811 |
| 0.7645 | 21.0 | 13692 | 0.7826 | 0.6795 |
| 0.7497 | 22.0 | 14344 | 0.7931 | 0.6807 |
| 0.761 | 23.0 | 14996 | 0.7719 | 0.6820 |
| 0.7486 | 24.0 | 15648 | 0.7641 | 0.6895 |
| 0.7446 | 25.0 | 16300 | 0.7686 | 0.6832 |
| 0.7418 | 26.0 | 16952 | 0.7683 | 0.6904 |
| 0.7344 | 27.0 | 17604 | 0.7549 | 0.6895 |
| 0.7369 | 28.0 | 18256 | 0.7501 | 0.6891 |
| 0.7238 | 29.0 | 18908 | 0.7454 | 0.6933 |
| 0.7264 | 30.0 | 19560 | 0.7565 | 0.6876 |
| 0.7185 | 31.0 | 20212 | 0.7524 | 0.6880 |
| 0.7112 | 32.0 | 20864 | 0.7712 | 0.6807 |
| 0.7073 | 33.0 | 21516 | 0.7532 | 0.6897 |
| 0.7102 | 34.0 | 22168 | 0.7457 | 0.6960 |
| 0.7053 | 35.0 | 22820 | 0.7438 | 0.6964 |
| 0.6979 | 36.0 | 23472 | 0.7449 | 0.6933 |
| 0.6973 | 37.0 | 24124 | 0.7477 | 0.6929 |
| 0.6967 | 38.0 | 24776 | 0.7508 | 0.6926 |
| 0.6939 | 39.0 | 25428 | 0.7481 | 0.6933 |
| 0.6936 | 40.0 | 26080 | 0.7460 | 0.6968 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"wd",
"other",
"g",
"dr",
"nd"
] |
anum231/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# anum231/food_classifier
This model is a fine-tuned version of [anum231/cancer_classifier_100](https://huggingface.co/anum231/cancer_classifier_100) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5815
- Validation Loss: 0.4561
- Train Accuracy: 0.8276
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6210 | 0.4706 | 0.8276 | 0 |
| 0.6095 | 0.4583 | 0.8103 | 1 |
| 0.6289 | 0.4566 | 0.8103 | 2 |
| 0.6230 | 0.5850 | 0.7241 | 3 |
| 0.5815 | 0.4561 | 0.8276 | 4 |
### Framework versions
- Transformers 4.38.1
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"bcc",
"scc",
"iec"
] |
GGital/vit-Covid
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-Covid
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0805
- Accuracy: 0.9847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1283 | 0.38 | 100 | 0.1878 | 0.9484 |
| 0.0312 | 0.76 | 200 | 0.1484 | 0.9560 |
| 0.0655 | 1.15 | 300 | 0.0976 | 0.9713 |
| 0.0587 | 1.53 | 400 | 0.0887 | 0.9713 |
| 0.0106 | 1.91 | 500 | 0.0980 | 0.9732 |
| 0.0137 | 2.29 | 600 | 0.1479 | 0.9618 |
| 0.07 | 2.67 | 700 | 0.0882 | 0.9751 |
| 0.0068 | 3.05 | 800 | 0.1160 | 0.9675 |
| 0.0321 | 3.44 | 900 | 0.0872 | 0.9694 |
| 0.0027 | 3.82 | 1000 | 0.0790 | 0.9809 |
| 0.0041 | 4.2 | 1100 | 0.1029 | 0.9713 |
| 0.0014 | 4.58 | 1200 | 0.0947 | 0.9809 |
| 0.0018 | 4.96 | 1300 | 0.1399 | 0.9713 |
| 0.001 | 5.34 | 1400 | 0.0689 | 0.9847 |
| 0.001 | 5.73 | 1500 | 0.0852 | 0.9790 |
| 0.0008 | 6.11 | 1600 | 0.1111 | 0.9790 |
| 0.0013 | 6.49 | 1700 | 0.0695 | 0.9866 |
| 0.0049 | 6.87 | 1800 | 0.0728 | 0.9885 |
| 0.0007 | 7.25 | 1900 | 0.0963 | 0.9790 |
| 0.0012 | 7.63 | 2000 | 0.0886 | 0.9847 |
| 0.0006 | 8.02 | 2100 | 0.0811 | 0.9847 |
| 0.0015 | 8.4 | 2200 | 0.0796 | 0.9847 |
| 0.0143 | 8.78 | 2300 | 0.0804 | 0.9847 |
| 0.0005 | 9.16 | 2400 | 0.0816 | 0.9847 |
| 0.0006 | 9.54 | 2500 | 0.0811 | 0.9847 |
| 0.0005 | 9.92 | 2600 | 0.0805 | 0.9847 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"covid",
"normal",
"pneumonia"
] |
anum231/cancer_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# anum231/cancer_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9100
- Validation Loss: 0.9439
- Train Accuracy: 0.5862
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.0608 | 1.0304 | 0.4828 | 0 |
| 1.0179 | 1.0202 | 0.4828 | 1 |
| 0.9865 | 1.0000 | 0.4828 | 2 |
| 0.9464 | 0.9694 | 0.5690 | 3 |
| 0.9100 | 0.9439 | 0.5862 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"bcc",
"iec",
"scc"
] |
anum231/cancer_classifier_100
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# anum231/cancer_classifier_100
This model is a fine-tuned version of [anum231/cancer_classifier_100](https://huggingface.co/anum231/cancer_classifier_100) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5354
- Validation Loss: 0.8077
- Train Accuracy: 0.6724
- Epoch: 22
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.0644 | 0.9210 | 0.5690 | 0 |
| 0.8927 | 0.8785 | 0.5345 | 1 |
| 0.8065 | 0.9131 | 0.6379 | 2 |
| 0.7085 | 0.7569 | 0.7241 | 3 |
| 0.7407 | 0.7963 | 0.6897 | 4 |
| 0.6635 | 0.8031 | 0.6897 | 5 |
| 0.7505 | 0.8074 | 0.6552 | 6 |
| 0.6149 | 0.8540 | 0.6379 | 7 |
| 0.6530 | 0.7823 | 0.6379 | 8 |
| 0.5969 | 0.8384 | 0.6552 | 9 |
| 0.6808 | 0.7863 | 0.6552 | 10 |
| 0.6269 | 0.8650 | 0.6552 | 11 |
| 0.5665 | 0.7941 | 0.6897 | 12 |
| 0.6414 | 0.8927 | 0.6552 | 13 |
| 0.7304 | 0.9703 | 0.6034 | 14 |
| 0.5518 | 0.9204 | 0.6552 | 15 |
| 0.6184 | 0.8850 | 0.6897 | 16 |
| 0.6397 | 0.8827 | 0.6724 | 17 |
| 0.5697 | 0.8658 | 0.6207 | 18 |
| 0.6103 | 0.8177 | 0.6379 | 19 |
| 0.5541 | 0.8526 | 0.6552 | 20 |
| 0.5831 | 0.8632 | 0.6379 | 21 |
| 0.5354 | 0.8077 | 0.6724 | 22 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"bcc",
"scc",
"iec"
] |
weightbot/vit-base-patch16-224-in21k-CDCC
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-CDCC
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9010
- Accuracy: 0.6006
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0151 | 1.0 | 586 | 1.0188 | 0.4931 |
| 0.9755 | 2.0 | 1172 | 0.9591 | 0.5558 |
| 0.8769 | 3.0 | 1758 | 0.9301 | 0.5974 |
| 0.8852 | 4.0 | 2345 | 0.9086 | 0.6025 |
| 0.8751 | 5.0 | 2930 | 0.9010 | 0.6006 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"g",
"wd",
"dr",
"other",
"nd"
] |
DScomp380/vit-b16-plant_village
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-b16-plant_village
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Treelar/plant_village dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0100
- Accuracy: 0.9973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1747 | 1.0 | 3119 | 0.0364 | 0.9885 |
| 0.0031 | 2.0 | 6238 | 0.0100 | 0.9973 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"apple___apple_scab",
"apple___black_rot",
"apple___cedar_apple_rust",
"apple___healthy",
"background_without_leaves",
"blueberry___healthy",
"cherry___powdery_mildew",
"cherry___healthy",
"corn___cercospora_leaf_spot gray_leaf_spot",
"corn___common_rust",
"corn___northern_leaf_blight",
"corn___healthy",
"grape___black_rot",
"grape___esca_(black_measles)",
"grape___leaf_blight_(isariopsis_leaf_spot)",
"grape___healthy",
"orange___haunglongbing_(citrus_greening)",
"peach___bacterial_spot",
"peach___healthy",
"pepper,_bell___bacterial_spot",
"pepper,_bell___healthy",
"potato___early_blight",
"potato___late_blight",
"potato___healthy",
"raspberry___healthy",
"soybean___healthy",
"squash___powdery_mildew",
"strawberry___leaf_scorch",
"strawberry___healthy",
"tomato___bacterial_spot",
"tomato___early_blight",
"tomato___late_blight",
"tomato___leaf_mold",
"tomato___septoria_leaf_spot",
"tomato___spider_mites two-spotted_spider_mite",
"tomato___target_spot",
"tomato___tomato_yellow_leaf_curl_virus",
"tomato___tomato_mosaic_virus",
"tomato___healthy"
] |
weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-plant-classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6592
- Accuracy: 0.7557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8257 | 1.0 | 268 | 0.7941 | 0.6695 |
| 0.7235 | 2.0 | 537 | 0.7696 | 0.6695 |
| 0.6939 | 3.0 | 806 | 0.7428 | 0.6724 |
| 0.665 | 4.0 | 1075 | 0.6884 | 0.7328 |
| 0.6846 | 5.0 | 1343 | 0.7144 | 0.6954 |
| 0.6391 | 6.0 | 1612 | 0.6854 | 0.7155 |
| 0.6172 | 7.0 | 1881 | 0.6698 | 0.7011 |
| 0.6332 | 8.0 | 2150 | 0.6510 | 0.7126 |
| 0.5679 | 9.0 | 2418 | 0.6323 | 0.7299 |
| 0.5109 | 10.0 | 2687 | 0.6629 | 0.7098 |
| 0.5594 | 11.0 | 2956 | 0.6556 | 0.7270 |
| 0.4874 | 12.0 | 3225 | 0.6627 | 0.7155 |
| 0.4687 | 13.0 | 3493 | 0.6645 | 0.7299 |
| 0.4686 | 14.0 | 3762 | 0.6469 | 0.7213 |
| 0.4862 | 15.0 | 4031 | 0.6602 | 0.7356 |
| 0.4432 | 16.0 | 4300 | 0.6550 | 0.7270 |
| 0.4368 | 17.0 | 4568 | 0.6472 | 0.7385 |
| 0.3815 | 18.0 | 4837 | 0.6557 | 0.7557 |
| 0.3674 | 19.0 | 5106 | 0.6638 | 0.7529 |
| 0.4224 | 19.94 | 5360 | 0.6592 | 0.7557 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
mhgun/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"leaf",
"not_leaf"
] |
mhgun/leafer
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6212
- Accuracy: 0.7222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 1 | 0.7020 | 0.4444 |
| No log | 1.6 | 2 | 0.6563 | 0.6667 |
| No log | 2.4 | 3 | 0.6212 | 0.7222 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"leaf",
"not_leaf"
] |
anum231/class2_v1
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# anum231/class2_v1
This model is a fine-tuned version of [anum231/class2_v1](https://huggingface.co/anum231/class2_v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4768
- Validation Loss: 0.3740
- Train Accuracy: 0.8966
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.9471 | 0.7465 | 0.4828 | 0 |
| 0.7152 | 0.6636 | 0.5862 | 1 |
| 0.6634 | 0.6322 | 0.6207 | 2 |
| 0.6447 | 0.5829 | 0.6897 | 3 |
| 0.6256 | 0.5359 | 0.7586 | 4 |
| 0.6044 | 0.4895 | 0.8621 | 5 |
| 0.5432 | 0.4623 | 0.8966 | 6 |
| 0.5232 | 0.4666 | 0.8621 | 7 |
| 0.5435 | 0.4061 | 0.8966 | 8 |
| 0.4768 | 0.3740 | 0.8966 | 9 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"scc",
"iec"
] |
weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification
This model is a fine-tuned version of [weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification](https://huggingface.co/weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4259
- Accuracy: 0.8351
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4225 | 1.0 | 183 | 0.3415 | 0.8703 |
| 0.4843 | 2.0 | 367 | 0.4024 | 0.8381 |
| 0.4679 | 3.0 | 550 | 0.4014 | 0.8385 |
| 0.4431 | 4.0 | 734 | 0.3986 | 0.8331 |
| 0.4263 | 5.0 | 917 | 0.4119 | 0.8351 |
| 0.3869 | 6.0 | 1101 | 0.4217 | 0.8278 |
| 0.3432 | 7.0 | 1284 | 0.4229 | 0.8305 |
| 0.3522 | 8.0 | 1468 | 0.4283 | 0.8347 |
| 0.3337 | 9.0 | 1651 | 0.4180 | 0.8301 |
| 0.2963 | 10.0 | 1835 | 0.4219 | 0.8366 |
| 0.3025 | 11.0 | 2018 | 0.4236 | 0.8335 |
| 0.2751 | 12.0 | 2202 | 0.4238 | 0.8366 |
| 0.271 | 13.0 | 2385 | 0.4314 | 0.8324 |
| 0.2416 | 14.0 | 2569 | 0.4229 | 0.8328 |
| 0.2507 | 14.96 | 2745 | 0.4259 | 0.8351 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
cquentin48/deep_learning
|
# Deep learning Project
Created by Joseph HUBERT, Victor DUPRIEZ, ABDELMOULA Oussema and Quentin CHAPEL
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification-ft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification-ft
This model is a fine-tuned version of [weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification](https://huggingface.co/weightbot/swin-tiny-patch4-window7-224-finetuned-plant-classification-finetuned-crops-classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3404
- Accuracy: 0.8774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4665 | 1.0 | 201 | 0.3881 | 0.8352 |
| 0.4054 | 2.0 | 403 | 0.3799 | 0.8582 |
| 0.3735 | 2.99 | 603 | 0.3404 | 0.8774 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"dr",
"g",
"nd",
"wd",
"other"
] |
Santipab/Esan-code-Maimeetrang-model-action-recognition
|
โมเดลนี้ได้ใช้เทคนิค Transfers Learning กับ Pre-Tained Model ของ Microsoft ที่ชื่อ microsoft/swin-base-patch4-window7-224-in22k
เป็นโมเดลที่จัดทำขึ้นในรอบชิงชนะเลิศ ของทีม "ไม่มีตังค์" ในรายการการแข่งขัน E-SAN Thailand Coding & AI Academy ในสาย Personal AI
คณะผู้จัดทำ :
- นายสันติภาพ ทองจันทร์
- นายสิทธัตกะ จรัสแสง
- นางสาวธนัชชา บุตรสิมา
|
[
"normal",
"stage1",
"stage2",
"stage3"
] |
Santipab/Esan-code-Maimeetrang-model-drawing
|
โมเดลนี้ได้ใช้เทคนิค Tramsfer Learning กับ Pre-Tained Model ของ Microsoft ที่ชื่อ microsoft/swin-base-patch4-window7-224-in22k
เป็นโมเดลที่จัดทำขึ้นในรอบชิงชนะเลิศ ของทีม "ไม่มีตังค์" ในรายการการแข่งขัน E-SAN Thailand Coding & AI Academy ในสาย Personal AI
คณะผู้จัดทำ :
- นายสันติภาพ ทองจันทร์
- นายสิทธัตกะ จรัสแสง
- นางสาวธนัชชา บุตรสิมา
|
[
"healthy",
"parkinson"
] |
vuihocrnd/teacher-status-van-tiny-256-v3.1
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"absent",
"bend down",
"horizontal rotation",
"normal"
] |
Janjuaimi/vit-base-patch16-224-in21k-lung_and_colon
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Janjuaimi/vit-base-patch16-224-in21k-lung_and_colon
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0088
- Train Accuracy: 1.0
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.0099
- Validation Accuracy: 0.9996
- Validation Top-3-accuracy: 1.0
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1995, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.1546 | 0.9705 | 1.0 | 0.0375 | 0.9987 | 1.0 | 0 |
| 0.0296 | 0.9973 | 1.0 | 0.0193 | 0.9991 | 1.0 | 1 |
| 0.0140 | 0.9994 | 1.0 | 0.0120 | 0.9996 | 1.0 | 2 |
| 0.0088 | 1.0 | 1.0 | 0.0099 | 0.9996 | 1.0 | 3 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.6.0
- Datasets 2.13.2
- Tokenizers 0.13.3
|
[
"lung_aca",
"lung_n",
"lung_scc"
] |
yotasr/Smart_Tour_GizaVersion1.01
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Smart_Tour_GizaVersion1.01
This model is a fine-tuned version of [yotasr/Smart_TourGuide](https://huggingface.co/yotasr/Smart_TourGuide) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0491
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 4.4777 | 0.0 |
| No log | 2.0 | 2 | 0.0777 | 1.0 |
| No log | 3.0 | 3 | 0.0510 | 1.0 |
| No log | 4.0 | 4 | 0.0491 | 1.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"dahshur area",
"area of sqara",
"hundred hostage village",
"oasis area",
"pyramids of giza & abu al hul"
] |
platzi/platzi-vit-model-jcms-bits
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-jcms-bits
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0357
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.135 | 3.85 | 500 | 0.0357 | 0.9925 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
sergeipetrov/convnextv2-base-22k-384-finetuned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnextv2-base-22k-384-finetuned
This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3257
- Accuracy: 0.9611
- F1: 0.9510
- Precision: 0.9714
- Recall: 0.9315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00015
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 2 | 0.5986 | 0.8167 | 0.8092 | 0.7 | 0.9589 |
| No log | 2.0 | 4 | 0.3945 | 0.9611 | 0.9510 | 0.9714 | 0.9315 |
| No log | 3.0 | 6 | 0.3257 | 0.9611 | 0.9510 | 0.9714 | 0.9315 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17"
] |
TTNVXX/CartoonOrNot
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.039548490196466446
f1: 0.9896907216494846
precision: 1.0
recall: 0.9795918367346939
auc: 0.9991376832423111
accuracy: 0.9916666666666667
|
[
"cartoon",
"not_cartoon"
] |
mejdik/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6104
- Accuracy: 0.888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7402 | 0.99 | 62 | 2.5338 | 0.83 |
| 1.8551 | 2.0 | 125 | 1.7845 | 0.871 |
| 1.5841 | 2.98 | 186 | 1.6104 | 0.888 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
platzi/platzi-vit_model-christian-conchari
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit_model-christian-conchari
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1459 | 3.85 | 500 | 0.0108 | 1.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Nubletz/msi-dinat-mini
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"0",
"1"
] |
Nubletz/dinat-mini-in1k-224
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
lixugang/lixg_food_model001
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lixg_food_model001
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 77893286362087424.0000
- Accuracy: 0.6672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:----------------------:|:-----:|:----:|:----------------------:|:--------:|
| 81023272984825040.0000 | 1.0 | 87 | 77893286362087424.0000 | 0.6010 |
| 68230118470215272.0000 | 2.0 | 174 | 77893286362087424.0000 | 0.6171 |
| 66808662965878784.0000 | 3.0 | 261 | 77893286362087424.0000 | 0.6672 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"乱堆物堆料",
"店外经营",
"非机动车乱停放",
"擅自饲养家禽家畜",
"施工占道(施工维护脏乱差)",
"施工废弃料",
"无照经营游商",
"暴露垃圾",
"机动车乱停放",
"沿街晾挂",
"违法占道广告牌"
] |
ruanwz/autotrain-prj-image-classification-for-slides
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.26321205496788025
f1: 0.8333333333333334
precision: 0.8333333333333334
recall: 0.8333333333333334
auc: 0.9351851851851851
accuracy: 0.9166666666666666
|
[
"keep",
"remove"
] |
TTNVXX/CartoonOrNot2H5
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.025116078555583954
f1: 0.98989898989899
precision: 0.98
recall: 1.0
auc: 1.0
accuracy: 0.9916666666666667
|
[
"cartoon",
"not_cartoon"
] |
ruanwz/autotrain-image-classification-for-slides-240131
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.16453494131565094
f1: 0.8
precision: 1.0
recall: 0.6666666666666666
auc: 0.9916666666666667
accuracy: 0.9230769230769231
|
[
"keep",
"remove"
] |
AndreyKor/test_trainer
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8643
- Accuracy: 0.915
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 3.6903 | 0.517 |
| No log | 2.0 | 250 | 2.7990 | 0.553 |
| No log | 3.0 | 375 | 2.3198 | 0.57 |
| 3.1391 | 4.0 | 500 | 2.0210 | 0.632 |
| 3.1391 | 5.0 | 625 | 1.8298 | 0.638 |
| 3.1391 | 6.0 | 750 | 1.6753 | 0.683 |
| 3.1391 | 7.0 | 875 | 1.5446 | 0.708 |
| 1.7309 | 8.0 | 1000 | 1.4338 | 0.751 |
| 1.7309 | 9.0 | 1125 | 1.3318 | 0.777 |
| 1.7309 | 10.0 | 1250 | 1.2387 | 0.807 |
| 1.7309 | 11.0 | 1375 | 1.1828 | 0.806 |
| 1.2855 | 12.0 | 1500 | 1.1052 | 0.843 |
| 1.2855 | 13.0 | 1625 | 1.0620 | 0.862 |
| 1.2855 | 14.0 | 1750 | 1.0029 | 0.87 |
| 1.2855 | 15.0 | 1875 | 0.9611 | 0.895 |
| 1.0212 | 16.0 | 2000 | 0.9314 | 0.905 |
| 1.0212 | 17.0 | 2125 | 0.9041 | 0.905 |
| 1.0212 | 18.0 | 2250 | 0.8840 | 0.913 |
| 1.0212 | 19.0 | 2375 | 0.8730 | 0.921 |
| 0.8953 | 20.0 | 2500 | 0.8639 | 0.92 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"abis_beauty",
"abis_book",
"accessory_or_part_or_supply",
"chair",
"changing_pad_cover",
"charging_adapter",
"charm",
"childrens_costume",
"chocolate_candy",
"cleaning_agent",
"cleaning_brush",
"clock",
"clothes_hanger",
"agricultural_supplies",
"clothes_rack",
"coffee",
"coffee_maker",
"computer",
"computer_add_on",
"computer_component",
"computer_cooling_device",
"computer_drive_or_storage",
"computer_input_device",
"computer_input_device_accessory",
"air_compressor",
"computer_speaker",
"condiment",
"conditioner",
"consumer_electronics",
"cookie",
"cookie_cutter",
"cooking_oven",
"cosmetic_brush",
"cosmetic_case",
"cosmetic_powder",
"air_conditioner",
"countertop_burner",
"countertop_griddle_appliance",
"countertop_oven",
"cracker",
"curtain",
"cutting_board",
"dairy_based_butter",
"dairy_based_cheese",
"dairy_based_cream",
"dairy_based_drink",
"air_fryer",
"dairy_based_ice_cream",
"deep_fryer",
"dehumidifier",
"desk",
"dietary_supplements",
"digital_device_3",
"dinnerware",
"dishware_bowl",
"dishware_place_setting",
"dishware_plate",
"air_mattress",
"dishwasher",
"dishwasher_detergent",
"doorstop",
"dresser",
"drill",
"drill_bits",
"drinking_cup",
"drinking_straw",
"drink_coaster",
"drink_flavored",
"air_pump",
"drying_rack",
"dutch_ovens",
"earmuff",
"earplug",
"earring",
"edible_oil_vegetable",
"educational_supplies",
"electric_fan",
"electric_water_boiler",
"electromechanical_gauge",
"air_purifier",
"electronic_adapter",
"electronic_cable",
"electronic_device_docking_station",
"electronic_switch",
"envelope",
"essential_oil",
"exercise_band",
"exercise_mat",
"eyebrow_color",
"eyelid_color",
"amazon_book_reader_accessory",
"eyewear",
"face_shaping_makeup",
"facial_tissue",
"fashionearring",
"fashionnecklacebraceletanklet",
"fashionother",
"fashionring",
"fastener_drive_bit",
"faucet",
"figurine",
"amazon_tablet_accessory",
"file_folder",
"fineearring",
"finenecklacebraceletanklet",
"fineother",
"finering",
"first_aid_kit",
"fish",
"fishing_equipment",
"fitness_bench",
"flash_drive",
"abis_drugstore",
"animal_collar",
"flash_memory",
"flatware",
"flat_screen_display_mount",
"flat_sheet",
"flavored_drink_concentrate",
"flour",
"food_blender",
"food_dehydrator",
"food_mixer",
"food_preparation_mold",
"animal_litter",
"food_processor",
"food_service_supply",
"food_slicer",
"food_storage_bag",
"fountain",
"freestanding_shelter",
"fruit",
"fruit_snack",
"fuel_pump",
"furniture",
"antenna",
"furniture_cover",
"furniture_liner",
"game_dice",
"garlic_press",
"golf_club",
"golf_club_bag",
"gps_or_navigation_accessory",
"gps_or_navigation_system",
"grocery",
"guitars",
"area_deodorizer",
"hair_brush",
"hair_cleaner_conditioner",
"hair_coloring_agent",
"hair_comb",
"hair_iron",
"hair_removal_agent",
"hair_styling_agent",
"handbag",
"hardware",
"hardware_clamp_vise",
"artificial_tree",
"hardware_handle",
"hardware_hinge",
"hardware_tubing",
"hat",
"headboard",
"headphones",
"health_personal_care",
"herb",
"herbal_supplement",
"home",
"art_and_craft_supply",
"home_bed_and_bath",
"home_furniture_and_decor",
"home_lighting_accessory",
"home_lighting_and_lamps",
"home_mirror",
"home_organizers_and_storage",
"honey",
"humidifier",
"ice_chest",
"ice_cube_tray",
"astringent_substance",
"incense",
"infant_toddler_car_seat",
"inkjet_printer_ink",
"ink_or_toner",
"input_mouse",
"input_pen",
"instrument_parts_and_accessories",
"janitorial_supply",
"jar",
"jerky",
"audio_or_video",
"jewelry",
"jewelry_set",
"jewelry_storage",
"juicer",
"juice_and_juice_drink",
"keyboards",
"kick_scooter",
"kitchen",
"kitchen_knife",
"kitchen_tools",
"auto_accessory",
"knife_block_set",
"label",
"lab_supply",
"ladder",
"lamp",
"landline_phone",
"laundry_appliance",
"laundry_detergent",
"laundry_hamper",
"leavening_agent",
"auto_chemical",
"legume",
"lehenga_choli_set",
"leotard",
"license_plate_attachment",
"light_bulb",
"light_fixture",
"light_source",
"lip_balm",
"lip_color",
"litter_box",
"abis_electronics",
"auto_oil",
"lock",
"luggage",
"major_home_appliances",
"manual_shaving_razor",
"marking_pen",
"mascara",
"massager",
"massage_stick",
"mattress",
"meal_holder",
"auto_part",
"meat",
"mechanical_components",
"mechanical_lighter",
"media_storage",
"medication",
"memory_reader",
"microphone",
"microscopes",
"microwave_oven",
"milk_substitute",
"av_furniture",
"mineral_supplement",
"monitor",
"mouse_pad",
"mouthwash",
"multiport_hub",
"multitool",
"muscle_roller",
"nail_polish",
"necklace",
"necktie",
"av_receiver",
"networking_device",
"networking_router",
"network_interface_controller_adapter",
"non_dairy_cream",
"non_riding_toy_vehicle",
"noodle",
"notebook_computer",
"nutritional_supplement",
"nuts",
"nut_butter",
"baby_bottle",
"office_electronics",
"office_products",
"orthopedic_brace",
"otc_medication",
"ottoman",
"outbuilding",
"outdoor_living",
"outdoor_recreation_product",
"overalls",
"pacifier",
"baby_product",
"packaged_soup_and_stew",
"pantry",
"paper_product",
"paper_towel_holder",
"pastry",
"percussion_instruments",
"personal_care_appliance",
"personal_computer",
"personal_pill_dispenser",
"pet_apparel",
"backpack",
"pet_pest_control",
"pet_supplies",
"pet_toy",
"phone",
"phone_accessory",
"picture_frame",
"pillow",
"pinboard",
"pitcher",
"placemat",
"badge_holder",
"planter",
"pliers",
"plumbing_fixture",
"popcorn",
"portable_audio",
"portable_av_device",
"portable_electronic_device_cover",
"portable_electronic_device_mount",
"portable_electronic_device_stand",
"portable_stove",
"bag",
"portable_tool_box",
"pot_holder",
"poultry",
"powersports_vehicle_part",
"power_bank",
"power_converter",
"power_strip",
"power_supplies_or_protection",
"pressure_cooker",
"pretzel",
"bakeware",
"printer",
"print_copy_paper",
"professional_healthcare",
"protein_drink",
"protein_supplement_powder",
"pump_dispenser",
"punching_bag",
"puzzles",
"radio",
"razor_blade_cartridge",
"abis_home_improvement",
"baking_cup",
"receiver_or_amplifier",
"recreation_ball",
"refrigeration_appliance",
"refrigerator",
"remote_control",
"rice_cookers",
"rice_mix",
"ring",
"roasting_pan",
"robotic_vacuum_cleaner",
"baking_mix",
"room_divider",
"rowing_machine",
"rug",
"rug_pad",
"safe",
"safety_glasses",
"safety_supply",
"salad_dressing",
"sandal",
"sauce",
"baking_pan",
"saute_fry_pan",
"saw_blade",
"scanner",
"scissors",
"screen_protector",
"screwdriver",
"sculpture",
"seals",
"security_camera",
"security_electronics",
"baking_paper",
"self_stick_note",
"shampoo",
"sheet_pan",
"shelf",
"shellfish",
"shipping_box",
"shoes",
"shoe_insert",
"shovel_spade",
"showerhead",
"barbecue_grill",
"skateboard",
"skin_cleaning_agent",
"skin_cleaning_wipe",
"skin_exfoliant",
"skin_foundation_concealer",
"skin_moisturizer",
"skin_treatment_mask",
"sleeping_bag",
"sleep_mask",
"slow_cooker",
"barbell",
"small_home_appliances",
"snack_chip_and_crisp",
"snack_food_bar",
"snack_mix",
"sofa",
"sound_and_recording_equipment",
"sous_vide_machine",
"speakers",
"spirits",
"sporting_goods",
"basket",
"sport_racket",
"sport_table_game",
"stapler",
"stationary_bicycle",
"steering_wheel_cover",
"sticker_decal",
"stool_seating",
"storage_bag",
"storage_binder",
"storage_box",
"bathwater_additive",
"storage_drawer",
"storage_hook",
"storage_rack",
"stringed_instruments",
"string_light",
"stroller",
"sugar",
"sugar_candy",
"sugar_substitute",
"suitcase",
"battery",
"sunglasses",
"sunscreen",
"surveilance_systems",
"swatch",
"sweatband",
"swing",
"system_power_device",
"table",
"tabletop_game",
"tea",
"bean_bag_chair",
"teaching_equipment",
"technical_sport_shoe",
"telescope",
"television",
"tent",
"terminal_block",
"thermometer",
"thermoplastic_filament",
"thermos",
"thickening_agent",
"abis_kitchen",
"beauty",
"timer",
"toaster",
"toilet_paper_holder",
"toilet_seat",
"tools",
"toothbrush",
"toothbrush_holder",
"tooth_cleaning_agent",
"tote_bag",
"towel_holder",
"bed",
"toys_and_games",
"toy_building_block",
"toy_figure",
"toy_slime",
"track_suit",
"transport_rack",
"trash_can",
"two_way_radio",
"umbrella",
"utility_cart_wagon",
"bed_frame",
"utility_knife",
"vacuum_cleaner",
"vacuum_sealer_machine",
"valve",
"vase",
"vegetable",
"vehicle_interior_shade",
"vehicle_light_assembly",
"vehicle_light_bulb",
"vehicle_mirror",
"bench",
"vehicle_safety_camera",
"vehicle_scan_tool",
"vehicle_seat_cover",
"vest",
"video_device",
"video_game_accessories",
"video_projector",
"vine",
"vinegar",
"vitamin",
"binocular",
"vivarium",
"waist_cincher",
"walking_stick",
"wallet",
"wallpaper",
"wall_art",
"washer_dryer_combination",
"waste_bag",
"watch",
"water",
"biss",
"water_flotation_device",
"water_pump",
"water_purification_unit",
"weigh_scale",
"wheel",
"wheel_cutter",
"wildlife_feeder",
"window_shade",
"wine",
"wireless_accessory",
"blanket",
"wireless_locked_phone",
"wound_dressing",
"wrench",
"writing_board",
"writing_instrument",
"writing_paper",
"blank_media",
"blood_pressure_monitor",
"body_deodorant",
"abis_lawn_and_garden",
"body_lubricant",
"body_positioner",
"bookend",
"boot",
"bottle",
"bottle_opener",
"bottle_rack",
"boxing_glove",
"bracelet",
"bread",
"abis_pet_products",
"bread_making_machine",
"breakfast_cereal",
"broom",
"bucket",
"building_material",
"cabinet",
"caddy",
"cake",
"calculator",
"camcorder",
"abis_video_games",
"camera_bags_and_cases",
"camera_flash",
"camera_lens_filters",
"camera_other_accessories",
"camera_support",
"camera_tripod",
"candle",
"candle_holder",
"candy",
"can_opener",
"accessory",
"card_stock",
"cargo_strap",
"carrying_case_or_bag",
"car_audio_or_theater",
"car_electronics",
"casseroles",
"cellular_phone",
"cellular_phone_case",
"ce_accessory",
"ce_carrying_case_or_bag"
] |
cva333/autotrain-uews8-ywq5u
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.7164884805679321
f1: 0.2
precision: 0.3333333333333333
recall: 0.14285714285714285
auc: 0.4365079365079365
accuracy: 0.5
|
[
"ng",
"ok"
] |
cva333/autotrain-bnxpk-7verl
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.6868882179260254
f1: 0.0
precision: 0.0
recall: 0.0
auc: 0.5611111111111111
accuracy: 0.5555555555555556
|
[
"ng",
"ok"
] |
cva333/autotrain-xhv9j-ulprb
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.6843844652175903
f1: 0.0
precision: 0.0
recall: 0.0
auc: 0.55
accuracy: 0.5185185185185185
|
[
"ng",
"ok"
] |
judith0/classification_INE_v3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classification_INE_v2-finetuned-eurosat
This model is a fine-tuned version of [judith0/classification_INE_v2](https://huggingface.co/judith0/classification_INE_v2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0682
- Accuracy: 0.9681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.89 | 6 | 0.0682 | 0.9681 |
| 0.0563 | 1.93 | 13 | 0.0963 | 0.9574 |
| 0.0563 | 2.67 | 18 | 0.0870 | 0.9574 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"other",
"anverso",
"reverso"
] |
minhocas/convnextv2-tiny-1k-224-finetuned-eurosat-albumentations
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnextv2-tiny-1k-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.0531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | nan | 0.0531 |
| 0.0 | 2.0 | 16 | nan | 0.0531 |
| 0.0 | 3.0 | 24 | nan | 0.0531 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"leaf01",
"leaf02",
"leaf03",
"leaf04",
"leaf05",
"leaf06",
"leaf07",
"leaf08",
"leaf09",
"leaf10",
"leaf11",
"leaf12",
"leaf13",
"leaf14",
"leaf15"
] |
judith0/v1-ineClassification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1430
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.89 | 6 | 0.5325 | 0.8936 |
| 0.833 | 1.93 | 13 | 0.1850 | 0.9787 |
| 0.833 | 2.67 | 18 | 0.1430 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"other",
"anverso",
"other",
"reverso"
] |
arpanl/fine-tuned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"boat",
"children",
"water",
"dogs",
"fireman",
"firetruck",
"mountains",
"people",
"river",
"snow",
"stairs"
] |
arpanl/Model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2752
- Accuracy: 0.3333
- F1: 0.1667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.1596 | 50.0 | 50 | 4.2752 | 0.3333 | 0.1667 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"boat",
"children",
"water",
"dogs",
"fireman",
"firetruck",
"mountains",
"people",
"river",
"snow",
"stairs"
] |
arpanl/Model2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model2
This model was trained from scratch on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"boat",
"children",
"dogs",
"fireman",
"firetruck",
"mountains",
"people",
"river",
"snow",
"stairs",
"water"
] |
ares1123/celebrity_classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Celebrity Classifier
## Model description
This model classifies a face to a celebrity. It is trained on [ares1123/celebrity_dataset](https://huggingface.co/datasets/ares1123/celebrity_dataset) dataset and fine-tuned on [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
## Dataset description
[ares1123/celebrity_dataset](https://huggingface.co/datasets/ares1123/celebrity_dataset)
Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.
### How to use
```python
from transformers import pipeline
# Initialize image classification pipeline
pipe = pipeline("image-classification", model="tonyassi/celebrity-classifier")
# Perform classification
result = pipe('image.png')
# Print results
print(result)
```
## Training and evaluation data
It achieves the following results on the evaluation set:
- Loss: 0.9089
- Accuracy: 0.7982
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"aaron eckhart",
"aaron paul",
"adam driver",
"blake lively",
"bob odenkirk",
"bonnie wright",
"boyd holbrook",
"brad pitt",
"bradley cooper",
"brendan fraser",
"brian cox",
"brie larson",
"brittany snow",
"adam lambert",
"bryan cranston",
"bryce dallas howard",
"busy philipps",
"caitriona balfe",
"cameron diaz",
"camila cabello",
"camila mendes",
"cardi b",
"carey mulligan",
"carla gugino",
"adam levine",
"carrie underwood",
"casey affleck",
"cate blanchett",
"catherine keener",
"catherine zeta-jones",
"celine dion",
"chace crawford",
"chadwick boseman",
"channing tatum",
"charlie cox",
"adam sandler",
"charlie day",
"charlie hunnam",
"charlie plummer",
"charlize theron",
"chiara ferragni",
"chiwetel ejiofor",
"chloe bennet",
"chloe grace moretz",
"chloe sevigny",
"chloë grace moretz",
"adam scott",
"chloë sevigny",
"chris cooper",
"chris evans",
"chris hemsworth",
"chris martin",
"chris messina",
"chris noth",
"chris o'dowd",
"chris pine",
"chris pratt",
"adele",
"chris tucker",
"chrissy teigen",
"christian bale",
"christian slater",
"christina aguilera",
"christina applegate",
"christina hendricks",
"christina milian",
"christina ricci",
"christine baranski",
"adrian grenier",
"christoph waltz",
"christopher plummer",
"christopher walken",
"cillian murphy",
"claire foy",
"clive owen",
"clive standen",
"cobie smulders",
"colin farrell",
"colin firth",
"adèle exarchopoulos",
"colin hanks",
"connie britton",
"conor mcgregor",
"constance wu",
"constance zimmer",
"courteney cox",
"cristiano ronaldo",
"daisy ridley",
"dak prescott",
"dakota fanning",
"aidan gillen",
"dakota johnson",
"damian lewis",
"dan stevens",
"danai gurira",
"dane dehaan",
"daniel craig",
"daniel dae kim",
"daniel day-lewis",
"daniel gillies",
"daniel kaluuya",
"aidan turner",
"daniel mays",
"daniel radcliffe",
"danny devito",
"darren criss",
"dave bautista",
"dave franco",
"dave grohl",
"daveed diggs",
"david attenborough",
"david beckham",
"aaron rodgers",
"aishwarya rai",
"david duchovny",
"david harbour",
"david oyelowo",
"david schwimmer",
"david tennant",
"david thewlis",
"dax shepard",
"debra messing",
"demi lovato",
"dennis quaid",
"aja naomi king",
"denzel washington",
"dermot mulroney",
"dev patel",
"diane keaton",
"diane kruger",
"diane lane",
"diego boneta",
"diego luna",
"djimon hounsou",
"dolly parton",
"alden ehrenreich",
"domhnall gleeson",
"dominic cooper",
"dominic monaghan",
"dominic west",
"don cheadle",
"donald glover",
"donald sutherland",
"donald trump",
"dua lipa",
"dwayne \"the rock\" johnson",
"aldis hodge",
"dwayne johnson",
"dylan o'brien",
"ed harris",
"ed helms",
"ed sheeran",
"eddie murphy",
"eddie redmayne",
"edgar ramirez",
"edward norton",
"eiza gonzalez",
"alec baldwin",
"eiza gonzález",
"elijah wood",
"elisabeth moss",
"elisha cuthbert",
"eliza coupe",
"elizabeth banks",
"elizabeth debicki",
"elizabeth lail",
"elizabeth mcgovern",
"elizabeth moss",
"alex morgan",
"elizabeth olsen",
"elle fanning",
"ellen degeneres",
"ellen page",
"ellen pompeo",
"ellie goulding",
"elon musk",
"emile hirsch",
"emilia clarke",
"emilia fox",
"alex pettyfer",
"emily beecham",
"emily blunt",
"emily browning",
"emily deschanel",
"emily hampshire",
"emily mortimer",
"emily ratajkowski",
"emily vancamp",
"emily watson",
"emma bunton",
"alex rodriguez",
"emma chamberlain",
"emma corrin",
"emma mackey",
"emma roberts",
"emma stone",
"emma thompson",
"emma watson",
"emmanuelle chriqui",
"emmy rossum",
"eoin macken",
"alexander skarsgård",
"eric bana",
"ethan hawke",
"eva green",
"eva longoria",
"eva mendes",
"evan peters",
"evan rachel wood",
"evangeline lilly",
"ewan mcgregor",
"ezra miller",
"alexandra daddario",
"felicity huffman",
"felicity jones",
"finn wolfhard",
"florence pugh",
"florence welch",
"forest whitaker",
"freddie highmore",
"freddie prinze jr.",
"freema agyeman",
"freida pinto",
"aaron taylor-johnson",
"alfre woodard",
"freya allan",
"gabrielle union",
"gael garcia bernal",
"gael garcía bernal",
"gal gadot",
"garrett hedlund",
"gary oldman",
"gemma arterton",
"gemma chan",
"gemma whelan",
"alia shawkat",
"george clooney",
"george lucas",
"gerard butler",
"giancarlo esposito",
"giannis antetokounmpo",
"gigi hadid",
"gillian anderson",
"gillian jacobs",
"gina carano",
"gina gershon",
"alice braga",
"gina rodriguez",
"ginnifer goodwin",
"gisele bundchen",
"glenn close",
"grace kelly",
"greg kinnear",
"greta gerwig",
"greta scacchi",
"greta thunberg",
"gugu mbatha-raw",
"alice eve",
"guy ritchie",
"gwen stefani",
"gwendoline christie",
"gwyneth paltrow",
"hafthor bjornsson",
"hailee steinfeld",
"hailey bieber",
"haley joel osment",
"halle berry",
"hannah simone",
"alicia keys",
"harrison ford",
"harry styles",
"harvey weinstein",
"hayden panettiere",
"hayley atwell",
"helen hunt",
"helen mirren",
"helena bonham carter",
"henry cavill",
"henry golding",
"alicia vikander",
"hilary swank",
"himesh patel",
"hozier",
"hugh bonneville",
"hugh dancy",
"hugh grant",
"hugh jackman",
"hugh laurie",
"ian somerhalder",
"idris elba",
"alison brie",
"imelda staunton",
"imogen poots",
"ioan gruffudd",
"isabella rossellini",
"isabelle huppert",
"isla fisher",
"issa rae",
"iwan rheon",
"j.k. rowling",
"j.k. simmons",
"allison janney",
"jack black",
"jack reynor",
"jack whitehall",
"jackie chan",
"jada pinkett smith",
"jaden smith",
"jaimie alexander",
"jake gyllenhaal",
"jake johnson",
"jake t. austin",
"allison williams",
"james cameron",
"james corden",
"james franco",
"james marsden",
"james mcavoy",
"james norton",
"jamie bell",
"jamie chung",
"jamie dornan",
"jamie foxx",
"alyson hannigan",
"jamie lee curtis",
"jamie oliver",
"jane fonda",
"jane krakowski",
"jane levy",
"jane lynch",
"jane seymour",
"janelle monáe",
"january jones",
"jared leto",
"abbi jacobson",
"amanda peet",
"jason bateman",
"jason clarke",
"jason derulo",
"jason isaacs",
"jason momoa",
"jason mraz",
"jason schwartzman",
"jason segel",
"jason statham",
"jason sudeikis",
"amanda seyfried",
"javier bardem",
"jay baruchel",
"jay-z",
"jeff bezos",
"jeff bridges",
"jeff daniels",
"jeff goldblum",
"jeffrey dean morgan",
"jeffrey donovan",
"jeffrey wright",
"amandla stenberg",
"jemima kirke",
"jenna coleman",
"jenna fischer",
"jenna ortega",
"jennifer aniston",
"jennifer connelly",
"jennifer coolidge",
"jennifer esposito",
"jennifer garner",
"jennifer hudson",
"amber heard",
"jennifer lawrence",
"jennifer lopez",
"jennifer love hewitt",
"jenny slate",
"jeremy irons",
"jeremy renner",
"jeremy strong",
"jerry seinfeld",
"jesse eisenberg",
"jesse metcalfe",
"america ferrera",
"jesse plemons",
"jesse tyler ferguson",
"jesse williams",
"jessica alba",
"jessica biel",
"jessica chastain",
"jessica lange",
"jessie buckley",
"jim carrey",
"jim parsons",
"amy adams",
"joan collins",
"joan cusack",
"joanne froggatt",
"joaquin phoenix",
"jodie comer",
"jodie foster",
"joe jonas",
"joe keery",
"joel edgerton",
"joel kinnaman",
"amy poehler",
"joel mchale",
"john boyega",
"john c. reilly",
"john cena",
"john cho",
"john cleese",
"john corbett",
"john david washington",
"john goodman",
"john hawkes",
"amy schumer",
"john krasinski",
"john legend",
"john leguizamo",
"john lithgow",
"john malkovich",
"john mayer",
"john mulaney",
"john oliver",
"john slattery",
"john travolta",
"ana de armas",
"john turturro",
"johnny depp",
"johnny knoxville",
"jon bernthal",
"jon favreau",
"jon hamm",
"jonah hill",
"jonathan groff",
"jonathan majors",
"jonathan pryce",
"andie macdowell",
"jonathan rhys meyers",
"jordan peele",
"jordana brewster",
"joseph fiennes",
"joseph gordon-levitt",
"josh allen",
"josh brolin",
"josh gad",
"josh hartnett",
"josh hutcherson",
"abhishek bachchan",
"andrew garfield",
"josh radnor",
"jude law",
"judy dench",
"judy greer",
"julia garner",
"julia louis-dreyfus",
"julia roberts",
"julia stiles",
"julian casablancas",
"julian mcmahon",
"andrew lincoln",
"julianna margulies",
"julianne hough",
"julianne moore",
"julianne nicholson",
"juliette binoche",
"juliette lewis",
"juno temple",
"jurnee smollett",
"justin bartha",
"justin bieber",
"andrew scott",
"justin hartley",
"justin herbert",
"justin long",
"justin theroux",
"justin timberlake",
"kj apa",
"kaitlyn dever",
"kaley cuoco",
"kanye west",
"karl urban",
"andy garcia",
"kat dennings",
"kate beckinsale",
"kate bosworth",
"kate hudson",
"kate mara",
"kate middleton",
"kate upton",
"kate walsh",
"kate winslet",
"katee sackhoff",
"andy samberg",
"katherine heigl",
"katherine langford",
"katherine waterston",
"kathryn hahn",
"katie holmes",
"katie mcgrath",
"katy perry",
"kaya scodelario",
"keanu reeves",
"keegan-michael key",
"andy serkis",
"keira knightley",
"keke palmer",
"kelly clarkson",
"kelly macdonald",
"kelly marie tran",
"kelly reilly",
"kelly ripa",
"kelvin harrison jr.",
"keri russell",
"kerry washington",
"angela bassett",
"kevin bacon",
"kevin costner",
"kevin hart",
"kevin spacey",
"ki hong lee",
"kiefer sutherland",
"kieran culkin",
"kiernan shipka",
"kim dickens",
"kim kardashian",
"angelina jolie",
"kirsten dunst",
"kit harington",
"kourtney kardashian",
"kristen bell",
"kristen stewart",
"kristen wiig",
"kristin davis",
"krysten ritter",
"kyle chandler",
"kylie jenner",
"anna camp",
"kylie minogue",
"lady gaga",
"lake bell",
"lakeith stanfield",
"lamar jackson",
"lana del rey",
"laura dern",
"laura harrier",
"laura linney",
"laura prepon",
"anna faris",
"laurence fishburne",
"laverne cox",
"lebron james",
"lea michele",
"lea seydoux",
"lee pace",
"leighton meester",
"lena headey",
"leonardo da vinci",
"leonardo dicaprio",
"abigail breslin",
"anna kendrick",
"leslie mann",
"leslie odom jr.",
"lewis hamilton",
"liam hemsworth",
"liam neeson",
"lili reinhart",
"lily aldridge",
"lily allen",
"lily collins",
"lily james",
"anna paquin",
"lily rabe",
"lily tomlin",
"lin-manuel miranda",
"linda cardellini",
"lionel messi",
"lisa bonet",
"lisa kudrow",
"liv tyler",
"lizzo",
"logan lerman",
"annasophia robb",
"lorde",
"lucy boynton",
"lucy hale",
"lucy lawless",
"lucy liu",
"luke evans",
"luke perry",
"luke wilson",
"lupita nyong'o",
"léa seydoux",
"annabelle wallis",
"mackenzie davis",
"madelaine petsch",
"mads mikkelsen",
"mae whitman",
"maggie gyllenhaal",
"maggie q",
"maggie siff",
"maggie smith",
"mahershala ali",
"mahira khan",
"anne hathaway",
"maisie richardson-sellers",
"maisie williams",
"mandy moore",
"mandy patinkin",
"marc anthony",
"margaret qualley",
"margot robbie",
"maria sharapova",
"marion cotillard",
"marisa tomei",
"anne marie",
"mariska hargitay",
"mark hamill",
"mark ruffalo",
"mark strong",
"mark wahlberg",
"mark zuckerberg",
"marlon brando",
"martin freeman",
"martin scorsese",
"mary elizabeth winstead",
"anne-marie",
"mary j. blige",
"mary steenburgen",
"mary-louise parker",
"matt bomer",
"matt damon",
"matt leblanc",
"matt smith",
"matthew fox",
"matthew goode",
"matthew macfadyen",
"ansel elgort",
"matthew mcconaughey",
"matthew perry",
"matthew rhys",
"matthew stafford",
"max minghella",
"maya angelou",
"maya hawke",
"maya rudolph",
"megan fox",
"megan rapinoe",
"anson mount",
"meghan markle",
"mel gibson",
"melanie lynskey",
"melissa benoist",
"melissa mccarthy",
"melonie diaz",
"meryl streep",
"mia wasikowska",
"michael b. jordan",
"michael c. hall",
"anthony hopkins",
"michael caine",
"michael cera",
"michael cudlitz",
"michael douglas",
"michael ealy",
"michael fassbender",
"michael jordan",
"michael keaton",
"michael pena",
"michael peña",
"abigail spencer",
"anthony joshua",
"michael phelps",
"michael shannon",
"michael sheen",
"michael stuhlbarg",
"michelle dockery",
"michelle monaghan",
"michelle obama",
"michelle pfeiffer",
"michelle rodriguez",
"michelle williams",
"anthony mackie",
"michelle yeoh",
"michiel huisman",
"mila kunis",
"miles teller",
"milla jovovich",
"millie bobby brown",
"milo ventimiglia",
"mindy kaling",
"miranda cosgrove",
"miranda kerr",
"antonio banderas",
"mireille enos",
"molly ringwald",
"morgan freeman",
"mélanie laurent",
"naomi campbell",
"naomi harris",
"naomi scott",
"naomi watts",
"naomie harris",
"nas",
"anya taylor-joy",
"natalie dormer",
"natalie imbruglia",
"natalie morales",
"natalie portman",
"nathalie emmanuel",
"nathalie portman",
"nathan fillion",
"naya rivera",
"neil patrick harris",
"neil degrasse tyson",
"ariana grande",
"neve campbell",
"neymar jr.",
"nicholas braun",
"nicholas hoult",
"nick jonas",
"nick kroll",
"nick offerman",
"nick robinson",
"nicole kidman",
"nikolaj coster-waldau",
"armie hammer",
"nina dobrev",
"noah centineo",
"noomi rapace",
"norman reedus",
"novak djokovic",
"octavia spencer",
"odessa young",
"odette annable",
"olivia colman",
"olivia cooke",
"ashley judd",
"olivia holt",
"olivia munn",
"olivia wilde",
"oprah winfrey",
"orlando bloom",
"oscar isaac",
"owen wilson",
"pablo picasso",
"patrick dempsey",
"patrick mahomes",
"ashton kutcher",
"patrick stewart",
"patrick wilson",
"paul bettany",
"paul dano",
"paul giamatti",
"paul mccartney",
"paul rudd",
"paul wesley",
"paula patton",
"pedro almodóvar",
"aubrey plaza",
"pedro pascal",
"penelope cruz",
"penélope cruz",
"pete davidson",
"peter dinklage",
"phoebe dynevor",
"phoebe waller-bridge",
"pierce brosnan",
"portia de rossi",
"priyanka chopra",
"auli'i cravalho",
"quentin tarantino",
"rachel bilson",
"rachel brosnahan",
"rachel mcadams",
"rachel weisz",
"rafe spall",
"rainn wilson",
"ralph fiennes",
"rami malek",
"rashida jones",
"adam brody",
"awkwafina",
"ray liotta",
"ray romano",
"rebecca ferguson",
"rebecca hall",
"reese witherspoon",
"regina hall",
"regina king",
"renee zellweger",
"renée zellweger",
"rhys ifans",
"barack obama",
"ricardo montalban",
"richard armitage",
"richard gere",
"richard jenkins",
"richard madden",
"ricky gervais",
"ricky martin",
"rihanna",
"riley keough",
"rita ora",
"bella hadid",
"river phoenix",
"riz ahmed",
"rob lowe",
"robert carlyle",
"robert de niro",
"robert downey jr.",
"robert pattinson",
"robert sheehan",
"robin tunney",
"robin williams",
"bella thorne",
"roger federer",
"rooney mara",
"rosamund pike",
"rosario dawson",
"rose byrne",
"rose leslie",
"roselyn sanchez",
"ruby rose",
"rupert grint",
"russell brand",
"ben barnes",
"russell crowe",
"russell wilson",
"ruth bader ginsburg",
"ruth wilson",
"ryan eggold",
"ryan gosling",
"ryan murphy",
"ryan phillippe",
"ryan reynolds",
"ryan seacrest",
"ben mendelsohn",
"salma hayek",
"sam claflin",
"sam heughan",
"sam rockwell",
"sam smith",
"samara weaving",
"samuel l. jackson",
"sandra bullock",
"sandra oh",
"saoirse ronan",
"ben stiller",
"sarah gadon",
"sarah hyland",
"sarah jessica parker",
"sarah michelle gellar",
"sarah paulson",
"sarah silverman",
"sarah wayne callies",
"sasha alexander",
"scarlett johansson",
"scott speedman",
"ben whishaw",
"sean bean",
"sebastian stan",
"selena gomez",
"selma blair",
"serena williams",
"seth macfarlane",
"seth meyers",
"seth rogen",
"shailene woodley",
"shakira",
"benedict cumberbatch",
"shania twain",
"sharlto copley",
"shawn mendes",
"shia labeouf",
"shiri appleby",
"shohreh aghdashloo",
"shonda rhimes",
"sienna miller",
"sigourney weaver",
"simon baker",
"benedict wong",
"simon cowell",
"simon pegg",
"simone biles",
"sofia boutella",
"sofia vergara",
"sophie turner",
"sophie wessex",
"stanley tucci",
"stephen amell",
"stephen colbert",
"adam devine",
"benicio del toro",
"stephen curry",
"stephen dorff",
"sterling k. brown",
"sterling knight",
"steve carell",
"steven yeun",
"susan sarandon",
"taika waititi",
"taraji p. henson",
"taron egerton",
"bill gates",
"taylor hill",
"taylor kitsch",
"taylor lautner",
"taylor schilling",
"taylor swift",
"teresa palmer",
"terrence howard",
"tessa thompson",
"thandie newton",
"the weeknd",
"bill hader",
"theo james",
"thomas brodie-sangster",
"thomas jane",
"tiger woods",
"tilda swinton",
"tim burton",
"tim cook",
"timothee chalamet",
"timothy olyphant",
"timothy spall",
"bill murray",
"timothée chalamet",
"tina fey",
"tobey maguire",
"toby jones",
"toby kebbell",
"toby regbo",
"tom brady",
"tom brokaw",
"tom cavanagh",
"tom cruise",
"bill pullman",
"tom ellis",
"tom felton",
"tom hanks",
"tom hardy",
"tom hiddleston",
"tom holland",
"tom hollander",
"tom hopper",
"tom selleck",
"toni collette",
"bill skarsgård",
"tony hale",
"topher grace",
"tracee ellis ross",
"tyra banks",
"tyrese gibson",
"uma thurman",
"usain bolt",
"uzo aduba",
"vanessa hudgens",
"vanessa kirby",
"billie eilish",
"vera farmiga",
"victoria pedretti",
"viggo mortensen",
"vin diesel",
"vince vaughn",
"vincent cassel",
"vincent d'onofrio",
"vincent kartheiser",
"viola davis",
"walton goggins",
"billie lourd",
"wes anderson",
"wes bentley",
"whoopi goldberg",
"will ferrell",
"will poulter",
"willem dafoe",
"william jackson harper",
"william shatner",
"winona ryder",
"woody harrelson",
"billy crudup",
"yara shahidi",
"yvonne strahovski",
"zac efron",
"zach braff",
"zach galifianakis",
"zachary levi",
"zachary quinto",
"zayn malik",
"zazie beetz",
"zendaya",
"billy porter",
"zoe kazan",
"zoe kravitz",
"zoe saldana",
"zoey deutch",
"zooey deschanel",
"zoë kravitz",
"zoë saldana"
] |
Martin-Michael/gockle_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gockle_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9618
- Accuracy: 0.7844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.7231 | 0.64 | 100 | 2.6467 | 0.2279 |
| 2.3217 | 1.28 | 200 | 2.4386 | 0.2288 |
| 2.0819 | 1.92 | 300 | 2.2887 | 0.2815 |
| 1.9583 | 2.56 | 400 | 2.1686 | 0.4501 |
| 1.8098 | 3.21 | 500 | 2.0731 | 0.5085 |
| 1.7511 | 3.85 | 600 | 1.9978 | 0.5320 |
| 1.6581 | 4.49 | 700 | 1.9233 | 0.5584 |
| 1.6094 | 5.13 | 800 | 1.8703 | 0.5706 |
| 1.5241 | 5.77 | 900 | 1.8192 | 0.6017 |
| 1.501 | 6.41 | 1000 | 1.7757 | 0.6111 |
| 1.4308 | 7.05 | 1100 | 1.7415 | 0.6281 |
| 1.3985 | 7.69 | 1200 | 1.7015 | 0.6375 |
| 1.3559 | 8.33 | 1300 | 1.6652 | 0.6403 |
| 1.3092 | 8.97 | 1400 | 1.6290 | 0.6488 |
| 1.3059 | 9.62 | 1500 | 1.6142 | 0.6620 |
| 1.2597 | 10.26 | 1600 | 1.5771 | 0.6704 |
| 1.2147 | 10.9 | 1700 | 1.5501 | 0.6902 |
| 1.1942 | 11.54 | 1800 | 1.5288 | 0.6911 |
| 1.1668 | 12.18 | 1900 | 1.5081 | 0.6902 |
| 1.1371 | 12.82 | 2000 | 1.4883 | 0.6949 |
| 1.1256 | 13.46 | 2100 | 1.4770 | 0.6930 |
| 1.0922 | 14.1 | 2200 | 1.4500 | 0.7081 |
| 1.0559 | 14.74 | 2300 | 1.4369 | 0.7072 |
| 1.054 | 15.38 | 2400 | 1.4157 | 0.7128 |
| 1.0465 | 16.03 | 2500 | 1.3899 | 0.7279 |
| 0.9965 | 16.67 | 2600 | 1.3734 | 0.7194 |
| 0.9876 | 17.31 | 2700 | 1.3603 | 0.7298 |
| 0.9791 | 17.95 | 2800 | 1.3422 | 0.7298 |
| 0.9551 | 18.59 | 2900 | 1.3309 | 0.7373 |
| 0.9313 | 19.23 | 3000 | 1.3223 | 0.7335 |
| 0.9211 | 19.87 | 3100 | 1.3052 | 0.7345 |
| 0.9071 | 20.51 | 3200 | 1.2897 | 0.7420 |
| 0.875 | 21.15 | 3300 | 1.2762 | 0.7561 |
| 0.8676 | 21.79 | 3400 | 1.2657 | 0.7542 |
| 0.8498 | 22.44 | 3500 | 1.2575 | 0.7580 |
| 0.8529 | 23.08 | 3600 | 1.2435 | 0.7542 |
| 0.8341 | 23.72 | 3700 | 1.2369 | 0.7561 |
| 0.8056 | 24.36 | 3800 | 1.2306 | 0.7533 |
| 0.8038 | 25.0 | 3900 | 1.2181 | 0.7665 |
| 0.7733 | 25.64 | 4000 | 1.2031 | 0.7655 |
| 0.7834 | 26.28 | 4100 | 1.2015 | 0.7637 |
| 0.7697 | 26.92 | 4200 | 1.1887 | 0.7637 |
| 0.7438 | 27.56 | 4300 | 1.1788 | 0.7674 |
| 0.733 | 28.21 | 4400 | 1.1740 | 0.7637 |
| 0.7244 | 28.85 | 4500 | 1.1671 | 0.7674 |
| 0.7091 | 29.49 | 4600 | 1.1563 | 0.7693 |
| 0.7138 | 30.13 | 4700 | 1.1543 | 0.7665 |
| 0.693 | 30.77 | 4800 | 1.1445 | 0.7665 |
| 0.6837 | 31.41 | 4900 | 1.1348 | 0.7731 |
| 0.6706 | 32.05 | 5000 | 1.1282 | 0.7702 |
| 0.6514 | 32.69 | 5100 | 1.1222 | 0.7712 |
| 0.6513 | 33.33 | 5200 | 1.1323 | 0.7665 |
| 0.6517 | 33.97 | 5300 | 1.1138 | 0.7693 |
| 0.637 | 34.62 | 5400 | 1.1014 | 0.7712 |
| 0.6277 | 35.26 | 5500 | 1.0949 | 0.7759 |
| 0.6103 | 35.9 | 5600 | 1.0882 | 0.7759 |
| 0.5916 | 36.54 | 5700 | 1.0888 | 0.7693 |
| 0.6101 | 37.18 | 5800 | 1.0890 | 0.7721 |
| 0.6042 | 37.82 | 5900 | 1.0779 | 0.7750 |
| 0.5618 | 38.46 | 6000 | 1.0769 | 0.7750 |
| 0.5878 | 39.1 | 6100 | 1.0638 | 0.7787 |
| 0.5522 | 39.74 | 6200 | 1.0611 | 0.7731 |
| 0.557 | 40.38 | 6300 | 1.0639 | 0.7768 |
| 0.5665 | 41.03 | 6400 | 1.0668 | 0.7740 |
| 0.5269 | 41.67 | 6500 | 1.0531 | 0.7759 |
| 0.5672 | 42.31 | 6600 | 1.0493 | 0.7759 |
| 0.5197 | 42.95 | 6700 | 1.0469 | 0.7759 |
| 0.5273 | 43.59 | 6800 | 1.0481 | 0.7740 |
| 0.5149 | 44.23 | 6900 | 1.0434 | 0.7712 |
| 0.5146 | 44.87 | 7000 | 1.0462 | 0.7787 |
| 0.5033 | 45.51 | 7100 | 1.0358 | 0.7759 |
| 0.5073 | 46.15 | 7200 | 1.0322 | 0.7806 |
| 0.4964 | 46.79 | 7300 | 1.0313 | 0.7815 |
| 0.4832 | 47.44 | 7400 | 1.0238 | 0.7797 |
| 0.484 | 48.08 | 7500 | 1.0355 | 0.7768 |
| 0.4856 | 48.72 | 7600 | 1.0263 | 0.7834 |
| 0.4688 | 49.36 | 7700 | 1.0178 | 0.7815 |
| 0.4628 | 50.0 | 7800 | 1.0161 | 0.7787 |
| 0.457 | 50.64 | 7900 | 1.0195 | 0.7768 |
| 0.4547 | 51.28 | 8000 | 1.0064 | 0.7825 |
| 0.4551 | 51.92 | 8100 | 1.0108 | 0.7806 |
| 0.4408 | 52.56 | 8200 | 1.0136 | 0.7768 |
| 0.4471 | 53.21 | 8300 | 1.0016 | 0.7834 |
| 0.4431 | 53.85 | 8400 | 1.0038 | 0.7863 |
| 0.4393 | 54.49 | 8500 | 1.0057 | 0.7815 |
| 0.4246 | 55.13 | 8600 | 0.9961 | 0.7797 |
| 0.4237 | 55.77 | 8700 | 1.0019 | 0.7806 |
| 0.4128 | 56.41 | 8800 | 0.9941 | 0.7806 |
| 0.4285 | 57.05 | 8900 | 0.9946 | 0.7815 |
| 0.4121 | 57.69 | 9000 | 0.9932 | 0.7806 |
| 0.4167 | 58.33 | 9100 | 0.9916 | 0.7825 |
| 0.4001 | 58.97 | 9200 | 0.9915 | 0.7825 |
| 0.4053 | 59.62 | 9300 | 0.9886 | 0.7815 |
| 0.3993 | 60.26 | 9400 | 0.9910 | 0.7844 |
| 0.3881 | 60.9 | 9500 | 0.9856 | 0.7863 |
| 0.3846 | 61.54 | 9600 | 0.9917 | 0.7806 |
| 0.3913 | 62.18 | 9700 | 0.9820 | 0.7834 |
| 0.3897 | 62.82 | 9800 | 0.9806 | 0.7844 |
| 0.3821 | 63.46 | 9900 | 0.9804 | 0.7825 |
| 0.3742 | 64.1 | 10000 | 0.9873 | 0.7844 |
| 0.3835 | 64.74 | 10100 | 0.9807 | 0.7834 |
| 0.3571 | 65.38 | 10200 | 0.9792 | 0.7844 |
| 0.38 | 66.03 | 10300 | 0.9786 | 0.7844 |
| 0.3612 | 66.67 | 10400 | 0.9769 | 0.7844 |
| 0.3628 | 67.31 | 10500 | 0.9991 | 0.7740 |
| 0.3655 | 67.95 | 10600 | 0.9737 | 0.7806 |
| 0.3489 | 68.59 | 10700 | 0.9745 | 0.7853 |
| 0.371 | 69.23 | 10800 | 0.9853 | 0.7787 |
| 0.3454 | 69.87 | 10900 | 0.9676 | 0.7825 |
| 0.3457 | 70.51 | 11000 | 0.9708 | 0.7853 |
| 0.3559 | 71.15 | 11100 | 0.9691 | 0.7863 |
| 0.3523 | 71.79 | 11200 | 0.9690 | 0.7872 |
| 0.3357 | 72.44 | 11300 | 0.9707 | 0.7815 |
| 0.344 | 73.08 | 11400 | 0.9690 | 0.7863 |
| 0.3527 | 73.72 | 11500 | 0.9788 | 0.7825 |
| 0.327 | 74.36 | 11600 | 0.9703 | 0.7825 |
| 0.3376 | 75.0 | 11700 | 0.9770 | 0.7787 |
| 0.3518 | 75.64 | 11800 | 0.9718 | 0.7834 |
| 0.3031 | 76.28 | 11900 | 0.9736 | 0.7863 |
| 0.3404 | 76.92 | 12000 | 0.9661 | 0.7825 |
| 0.3243 | 77.56 | 12100 | 0.9731 | 0.7853 |
| 0.3381 | 78.21 | 12200 | 0.9685 | 0.7900 |
| 0.3258 | 78.85 | 12300 | 0.9691 | 0.7844 |
| 0.3149 | 79.49 | 12400 | 0.9615 | 0.7844 |
| 0.3234 | 80.13 | 12500 | 0.9661 | 0.7853 |
| 0.3296 | 80.77 | 12600 | 0.9722 | 0.7815 |
| 0.3215 | 81.41 | 12700 | 0.9672 | 0.7834 |
| 0.3121 | 82.05 | 12800 | 0.9641 | 0.7834 |
| 0.3163 | 82.69 | 12900 | 0.9636 | 0.7834 |
| 0.3225 | 83.33 | 13000 | 0.9649 | 0.7853 |
| 0.3136 | 83.97 | 13100 | 0.9652 | 0.7825 |
| 0.3172 | 84.62 | 13200 | 0.9639 | 0.7853 |
| 0.3098 | 85.26 | 13300 | 0.9671 | 0.7834 |
| 0.3081 | 85.9 | 13400 | 0.9627 | 0.7806 |
| 0.3099 | 86.54 | 13500 | 0.9626 | 0.7815 |
| 0.3144 | 87.18 | 13600 | 0.9612 | 0.7815 |
| 0.2952 | 87.82 | 13700 | 0.9645 | 0.7863 |
| 0.3092 | 88.46 | 13800 | 0.9604 | 0.7853 |
| 0.3193 | 89.1 | 13900 | 0.9630 | 0.7844 |
| 0.3005 | 89.74 | 14000 | 0.9667 | 0.7815 |
| 0.2928 | 90.38 | 14100 | 0.9638 | 0.7844 |
| 0.315 | 91.03 | 14200 | 0.9644 | 0.7844 |
| 0.3095 | 91.67 | 14300 | 0.9637 | 0.7834 |
| 0.3036 | 92.31 | 14400 | 0.9615 | 0.7834 |
| 0.298 | 92.95 | 14500 | 0.9617 | 0.7844 |
| 0.2944 | 93.59 | 14600 | 0.9658 | 0.7834 |
| 0.3065 | 94.23 | 14700 | 0.9625 | 0.7834 |
| 0.2983 | 94.87 | 14800 | 0.9622 | 0.7844 |
| 0.2953 | 95.51 | 14900 | 0.9626 | 0.7834 |
| 0.3063 | 96.15 | 15000 | 0.9608 | 0.7853 |
| 0.3058 | 96.79 | 15100 | 0.9631 | 0.7853 |
| 0.2974 | 97.44 | 15200 | 0.9614 | 0.7844 |
| 0.3004 | 98.08 | 15300 | 0.9608 | 0.7844 |
| 0.3001 | 98.72 | 15400 | 0.9613 | 0.7853 |
| 0.2968 | 99.36 | 15500 | 0.9623 | 0.7853 |
| 0.2985 | 100.0 | 15600 | 0.9618 | 0.7844 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18"
] |
TirathP/finetuned_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 14
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.13.3
|
[
"aeroplane",
"boat",
"stairs",
"tree",
"water",
"children",
"dogs",
"fireman",
"firetruck",
"mountains",
"people",
"river",
"snow"
] |
Martin-Michael/gockle_v2_10epochs
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gockle_v2_10epochs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9142
- Accuracy: 0.5612
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6464 | 6.41 | 1000 | 1.9142 | 0.5612 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18"
] |
ChrisGuarino/model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.4.0
- Tokenizers 0.21.0
|
[
"cat_00",
"cat_01",
"cat_02",
"cat_03",
"cat_04",
"cat_05",
"cat_06"
] |
vuihocrnd/teacher-status-van-tiny-256-v3.2
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"absent",
"bend down",
"horizontal rotation",
"normal"
] |
JohnJumon/emotion_recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1376
- Accuracy: 0.6062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 1.3456 | 0.4813 |
| No log | 2.0 | 40 | 1.3147 | 0.5188 |
| No log | 3.0 | 60 | 1.2345 | 0.5563 |
| No log | 4.0 | 80 | 1.2281 | 0.5625 |
| No log | 5.0 | 100 | 1.1851 | 0.5687 |
| No log | 6.0 | 120 | 1.1911 | 0.5563 |
| No log | 7.0 | 140 | 1.1834 | 0.5813 |
| No log | 8.0 | 160 | 1.1682 | 0.5875 |
| No log | 9.0 | 180 | 1.2359 | 0.55 |
| No log | 10.0 | 200 | 1.1850 | 0.5563 |
| No log | 11.0 | 220 | 1.1877 | 0.5687 |
| No log | 12.0 | 240 | 1.1546 | 0.5687 |
| No log | 13.0 | 260 | 1.1694 | 0.5813 |
| No log | 14.0 | 280 | 1.2401 | 0.5875 |
| No log | 15.0 | 300 | 1.1899 | 0.575 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
p1atdev/siglip-tagger-test-2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# siglip-tagger-test-2
This model is a fine-tuned version of [google/siglip-base-patch16-512](https://huggingface.co/google/siglip-base-patch16-512) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 364.7850
- Accuracy: 0.2539
- F1: 0.9967
## Model description
This model is an experimental model that predicts danbooru tags of images.
## Example
```py
from PIL import Image
import torch
from transformers import (
AutoModelForImageClassification,
AutoImageProcessor,
)
import numpy as np
MODEL_NAME = "p1atdev/siglip-tagger-test-2"
model = AutoModelForImageClassification.from_pretrained(
MODEL_NAME, torch_dtype=torch.bfloat16, trust_remote_code=True
)
model.eval()
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
image = Image.open("sample.jpg") # load your image
inputs = processor(image, return_tensors="pt").to(model.device, model.dtype)
logits = model(**inputs).logits.detach().cpu().float()[0]
logits = np.clip(logits, 0.0, 1.0)
results = {
model.config.id2label[i]: logit for i, logit in enumerate(logits) if logit > 0
}
results = sorted(results.items(), key=lambda x: x[1], reverse=True)
for tag, score in results:
print(f"{tag}: {score*100:.2f}%")
# 1girl: 100.00%
# outdoors: 100.00%
# sky: 100.00%
# solo: 100.00%
# school uniform: 96.88%
# skirt: 92.97%
# day: 89.06%
# ...
```
## Intended uses & limitations
This model is for research use only and is not recommended for production.
Please use wd-v1-4-tagger series by SmilingWolf:
- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2)
etc.
## Training and evaluation data
High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1496.9876 | 1.0 | 141 | 691.3267 | 0.1242 | 0.9957 |
| 860.0218 | 2.0 | 282 | 433.5286 | 0.1626 | 0.9965 |
| 775.4277 | 3.0 | 423 | 409.0374 | 0.1827 | 0.9966 |
| 697.2465 | 4.0 | 564 | 396.5604 | 0.2025 | 0.9966 |
| 582.6023 | 5.0 | 705 | 388.3294 | 0.2065 | 0.9966 |
| 617.5087 | 6.0 | 846 | 382.2605 | 0.2213 | 0.9966 |
| 627.533 | 7.0 | 987 | 377.6726 | 0.2269 | 0.9967 |
| 595.4033 | 8.0 | 1128 | 374.3268 | 0.2327 | 0.9967 |
| 593.3854 | 9.0 | 1269 | 371.4181 | 0.2409 | 0.9967 |
| 537.9777 | 10.0 | 1410 | 369.5010 | 0.2421 | 0.9967 |
| 552.3083 | 11.0 | 1551 | 368.0743 | 0.2468 | 0.9967 |
| 570.5438 | 12.0 | 1692 | 366.8302 | 0.2498 | 0.9967 |
| 507.5343 | 13.0 | 1833 | 366.1787 | 0.2499 | 0.9967 |
| 515.5528 | 14.0 | 1974 | 365.5653 | 0.2525 | 0.9967 |
| 458.5096 | 15.0 | 2115 | 365.1838 | 0.2528 | 0.9967 |
| 515.6953 | 16.0 | 2256 | 364.9844 | 0.2535 | 0.9967 |
| 533.7929 | 17.0 | 2397 | 364.8577 | 0.2538 | 0.9967 |
| 520.3728 | 18.0 | 2538 | 364.8066 | 0.2537 | 0.9967 |
| 525.1097 | 19.0 | 2679 | 364.7850 | 0.2539 | 0.9967 |
| 482.0612 | 20.0 | 2820 | 364.7876 | 0.2539 | 0.9967 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"!",
"!!",
"!?",
"+++",
"+_+",
"...",
"...?",
"0_0",
"0w0",
"101st airborne",
"1945",
"1980s (style)",
"1990s (style)",
"19vodnik",
"1boy",
"1girl",
"1koma",
"1other",
"1st infantry division (us army)",
"2000s (style)",
"2010s (style)",
"2018",
"2019",
"2021",
"2022",
"2023",
"2023 titan submersible incident",
"2boys",
"2girls",
"2koma",
"2others",
"3.7 cm pak 36",
"300 blackout",
"3:",
"3boys",
"3d",
"3d background",
"3d printer",
"3girls",
"3koma",
"3others",
"4boys",
"4girls",
"4koma",
"4others",
"4shi",
"5boys",
"5girls",
"6+boys",
"6+girls",
"6+others",
"666",
"69",
"88 flak",
"9a-91 (girls' frontline) (cosplay)",
":/",
":3",
":<",
":>",
":>=",
":d",
":i",
":o",
":p",
":q",
":s",
":t",
":|",
";)",
";d",
";i",
";o",
";p",
"<o>_<o>",
"<|>_<|>",
"=_=",
">:(",
">:)",
">_<",
">_o",
">o<",
"?",
"??",
"@_@",
"\\m/",
"\\||/",
"^^^",
"^_^",
"^o^",
"a-10 thunderbolt ii",
"a6m zero",
"aa-12",
"abandoned",
"abduction",
"above clouds",
"abs",
"abstract",
"abstract background",
"absurdly long hair",
"accident",
"accidental exposure",
"accordion",
"ace (playing card)",
"ace of spades",
"acoustic guitar",
"acrobatics",
"action figure",
"ad",
"adam's apple",
"adapted costume",
"adapted uniform",
"adidas",
"adjusting another's clothes",
"adjusting apron",
"adjusting clothes",
"adjusting collar",
"adjusting earphones",
"adjusting eyewear",
"adjusting footwear",
"adjusting gloves",
"adjusting hair",
"adjusting headwear",
"adjusting hood",
"adjusting legwear",
"adjusting necktie",
"adjusting panties",
"adjusting scarf",
"adjusting shoe",
"adjusting shorts",
"adjusting strap",
"adjusting swimsuit",
"adobe acrobat",
"adobe after effects",
"adobe bridge",
"adobe photoshop",
"adrian helmet",
"adult baby",
"aegyo sal",
"aerial bomb",
"aerovity",
"afdian username",
"affectionate",
"afloat",
"afro",
"after anal",
"after bathing",
"after battle",
"after cunnilingus",
"after ejaculation",
"after fellatio",
"after handjob",
"after kiss",
"after masturbation",
"after paizuri",
"after rain",
"after sex",
"after vaginal",
"afterimage",
"against bookshelf",
"against door",
"against glass",
"against pillar",
"against railing",
"against tree",
"against wall",
"agave",
"age comparison",
"age conscious",
"age difference",
"age progression",
"aged down",
"aged up",
"agent 47 (cosplay)",
"agm-65 maverick",
"ah eto... bleh (meme)",
"ah-64 apache",
"ahegao",
"ahoge",
"ahoge wag",
"ai ai gasa",
"ai drawing anime characters eating ramen (meme)",
"ai-generated art (topic)",
"aiguillette",
"aim-120 amraam",
"aim-7 sparrow",
"aiming",
"aiming at viewer",
"aimpoint",
"air bubble",
"air conditioner",
"air freshener",
"air pods",
"airborne",
"aircraft",
"aircraft carrier",
"airfield",
"airplane",
"airplane interior",
"airplane wing",
"airport",
"airship",
"airsoft",
"ajirogasa",
"ak 5",
"ak-103",
"ak-105",
"ak-12",
"ak-47",
"ak-74",
"ak-74m",
"akabeko",
"akagi (aircraft carrier)",
"akg",
"akg k-series headphones",
"akira movie poster",
"akm",
"akms",
"aks-74",
"aks-74u",
"al rihla",
"alarm clock",
"alarm siren",
"albino",
"album cover",
"alchemy",
"alcohol",
"alice gear",
"alien",
"all fours",
"alley",
"alligator",
"alphabet",
"alraune",
"altar",
"alternate breast size",
"alternate color",
"alternate costume",
"alternate flag color",
"alternate form",
"alternate hair color",
"alternate hair length",
"alternate hairstyle",
"alternate size",
"alternate skin color",
"altyn helmet",
"amagi shino",
"amazake (drink)",
"ambiguous gender",
"ambiguous red liquid",
"amd ryzen",
"american flag",
"american football (object)",
"ammunition",
"ammunition belt",
"ammunition box",
"ammunition pouch",
"amnesia",
"among us drip (meme)",
"among us eyes (meme)",
"amor",
"amplifier",
"amputee",
"amusement park",
"an-22",
"anachronism",
"anal",
"anal beads",
"anal fingering",
"anal fisting",
"anal fluid",
"anal invitation",
"anal object insertion",
"anal only",
"anal tail",
"analog clock",
"anato finnstark",
"anatomical nonsense",
"anatomy",
"anchor",
"anchor hair ornament",
"anchor symbol",
"ancient egyptian hat",
"androgynous",
"android",
"angel",
"angel and devil",
"angel wings",
"anger vein",
"angle grinder",
"angled foregrip",
"angry",
"anilingus",
"animal",
"animal bag",
"animal collar",
"animal costume",
"animal ear fluff",
"animal ear headphones",
"animal ear headwear",
"animal ear legwear",
"animal ears",
"animal ears helmet",
"animal feet",
"animal focus",
"animal hair ornament",
"animal hands",
"animal hat",
"animal hood",
"animal hug",
"animal nose",
"animal on chest",
"animal on hand",
"animal on head",
"animal on lap",
"animal on shoulder",
"animal penis",
"animal print",
"animal skull",
"animal slippers",
"animal with human ears",
"animal-themed vehicle",
"animalization",
"anime girl hiding from a terminator (meme)",
"animification",
"ankh",
"ankh necklace",
"ankha zone (meme)",
"ankle boots",
"ankle cuffs",
"ankle grab",
"ankle lace-up",
"ankle ribbon",
"ankle socks",
"anklet",
"anniversary",
"annoyed",
"ant",
"ant girl",
"antenna hair",
"antennae",
"anti-aircraft",
"anti-aircraft gun",
"anti-aircraft missile",
"anti-materiel rifle",
"anti-tank grenade",
"anti-tank gun",
"anti-tank mine",
"anti-war",
"antique cannon",
"antique firearm",
"antlers",
"antonov an-225",
"anus",
"anus peek",
"anvil",
"anya's heh face (meme)",
"apartment",
"aphid",
"aphid girl",
"apocalypse",
"apollo lunar module",
"apple",
"apple earrings",
"apple watch",
"applying bandages",
"applying makeup",
"april",
"april fools",
"apron",
"apron aside",
"apron hold",
"apron lift",
"apron tug",
"aqua background",
"aqua bow",
"aqua bowtie",
"aqua bra",
"aqua dress",
"aqua eyes",
"aqua gloves",
"aqua hair",
"aqua jacket",
"aqua nails",
"aqua necktie",
"aqua panties",
"aqua pants",
"aqua pupils",
"aqua ribbon",
"aqua sailor collar",
"aqua shirt",
"aqua shorts",
"aqua skin",
"aqua skirt",
"aqua theme",
"aquarium",
"aquarius (constellation)",
"aquarius (symbol)",
"aquarius (zodiac)",
"aqueduct",
"ar-15",
"arabic text",
"arachne",
"aran sweater",
"arc de triomphe",
"arcade",
"arcade cabinet",
"arch",
"arched back",
"archery",
"architecture",
"are you winning son? (meme)",
"areola slip",
"areolae",
"argentina",
"argentinian flag",
"argyle",
"argyle background",
"argyle legwear",
"argyle pantyhose",
"argyle sweater",
"arm armor",
"arm around shoulder",
"arm around waist",
"arm at side",
"arm behind back",
"arm behind head",
"arm behind leg",
"arm belt",
"arm between breasts",
"arm between legs",
"arm blade",
"arm cannon",
"arm garter",
"arm grab",
"arm guards",
"arm hair",
"arm held back",
"arm hug",
"arm mounted weapon",
"arm on knee",
"arm pillow",
"arm rest",
"arm ribbon",
"arm shield",
"arm strap",
"arm support",
"arm tattoo",
"arm under breasts",
"arm up",
"arm warmers",
"arm wrap",
"armband",
"armchair",
"armillary sphere",
"armlet",
"armor",
"armor under clothes",
"armored bodysuit",
"armored boots",
"armored dress",
"armored gloves",
"armored personnel carrier",
"armored vehicle",
"armpit crease",
"armpit cutout",
"armpit peek",
"armpits",
"arms around neck",
"arms around waist",
"arms at sides",
"arms behind back",
"arms behind head",
"arms between legs",
"arms under breasts",
"arms up",
"army",
"around corner",
"aroused",
"arrest",
"arrow (projectile)",
"arrow (symbol)",
"arrow hair ornament",
"art brush",
"art gallery",
"art shift",
"arthropod",
"arthropod girl",
"arthropod limbs",
"artificial eye",
"artificial vagina",
"artificial vagina with body",
"artillery",
"artillery shell",
"artist logo",
"artist name",
"artist self-insert",
"artist self-reference",
"artistic error",
"artoria pendragon (swimsuit ruler) (fate) (cosplay)",
"artstation username",
"arx-160",
"asa no ha (pattern)",
"asahi breweries",
"ascot",
"ashes",
"ashiyu",
"ashtray",
"asian",
"asmr",
"asphyxiation",
"ass",
"ass focus",
"ass freckles",
"ass grab",
"ass press",
"ass ripple",
"ass support",
"ass tattoo",
"ass visible through thighs",
"ass-to-ass",
"assault rifle",
"assault visor",
"assembling",
"assertive female",
"assisted exposure",
"assisted stretching",
"asteroid",
"asteroid ill",
"astronaut",
"astronomical clock",
"asymmetrical armor",
"asymmetrical arms",
"asymmetrical bangs",
"asymmetrical clothes",
"asymmetrical docking",
"asymmetrical gloves",
"asymmetrical hair",
"asymmetrical irises",
"asymmetrical legwear",
"asymmetrical limbs",
"asymmetrical sleeves",
"at computer",
"at knifepoint",
"athletic leotard",
"attack",
"au ra",
"audience",
"audio jack",
"aunt and nephew",
"aunt and niece",
"aura",
"aurora",
"austria",
"austria-hungary",
"austro-hungarian army",
"autoarousal",
"autocannon",
"autofacial",
"autopenetration",
"autumn",
"autumn leaves",
"averting eyes",
"aviator sunglasses",
"awkward",
"awning",
"axe",
"axolotl",
"azazel (helltaker) (cosplay)",
"azula (cosplay)",
"b-2 spirit",
"bababooey",
"baby",
"baby bottle",
"babydoll",
"babywearing",
"back",
"back bow",
"back cutout",
"back focus",
"back hair",
"back slit",
"back tattoo",
"back-to-back",
"backboob",
"backless dress",
"backless outfit",
"backless panties",
"backlighting",
"backpack",
"backpack basket",
"backwards hat",
"bacon",
"bacteria",
"bad apple!!",
"bad end",
"bad food",
"bad gun anatomy",
"bad hands",
"bad perspective",
"bad proportions",
"bad reflection",
"badge",
"bag",
"bag charm",
"bag of chips",
"bag on lap",
"baggy clothes",
"baggy pants",
"bagpipes",
"bags under eyes",
"bagua",
"baguette",
"bait and switch",
"bakery",
"balaclava",
"balance scale",
"balance scale print",
"balancing",
"balcony",
"bald",
"balding",
"balisong",
"ball",
"ball and chain restraint",
"ball gag",
"ballerina",
"ballet",
"ballet slippers",
"ballistic shield",
"balloon",
"bamboo",
"bamboo broom",
"bamboo forest",
"bamboo screen",
"bamboo steamer",
"banana",
"banana peel",
"band shirt",
"bandage on face",
"bandage over one eye",
"bandaged ankle",
"bandaged arm",
"bandaged ear",
"bandaged fingers",
"bandaged foot",
"bandaged hand",
"bandaged head",
"bandaged leg",
"bandaged wrist",
"bandages",
"bandaid",
"bandaid hair ornament",
"bandaid on ahoge",
"bandaid on arm",
"bandaid on cheek",
"bandaid on face",
"bandaid on foot",
"bandaid on hand",
"bandaid on head",
"bandaid on knee",
"bandaid on leg",
"bandaid on neck",
"bandaid on nose",
"bandaid on pussy",
"bandaid on thigh",
"bandaids on nipples",
"bandana",
"bandolier",
"bangle",
"bangs pinned back",
"banister",
"banknote",
"banner",
"baozi",
"bar (place)",
"bar censor",
"bar stool",
"barbed wire",
"barbell",
"barbell piercing",
"barcode",
"barcode scanner",
"barcode tattoo",
"barding",
"bare arms",
"bare back",
"bare bush",
"bare hips",
"bare legs",
"bare shoulders",
"bare tree",
"barefoot",
"barefoot sandals (jewelry)",
"barista",
"barking",
"barn",
"barre",
"barrel",
"barrel shroud",
"barrett m82",
"barrett mrad",
"bars",
"bartender",
"baseball",
"baseball bat",
"baseball cap",
"baseball mitt",
"baseball stadium",
"baseball uniform",
"bashlik",
"basket",
"basket hilt",
"basketball",
"basketball (object)",
"basketball court",
"basketball hoop",
"basketball jersey",
"basketball uniform",
"bass guitar",
"bassinet",
"bat (animal)",
"bat bowtie",
"bat ears",
"bat girl",
"bat hair ornament",
"bat ornament",
"bat wings",
"bath",
"bath stool",
"bath yukata",
"bathing",
"bathrobe",
"bathroom",
"bathroom scale",
"bathtub",
"baton (weapon)",
"battery indicator",
"battle",
"battle axe",
"battle belt",
"battle damage",
"battle of berlin",
"battle of britain",
"battle of midway",
"battle rifle",
"battle standard",
"battlefield",
"battleship",
"baumkuchen",
"bayonet",
"bdsm",
"beach",
"beach mat",
"beach towel",
"beach umbrella",
"beach volleyball",
"beachball",
"beached",
"bead bracelet",
"bead necklace",
"beads",
"beak",
"beak hold",
"beaker",
"beam rifle",
"beamed eighth notes",
"beanie",
"beans",
"bear",
"bear ears",
"bear girl",
"bear hat",
"bear print",
"beard",
"beauty treatment",
"beckoning",
"bed",
"bed frame",
"bed invitation",
"bed sheet",
"bedroom",
"bedwetting",
"bee",
"bee girl",
"bee wings",
"beehive",
"beer",
"beer bottle",
"beer can",
"beer glass",
"beer mug",
"beetle",
"before and after",
"behelit",
"behind another",
"beige background",
"beige cardigan",
"beige jacket",
"beige sweater",
"belgium",
"bell",
"bell tower",
"belly",
"belly grab",
"belly poke",
"belly rub",
"belt",
"belt boots",
"belt buckle",
"belt chain",
"belt choker",
"belt collar",
"belt pouch",
"bench",
"bending",
"bendy straw",
"benelli m4",
"bent over",
"bentley",
"bentley continental gt",
"bento",
"beret",
"beretta 92",
"beretta px4",
"berlin",
"bernese mountain dog",
"berry",
"bespectacled",
"bestiality",
"between breasts",
"between fingers",
"between legs",
"between toes",
"bevor",
"bf 109",
"bib",
"biblically accurate angel",
"biceps",
"bicorne",
"bicycle",
"bicycle basket",
"bicycle helmet",
"big belly",
"big enough (meme)",
"big hair",
"big mac",
"big nose",
"bike shorts",
"bike shorts under skirt",
"bikini",
"bikini armor",
"bikini around one leg",
"bikini bottom aside",
"bikini bottom only",
"bikini pull",
"bikini skirt",
"bikini tan",
"bikini top lift",
"bikini top only",
"bikini under clothes",
"bilingual",
"billboard",
"binaural microphone",
"binder",
"binoculars",
"biohazard symbol",
"biology",
"bioluminescence",
"biopunk",
"biplane",
"bipod",
"birch tree",
"bird",
"bird hair ornament",
"bird legs",
"bird on hand",
"bird on head",
"bird on shoulder",
"bird print",
"bird shadow puppet",
"bird tail",
"bird wings",
"birdcage",
"birthday",
"birthday cake",
"birthday party",
"bisected",
"bisexual female",
"bisexual flag",
"bisexual male",
"bit gag",
"bite mark",
"biting",
"biting another's finger",
"biting own lip",
"black apron",
"black arm warmers",
"black armband",
"black armor",
"black ascot",
"black background",
"black bag",
"black belt",
"black bikini",
"black bird",
"black blindfold",
"black bodysuit",
"black border",
"black bow",
"black bowtie",
"black bra",
"black bracelet",
"black bridal gauntlets",
"black buruma",
"black camisole",
"black cape",
"black capelet",
"black cardigan",
"black cat",
"black choker",
"black cloak",
"black coat",
"black collar",
"black corset",
"black dress",
"black eyeliner",
"black eyes",
"black flower",
"black footwear",
"black fur",
"black garter belt",
"black garter straps",
"black gloves",
"black hair",
"black hairband",
"black hakama",
"black halo",
"black headband",
"black headwear",
"black hole",
"black hood",
"black hoodie",
"black horns",
"black jacket",
"black kimono",
"black leggings",
"black legwear",
"black leotard",
"black lips",
"black mask",
"black mittens",
"black mouth",
"black nails",
"black neckerchief",
"black necktie",
"black neckwear",
"black one-piece swimsuit",
"black overalls",
"black pajamas",
"black panties",
"black pants",
"black pantyhose",
"black pasties",
"black pubic hair",
"black ribbon",
"black robe",
"black rose",
"black sailor collar",
"black sash",
"black scarf",
"black sclera",
"black scrunchie",
"black serafuku",
"black shirt",
"black shorts",
"black skin",
"black skirt",
"black sky",
"black sleeves",
"black snake",
"black socks",
"black sports bra",
"black suit",
"black swan (bird)",
"black sweater",
"black sweater vest",
"black tail",
"black tank top",
"black tears",
"black theme",
"black thighhighs",
"black umbrella",
"black vest",
"black vs white",
"black wetsuit",
"black wings",
"black wristband",
"black-framed eyewear",
"blacked (phrase)",
"blackmail",
"blacksmith",
"blank censor",
"blank eyes",
"blank speech bubble",
"blank stare",
"blanket",
"blanket hug",
"blazer",
"blender logo",
"blind",
"blindfold",
"bliss (image)",
"blizzard",
"blob",
"blob (google)",
"block (object)",
"blocking",
"blonde hair",
"blonde pubic hair",
"blood",
"blood bag",
"blood from eyes",
"blood from mouth",
"blood on arm",
"blood on clothes",
"blood on dress",
"blood on face",
"blood on gloves",
"blood on ground",
"blood on hands",
"blood on knife",
"blood on wall",
"blood on weapon",
"blood splatter",
"blood stain",
"blood trail",
"blood type",
"bloodshot eyes",
"bloom",
"bloomers",
"blouse",
"blowgun",
"blowing smoke",
"blue anus",
"blue apron",
"blue armband",
"blue armor",
"blue ascot",
"blue background",
"blue bag",
"blue belt",
"blue bikini",
"blue bird",
"blue bow",
"blue bowtie",
"blue bra",
"blue bracelet",
"blue buruma",
"blue butterfly",
"blue cape",
"blue capelet",
"blue cardigan",
"blue choker",
"blue cloak",
"blue coat",
"blue collar",
"blue corset",
"blue dress",
"blue eyes",
"blue eyeshadow",
"blue feathers",
"blue flower",
"blue footwear",
"blue fur",
"blue gemstone",
"blue gloves",
"blue hair",
"blue hairband",
"blue hakama",
"blue headband",
"blue headwear",
"blue hoodie",
"blue horns",
"blue jacket",
"blue kimono",
"blue leggings",
"blue legwear",
"blue leotard",
"blue lips",
"blue lipstick tube",
"blue mask",
"blue nails",
"blue neckerchief",
"blue necktie",
"blue neckwear",
"blue one-piece swimsuit",
"blue outline",
"blue overalls",
"blue pajamas",
"blue panties",
"blue pants",
"blue pantyhose",
"blue pupils",
"blue ribbon",
"blue robe",
"blue rose",
"blue sailor collar",
"blue sash",
"blue scarf",
"blue sclera",
"blue screen of death",
"blue scrunchie",
"blue serafuku",
"blue shirt",
"blue shorts",
"blue skin",
"blue skirt",
"blue sky",
"blue sleeves",
"blue socks",
"blue sports bra",
"blue stripes",
"blue suit",
"blue sweater",
"blue tabard",
"blue tank top",
"blue theme",
"blue thighhighs",
"blue tongue",
"blue track suit",
"blue vest",
"blue-framed eyewear",
"blue-tinted eyewear",
"bluebell (flower)",
"blueberry",
"blunt bangs",
"blunt ends",
"blur censor",
"blurry",
"blurry background",
"blurry foreground",
"blurry vision",
"blush",
"blush stickers",
"blush visible through hair",
"bmw",
"boar",
"board eraser",
"board game",
"boat",
"bob cut",
"bobby pin",
"bobby socks",
"body armor",
"body blush",
"body cam",
"body control",
"body freckles",
"body fur",
"body markings",
"body pillow",
"body writing",
"bodypaint",
"bodystocking",
"bodysuit",
"bodysuit under clothes",
"boeing bird of prey",
"boeing x-32",
"boeing x-45",
"bofors 40 mm gun",
"bokeh",
"bold and brash (spongebob squarepants)",
"bolt",
"bolt action",
"bolter",
"bomb",
"bomb suit",
"bomber",
"bomber jacket",
"bondage",
"bondage gear",
"bondage mask",
"bone",
"bone hair ornament",
"boned meat",
"bonk",
"bonnet",
"boobplate",
"book",
"book on head",
"book on lap",
"book stack",
"bookbag",
"bookend",
"booklet",
"bookmark",
"bookshelf",
"boom barrier",
"boombox",
"boot knife",
"booth seating",
"boots",
"border",
"borrowed character",
"borrowed clothes",
"borrowed weapon",
"bottle",
"bottle to cheek",
"bottomless",
"bougu",
"bouncing ass",
"bouncing breasts",
"bound",
"bound ankles",
"bound arms",
"bound legs",
"bound thighs",
"bound together",
"bound torso",
"bound wrists",
"bouquet",
"boustrophedon order",
"bow",
"bow (music)",
"bow (weapon)",
"bow bikini",
"bow bra",
"bow choker",
"bow earrings",
"bow hairband",
"bow legwear",
"bow panties",
"bowing",
"bowl",
"bowl cut",
"bowl hat",
"bowlegged pose",
"bowtie",
"box",
"box of chocolates",
"boxcutter",
"boxer briefs",
"boy on top",
"bra",
"bra lift",
"bra peek",
"bra pull",
"bra strap",
"bra visible through clothes",
"bracelet",
"bracer",
"braces",
"braid",
"braided bangs",
"braided beard",
"braided bun",
"braided ponytail",
"braiding hair",
"bralines",
"branch",
"brand name imitation",
"brass knuckles",
"brassard",
"brazier",
"bread",
"bread bun",
"bread slice",
"breakfast",
"breaking",
"breast conscious",
"breast curtains",
"breast cutout",
"breast envy",
"breast expansion",
"breast focus",
"breast hold",
"breast lift",
"breast milk",
"breast pillow",
"breast pocket",
"breast press",
"breast pull",
"breast pump",
"breast rest",
"breast smother",
"breast strap",
"breast sucking",
"breast tattoo",
"breastfeeding",
"breastless clothes",
"breastplate",
"breasts",
"breasts apart",
"breasts on another's back",
"breasts on glass",
"breasts on head",
"breasts on table",
"breasts out",
"breasts squeezed together",
"breaststrap (saddle)",
"breath",
"breathing fire",
"breeding mount",
"brick",
"brick floor",
"brick road",
"brick wall",
"bridal garter",
"bridal gauntlets",
"bridal veil",
"bride",
"bridge",
"briefcase",
"brigandine (armor)",
"bright pupils",
"british army",
"brn-180",
"broad shoulders",
"broccoli",
"brodie helmet",
"broken",
"broken chain",
"broken cup",
"broken glass",
"broken heart",
"broken horn",
"broken mirror",
"broken neck",
"broken shield",
"broken sword",
"broken umbrella",
"broken wall",
"broken weapon",
"broken window",
"brooch",
"broom",
"broom riding",
"brother and sister",
"brown apron",
"brown background",
"brown bag",
"brown belt",
"brown border",
"brown bow",
"brown bowtie",
"brown bra",
"brown cape",
"brown capelet",
"brown cardigan",
"brown cat",
"brown choker",
"brown cloak",
"brown coat",
"brown collar",
"brown dress",
"brown eyes",
"brown facial hair",
"brown feathers",
"brown flower",
"brown footwear",
"brown fur",
"brown gloves",
"brown hair",
"brown hairband",
"brown headwear",
"brown hoodie",
"brown horns",
"brown jacket",
"brown legwear",
"brown lips",
"brown mittens",
"brown nails",
"brown necktie",
"brown panties",
"brown pants",
"brown pantyhose",
"brown poncho",
"brown pubic hair",
"brown ribbon",
"brown robe",
"brown sailor collar",
"brown scarf",
"brown scrunchie",
"brown shirt",
"brown shorts",
"brown skirt",
"brown sky",
"brown sleeves",
"brown socks",
"brown sweater",
"brown sweater vest",
"brown tank top",
"brown theme",
"brown thighhighs",
"brown vest",
"brown wings",
"brown-framed eyewear",
"browning auto 5",
"browning m1919",
"browning m2",
"bruise",
"bruise on face",
"brush",
"brush stroke",
"brushing hair",
"brushing own hair",
"brushing teeth",
"btr",
"btr-4 bucephalus",
"btr-80",
"bubble",
"bubble bath",
"bubble blowing",
"bubble tea",
"buck teeth",
"bucket",
"bucket of water",
"bucket on head",
"bucket-wheel excavator",
"buckle",
"buckler",
"budenovka",
"bug",
"bug spray",
"bugatti",
"bugatti type 57 sc atlantic",
"building",
"bukkake",
"bulgaria",
"bulge",
"bulge press",
"bulges touching",
"bullet",
"bulletin board",
"bulletproof vest",
"bullpup",
"bullying",
"bun cover",
"bunching hair",
"bundesheer (austria)",
"bundeswehr",
"bunk bed",
"bunker",
"buran (spacecraft)",
"burger",
"burglar",
"burn scar",
"burning",
"burning building",
"burnt",
"burnt clothes",
"burnt food",
"bursting ass",
"bursting breasts",
"buruma",
"buruma around one leg",
"buruma pull",
"bus",
"bus interior",
"bus stop",
"bush",
"business suit",
"bust (sculpture)",
"buster sword",
"bustier",
"butler",
"butt crack",
"butt plug",
"butter",
"butterfly",
"butterfly choker",
"butterfly earrings",
"butterfly hair ornament",
"butterfly hat ornament",
"butterfly on hand",
"butterfly on head",
"butterfly ornament",
"butterfly print",
"butterfly tattoo",
"buttjob",
"buttjob over clothes",
"buttjob under clothes",
"button badge",
"button eyes",
"button gap",
"buttoned cuffs",
"buttons",
"buying condoms",
"c-string",
"c:",
"cabbage",
"cabbie hat",
"cabin",
"cabinet",
"cable",
"cable knit",
"cable tail",
"cable tie",
"cactus",
"cafe",
"cafeteria",
"cage",
"cake",
"cake slice",
"calculator",
"calendar (medium)",
"calendar (object)",
"calico",
"call an ambulance but not for me (meme)",
"calla lily",
"calligraphy",
"calligraphy brush",
"calling",
"camcorder",
"camellia",
"cameltoe",
"camembert (headgear)",
"cameo",
"camera",
"camisole",
"camouflage",
"camouflage coat",
"camouflage headwear",
"camouflage jacket",
"camouflage paint",
"camouflage pants",
"camouflage shirt",
"campaign hat",
"campbell's",
"camper",
"can",
"canada",
"canadian army",
"canadian flag",
"canal",
"candle",
"candlelight",
"candlestand",
"candy",
"candy apple",
"candy heart",
"candy store",
"cane",
"canned coffee",
"canned food",
"cannon",
"canon (company)",
"canopy (aircraft)",
"canopy bed",
"canteen",
"canvas (object)",
"cape",
"capelet",
"caplock",
"cappello alpino",
"capri pants",
"capsule corp",
"captcha",
"captured",
"car",
"car crash",
"car interior",
"car keys",
"carabiner",
"caramelldansen",
"carapace",
"carbonara (food)",
"carcano",
"card",
"card (medium)",
"cardboard",
"cardboard box",
"cardigan",
"cardigan around waist",
"cardigan on shoulders",
"cardigan vest",
"cardiogram",
"caressing testicles",
"cargo",
"cargo aircraft",
"cargo pants",
"carl gustaf recoilless rifle",
"carpet",
"carrot",
"carrot hair ornament",
"carrying",
"carrying bag",
"carrying over shoulder",
"carrying overhead",
"carrying person",
"carrying under arm",
"cart",
"carton",
"cartoon bone",
"cartridge",
"carving fork",
"case",
"cash register",
"cashier",
"casing ejection",
"cassette tape",
"cassock",
"cast",
"casting spell",
"castle",
"casual",
"cat",
"cat bag",
"cat boy",
"cat day",
"cat ear headphones",
"cat ear legwear",
"cat ears",
"cat girl",
"cat hair ornament",
"cat hat",
"cat hood",
"cat lingerie",
"cat on head",
"cat on lap",
"cat on shoulder",
"cat ornament",
"cat paw",
"cat paws",
"cat print",
"cat slippers",
"cat tail",
"cat tattoo",
"cat teaser",
"catapult",
"caterpillar tracks",
"cathedral",
"catholic",
"caught",
"cauldron",
"caustics",
"caution",
"cavalier hat",
"cavalry",
"cave",
"cave interior",
"cd",
"cd case",
"ceiling",
"ceiling light",
"cell (biology)",
"cell nucleus",
"cellphone",
"cellphone charm",
"cellphone photo",
"cellular tower",
"celtic",
"censer",
"censored",
"censored nipples",
"censored symbol",
"centaur",
"centauroid",
"center frills",
"center opening",
"center-flap bangs",
"centurii-chan (artist)",
"cephalopod eyes",
"cereal",
"cervix",
"ch-53",
"chain",
"chain earrings",
"chain leash",
"chain necklace",
"chain-link fence",
"chained",
"chained wrists",
"chainmail",
"chainsaw",
"chair",
"chalk",
"chalkboard",
"chalkboard sign",
"champagne flute",
"chandelier",
"chanel",
"chaneque",
"chaps",
"char-siu",
"character age",
"character charm",
"character doll",
"character hair ornament",
"character name",
"character pin",
"character print",
"character profile",
"charger",
"charging device",
"charging forward",
"charm (object)",
"chart",
"chasing",
"chastity belt",
"chastity cage",
"chat log",
"cheating (relationship)",
"checkerboard cookie",
"checkered",
"checkered background",
"checkered clothes",
"checkered floor",
"checkered scarf",
"checkered skirt",
"checkered trim",
"checkered wall",
"cheek bulge",
"cheek pinching",
"cheek poking",
"cheek press",
"cheek rest",
"cheek squash",
"cheek-to-cheek",
"cheering",
"cheerleader",
"cheese",
"cheese vs. cheese (meme)",
"chef",
"chef hat",
"chemise",
"chemistry",
"chemistry set",
"cherry",
"cherry blossom print",
"cherry blossoms",
"cherry print",
"cherub",
"chess",
"chess piece",
"chessboard",
"chest armor",
"chest guard",
"chest hair",
"chest harness",
"chest of drawers",
"chest rig",
"chest tattoo",
"chestnut mouth",
"chevrolet",
"chevrolet nova",
"chevron (symbol)",
"chewing",
"chewing gum",
"chibi",
"chibi inset",
"chicken",
"chicken leg",
"chihaya (clothing)",
"chikaretsu",
"child",
"child carry",
"child's drawing",
"chimerism",
"chimney",
"chin strap",
"china",
"china dress",
"chinese armor",
"chinese clothes",
"chinese empire",
"chinese spoon",
"chinese text",
"chinese zodiac",
"chip star",
"chips (food)",
"chisato and takina kicking each other's butt (meme)",
"chloroplast",
"chocolate",
"chocolate bar",
"chocolate cake",
"chocolate on body",
"chocolate on breasts",
"chocolate on clothes",
"chocolate on head",
"chocolate on legs",
"choke hold",
"choker",
"choker removed",
"chopping",
"chopsticks",
"christianity",
"christmas",
"christmas cake",
"christmas lights",
"christmas ornaments",
"christmas tree",
"christmas tree print",
"chromatic aberration",
"chrysanthemum",
"church",
"cicada",
"cigar",
"cigarette",
"cigarette butt",
"cigarette case",
"cigarette pack",
"circle",
"circlet",
"circuit board",
"circular border",
"circular saw",
"city",
"city lights",
"cityscape",
"clapping",
"clash",
"classroom",
"claw pose",
"claw ring",
"clawed boots",
"clawed gauntlets",
"claws",
"clay",
"claymore (sword)",
"cleaning",
"cleaning brush",
"clear insertion",
"clear sky",
"cleavage",
"cleavage cutout",
"cleavage reach",
"cleaver",
"cleft of venus",
"clenched hand",
"clenched hands",
"clenched teeth",
"cleric",
"cliff",
"climbing wall",
"clip studio paint",
"clipboard",
"clipping nails",
"clitoral hood",
"clitoral stimulation",
"clitoris",
"clitoris piercing",
"cloak",
"clock",
"clock hands",
"clock tower",
"clone trooper",
"close-up",
"closed eyes",
"closed mouth",
"closed umbrella",
"closing door",
"cloth",
"cloth gag",
"clothed animal",
"clothed female nude female",
"clothed female nude male",
"clothed male nude female",
"clothed masturbation",
"clothed robot",
"clothed sex",
"clothes",
"clothes around waist",
"clothes between breasts",
"clothes down",
"clothes dryer",
"clothes grab",
"clothes hanger",
"clothes in front",
"clothes in mouth",
"clothes iron",
"clothes lift",
"clothes on floor",
"clothes on shoulders",
"clothes on/clothes off",
"clothes only",
"clothes pin",
"clothes pull",
"clothes tug",
"clothes writing",
"clothesline",
"clothing aside",
"clothing cutout",
"cloud",
"cloud hair ornament",
"cloud print",
"cloudy sky",
"clover",
"clown",
"club (shape)",
"clubroom",
"clueless",
"clutter",
"coach",
"coat",
"coat of arms",
"coat on shoulders",
"coat partially removed",
"coat tug",
"coattails",
"cobblestone",
"coca-cola",
"cock ring",
"cockade",
"cocking gun",
"cockroach",
"cockroach girl",
"cocktail",
"cocktail dress",
"cocktail flower",
"cocktail glass",
"cocktail shaker",
"coconut tree",
"cocoon",
"coffee",
"coffee cup",
"coffee grinder",
"coffee maker",
"coffee mug",
"coffee pot",
"coffee table",
"coffin",
"coif",
"coiled",
"coin",
"coin on string",
"coke-bottle glasses",
"cola",
"cold",
"cold pack",
"collage",
"collar",
"collar grab",
"collar tabs",
"collarbone",
"collared apron",
"collared cape",
"collared coat",
"collared dress",
"collared jacket",
"collared shirt",
"colonnade",
"color guide",
"colored anus",
"colored eyelashes",
"colored inner hair",
"colored nipples",
"colored pencil",
"colored pencil (medium)",
"colored pubic hair",
"colored sclera",
"colored skin",
"colored text",
"colored tips",
"colored tongue",
"colorful",
"colosseum",
"colt canada c7",
"column",
"comb",
"combat boots",
"combat helmet",
"combat knife",
"combat shirt",
"come hither",
"comet",
"comic",
"comic sans",
"comiket",
"coming out",
"company connection",
"company name",
"comparison",
"competition school swimsuit",
"competition swimsuit",
"completely nude",
"compound eyes",
"computer",
"computer tower",
"computer virus",
"concert",
"condensation",
"condom",
"condom belt",
"condom box",
"condom in mouth",
"condom on nipples",
"condom on penis",
"condom packet strip",
"condom wrapper",
"confession",
"confessional",
"confetti",
"confetti balloon",
"confused",
"conifer",
"conjoined",
"conquistador",
"consensual tentacles",
"console war",
"constellation",
"constricted pupils",
"construction",
"construction site",
"construction worker",
"contemporary",
"content rating",
"contrail",
"contrapposto",
"contrast",
"controller",
"convenience store",
"convenient censoring",
"convenient leg",
"convention",
"convention greeting",
"converse",
"convertible",
"conveyor belt",
"conveyor belt sushi",
"cookie",
"cooking",
"cooking pot",
"cooler",
"cooperative fellatio",
"copyright name",
"copyright notice",
"core",
"cork",
"corked bottle",
"corn dog",
"cornrows",
"corrugated galvanised iron sheet",
"corruption",
"corset",
"corset piercing",
"cosmetics",
"cosmos (flower)",
"cosplay",
"cotton candy",
"cotton swab",
"couch",
"counter",
"country connection",
"couple",
"courtroom",
"cousins",
"couter",
"cover",
"cover image",
"cover page",
"covered abs",
"covered anus",
"covered clitoris",
"covered collarbone",
"covered eyes",
"covered face",
"covered mouth",
"covered navel",
"covered nipples",
"covered penis",
"covered pussy",
"covered testicles",
"covering anus",
"covering body",
"covering breasts",
"covering crotch",
"covering face",
"covering head",
"covering nipples",
"covering one breast",
"covering one eye",
"covering own eyes",
"covering own mouth",
"covering privates",
"cow",
"cow ears",
"cow girl",
"cow horns",
"cow print",
"cow print bikini",
"cow print gloves",
"cow tail",
"cowbell",
"cowboy",
"cowboy boots",
"cowboy hat",
"cowboy shot",
"cowboy western",
"cowering",
"cowgirl (western)",
"cowgirl position",
"cowlick",
"cpu",
"crab",
"crack",
"crack of light",
"cracked floor",
"cracked glass",
"cracked screen",
"cracked skin",
"cracked wall",
"crane (animal)",
"crane (machine)",
"crane game",
"crash landing",
"crate",
"crater",
"crayon",
"crazy",
"crazy eyes",
"crazy smile",
"cream",
"cream on body",
"cream on face",
"cream puff",
"creature",
"creature and personification",
"creature on shoulder",
"creepy eyes",
"crepe",
"crescent",
"crescent earrings",
"crescent hair ornament",
"crescent hat ornament",
"crescent moon",
"crescent pin",
"crescent print",
"crescent tattoo",
"crescent-shaped pupils",
"crest",
"crew neck",
"crime prevention buzzer",
"criss-cross back-straps",
"criss-cross halter",
"crocodile",
"crocodilian",
"crocodilian tail",
"crocs",
"croissant",
"crop circle",
"crop top",
"crop top overhang",
"cropped arms",
"cropped hoodie",
"cropped jacket",
"cropped legs",
"cropped shirt",
"cropped shoulders",
"cropped sweater",
"cropped torso",
"crops",
"croquette",
"cross",
"cross background",
"cross bracelet",
"cross choker",
"cross earrings",
"cross hair ornament",
"cross hat ornament",
"cross moline",
"cross necklace",
"cross ornament",
"cross pasties",
"cross potent",
"cross print",
"cross tie",
"cross-body stretch",
"cross-eyed",
"cross-laced bikini",
"cross-laced clothes",
"cross-laced cutout",
"cross-laced dress",
"cross-laced footwear",
"cross-laced hoodie",
"cross-laced skirt",
"cross-laced sleeves",
"cross-laced slit",
"cross-laced top",
"cross-section",
"cross-shaped pupils",
"crossbow",
"crossbow bolt",
"crossdressing",
"crossed ankles",
"crossed arms",
"crossed bangs",
"crossed fingers",
"crossed legs",
"crosshair",
"crosshair pupils",
"crossover",
"crosswalk",
"crotch",
"crotch cutout",
"crotch rope",
"crotch rub",
"crotch seam",
"crotchless",
"crotchless panties",
"crow",
"crowbar",
"crowd",
"crowded",
"crown",
"crown braid",
"crt",
"crucifix",
"crumbs",
"crumpled paper",
"crushed can",
"crutch",
"crying",
"crying aqua (meme)",
"crying cat (meme)",
"crying emoji",
"crying with eyes open",
"cryptid",
"crystal",
"crystal ball",
"crystal eye",
"cube",
"cubicle",
"cuddling",
"cuffs",
"cuirass",
"cuirassier",
"cuisses",
"cum",
"cum in ass",
"cum in bowl",
"cum in clothes",
"cum in container",
"cum in mouth",
"cum in pussy",
"cum on armpits",
"cum on ass",
"cum on bed",
"cum on body",
"cum on breasts",
"cum on clothes",
"cum on crotch",
"cum on eyewear",
"cum on feet",
"cum on fingers",
"cum on floor",
"cum on gloves",
"cum on hair",
"cum on hands",
"cum on legs",
"cum on legwear",
"cum on male",
"cum on penis",
"cum on pussy",
"cum on self",
"cum on sheets",
"cum on skirt",
"cum on stomach",
"cum on tongue",
"cum on wall",
"cum overflow",
"cum pool",
"cum string",
"cum through clothes",
"cumdrip",
"cumulonimbus cloud",
"cunnilingus",
"cunnilingus gesture",
"cunnilingus through clothes",
"cunt punt",
"cup",
"cup ramen",
"cup size",
"cupboard",
"cupcake",
"cupless bra",
"cupping hands",
"curled fingers",
"curled horns",
"curling iron",
"curly hair",
"curry",
"cursive",
"cursor",
"curtain grab",
"curtains",
"curtsey",
"curved monitor",
"curvy",
"cushion",
"cutoffs",
"cuts",
"cutting board",
"cutting hair",
"cutting own hair",
"cyberpunk",
"cyborg",
"cycle",
"cyclops",
"cynthia (pokemon) (cosplay)",
"cyrillic",
"cz 805 bren",
"cz scorpion evo 3",
"d.va (overwatch) (cosplay)",
"d:",
"daewoo k1",
"daewoo k11",
"daewoo k2",
"dagger",
"daikoku parking area",
"dakimakura (medium)",
"dakimakura (object)",
"damaged",
"dancer",
"dancing",
"dangle earrings",
"dango",
"danmaku comments",
"dappled sunlight",
"dark",
"dark areolae",
"dark aura",
"dark background",
"dark blue hair",
"dark elf",
"dark halo",
"dark nipples",
"dark penis",
"dark persona",
"dark room",
"dark skin",
"dark-skinned female",
"dark-skinned male",
"darkness",
"dated",
"dating",
"dawn",
"day",
"deal with it (meme)",
"death",
"death flag",
"death note (object)",
"debris",
"decade comparison",
"decantering",
"decora",
"deep penetration",
"deep skin",
"deepthroat",
"deer",
"deer ears",
"deer girl",
"deer print",
"defeat",
"defensive wall",
"defloration",
"delinquent",
"delivery",
"demon",
"demon boy",
"demon girl",
"demon horns",
"demon tail",
"demon wings",
"denim",
"denim apron",
"denim jacket",
"denim shorts",
"dental chair",
"dentist",
"depth of field",
"desert",
"desert camouflage",
"desert tech mdr",
"desk",
"desk lamp",
"dessert",
"destroyed",
"destroyer",
"destruction",
"detached collar",
"detached sleeves",
"detached wings",
"determined",
"deutsche bahn",
"deviantart username",
"dew drop",
"diagonal bangs",
"diagonal stripes",
"diagonal-striped bow",
"diagonal-striped bowtie",
"diagonal-striped clothes",
"diagonal-striped necktie",
"diagram",
"dialogue options",
"diamond (gemstone)",
"diamond (shape)",
"diamond cutout",
"diamond earrings",
"diamond mouth",
"diaper",
"diary",
"different reflection",
"different shadow",
"diffraction spikes",
"digital camouflage",
"digital clock",
"digital dissolve",
"digitigrade",
"digivice",
"dilapidated",
"dildo",
"dildo gag",
"dildo reveal",
"dildo riding",
"dildo under mask",
"dimensional hole",
"dimples of venus",
"diner",
"dinner",
"dinosaur",
"dinosaur girl",
"dinosaur tail",
"dior",
"diploma",
"dirndl",
"dirt",
"dirt road",
"dirty",
"dirty clothes",
"dirty face",
"dirty hands",
"disembodied eye",
"disembodied head",
"disembodied limb",
"disembodied penis",
"disgust",
"dishes",
"display",
"disposable camera",
"disposable cup",
"dissolving",
"distortion",
"dive bomber",
"diving mask",
"diving suit",
"dna",
"doberman",
"dock",
"doctor",
"dog",
"dog ears",
"dog girl",
"dog penis",
"dog shadow puppet",
"dog tags",
"dog tail",
"dog-shaped pillow",
"doggystyle",
"doily",
"dojo",
"doll",
"doll joints",
"dollar sign",
"dollhouse view",
"dolphin",
"dolphin print",
"dolphin shorts",
"dome",
"dominatrix",
"don't worry i'm wearing",
"donation box",
"dondurma (ice cream)",
"dongtan dress",
"door",
"door handle",
"doorknob",
"doorway",
"doqute stuffed doll",
"doritos",
"dorsiflexion",
"dot mouth",
"dot nose",
"dou",
"double \\m/",
"double breast sucking",
"double bun",
"double fox shadow puppet",
"double handjob",
"double horizontal stripe",
"double middle finger",
"double penetration",
"double scoop",
"double thumbs up",
"double v",
"double-breasted",
"double-decker hamburger bun",
"double-parted bangs",
"doughnut",
"doujin cover",
"dove",
"down jacket",
"downblouse",
"doyagao",
"dp-27",
"dragging",
"dragon",
"dragon ball (object)",
"dragon boy",
"dragon girl",
"dragon horns",
"dragon on head",
"dragon on shoulder",
"dragon ornament",
"dragon print",
"dragon tail",
"dragon wings",
"dragonfly",
"dragoon",
"dragoon helmet",
"dragunov svd",
"drain (object)",
"drakeposting (meme)",
"dramatic dmitry (meme)",
"drawbridge",
"drawer",
"drawing",
"drawing (object)",
"drawing bow",
"drawing tablet",
"drawn ears",
"drawn whiskers",
"drawstring",
"dream catcher",
"dress",
"dress bow",
"dress lift",
"dress pants",
"dress pull",
"dress shirt",
"dress shoes",
"dress tug",
"dress uniform",
"dressing",
"dressing another",
"dressing room",
"drill",
"drill hair",
"drill hand",
"drill sidelocks",
"drink",
"drink can",
"drinking",
"drinking blood",
"drinking glass",
"drinking horn",
"drinking straw",
"drinking straw in mouth",
"dripping",
"driving",
"drooling",
"drop earrings",
"drop tank",
"dropper",
"dropping",
"drugged",
"drugs",
"drum",
"drum (container)",
"drum magazine",
"drumsticks",
"drunk",
"dry lips",
"dryad",
"drying",
"drying hair",
"dual persona",
"dual sights",
"dual wielding",
"dualshock",
"duck",
"duck earrings",
"duck hair ornament",
"duel",
"duffel bag",
"duffel coat",
"dullahan",
"dumbbell",
"dummy",
"dumpling",
"dungeon",
"dusk",
"dust",
"dust cloud",
"duster",
"dutch angle",
"dvd (object)",
"dvd case",
"dwarf",
"dyed bangs",
"dynamite",
"e.g.o (project moon)",
"eagle",
"ear biting",
"ear blush",
"ear bow",
"ear chain",
"ear covers",
"ear focus",
"ear piercing",
"ear protection",
"ear ribbon",
"ear tag",
"earbuds",
"earclip",
"earmuffs",
"earmuffs around neck",
"earphones",
"earphones removed",
"earpiece",
"earplugs",
"earrings",
"ears down",
"ears through headwear",
"ears visible through hair",
"earth (ornament)",
"earth (planet)",
"earth hair",
"earthworm",
"easel",
"east asian architecture",
"east german",
"eastern dragon",
"eating",
"eclipse",
"educational",
"eel hat",
"eevee ears",
"effects pedal",
"egasumi",
"egg",
"egg (food)",
"egg hair ornament",
"egg vibrator",
"egg yolk",
"egyptian",
"egyptian clothes",
"egyptian plover",
"eiffel tower",
"eighth note",
"eisenhower jacket",
"ejaculating while penetrated",
"ejaculation",
"ejaculation under clothes",
"elbow gloves",
"elbow on table",
"elbow pads",
"elbow rest",
"elbow sleeve",
"elbow spikes",
"elbows on knees",
"elbows on table",
"elcan scope",
"eldritch abomination",
"electric fan",
"electric guitar",
"electric kettle",
"electric plug",
"electric plug tail",
"electrical outlet",
"electricity",
"electrocution",
"elemental (creature)",
"elevator",
"elevator door",
"elf",
"elite ii (arknights)",
"ema",
"embarrassed",
"embers",
"emblem",
"embroidered",
"embroidery",
"emo fashion",
"emoji",
"emoticon",
"emotionless sex",
"emphasis lines",
"employee uniform",
"empty eyes",
"empty picture frame",
"emr camouflage",
"endoplasmic reticulum",
"energy",
"energy blade",
"energy drink",
"energy gun",
"energy sword",
"energy weapon",
"engine",
"engineering nonsense",
"english flag",
"english text",
"engraved",
"engrish text",
"enmaided",
"enpera",
"entrance",
"entrenching tool",
"envelope",
"enveloped",
"eotech",
"epaulettes",
"equation",
"equipment layout",
"eraser",
"erection",
"erection under clothes",
"erlenmeyer flask",
"error message",
"escalator",
"espresso (drink)",
"estonian flag",
"european architecture",
"evening",
"evening gown",
"evil grin",
"evil smile",
"excavator",
"excessive cum",
"excessive pubic hair",
"excessive pussy juice",
"excited",
"exercise",
"exercise ball",
"exhausted",
"exhibition drill",
"exhibitionism",
"exit sign",
"exoskeleton",
"expectations/reality",
"explosion",
"explosive",
"exposed muscle",
"exposed pocket",
"expression chart",
"expressionless",
"expressions",
"expressive hair",
"extra arms",
"extra digits",
"extra ears",
"extra eyes",
"extra hands",
"extra legs",
"extra mouth",
"eye contact",
"eye focus",
"eye glitter",
"eye in palm",
"eye print",
"eye reflection",
"eye socket",
"eyeball",
"eyeball bracelet",
"eyeball hair ornament",
"eyebrow cut",
"eyebrow piercing",
"eyebrows",
"eyebrows hidden by hair",
"eyedrops",
"eyelashes",
"eyelid piercing",
"eyeliner",
"eyepatch",
"eyepatch bikini",
"eyes in shadow",
"eyes visible through hair",
"eyeshadow",
"eyeshadow under eye",
"eyewear on head",
"eyewear on headwear",
"eyewear view",
"f-117 nighthawk",
"f-14 tomcat",
"f-16 fighting falcon",
"f-18 hornet",
"f-22 raptor",
"f-35 lightning ii",
"f6f hellcat",
"face between breasts",
"face down",
"face filter",
"face in pillow",
"face punch",
"face to breasts",
"face-to-face",
"facebook logo",
"facebook username",
"faceless",
"faceless female",
"faceless male",
"facepaint",
"faceplate",
"faceswap",
"facial",
"facial hair",
"facial mark",
"facial tattoo",
"facing ahead",
"facing another",
"facing away",
"facing back",
"facing down",
"facing to the side",
"facing viewer",
"failure",
"fairy",
"fairy wings",
"fake ad",
"fake animal ears",
"fake antlers",
"fake horns",
"fake nails",
"fake phone screenshot",
"fake screenshot",
"fake tail",
"fake video",
"fake wings",
"fallen angel",
"fallen down",
"falling",
"falling feathers",
"falling leaves",
"falling petals",
"false smile",
"famas",
"family",
"family tree",
"fanbox username",
"fang",
"fang out",
"fanged bangs",
"fangs",
"fangs out",
"fanning face",
"fanning self",
"fanny pack",
"fantasy",
"fare gate",
"farm",
"fashion",
"fast food",
"fat",
"fat man",
"fat mons",
"father and child",
"father and daughter",
"father and son",
"fatigues",
"faucet",
"faulds",
"faux figurine",
"faux text",
"faux traditional media",
"fb msbs grot",
"fbi",
"feast",
"feather boa",
"feather dress",
"feather hair ornament",
"feather trim",
"feathered wings",
"feathers",
"february",
"fed by viewer",
"fedora",
"feeding",
"feet",
"feet on chair",
"feet on table",
"feet only",
"feet out of frame",
"feet up",
"fellatio",
"fellatio gesture",
"female butler",
"female ejaculation",
"female goblin",
"female masturbation",
"female orc",
"female orgasm",
"female pervert",
"female pov",
"female pubic hair",
"female service cap",
"femdom",
"feminization",
"fence",
"fender precision bass",
"fender telecaster",
"fern",
"ferrari",
"ferrari f60",
"ferris wheel",
"fertilization",
"festival",
"fetal position",
"fetch",
"fewer digits",
"fez hat",
"fff threesome",
"ffm threesome",
"fgm-148 javelin",
"fidgeting",
"field",
"field cap",
"field ration",
"fiery hair",
"fiery wings",
"fig",
"fighter jet",
"fighting",
"fighting stance",
"figure",
"fiji water",
"file",
"file cabinet",
"film grain",
"fim-92 stinger",
"fine art",
"fine art parody",
"fine fabric emphasis",
"finger frame",
"finger frame duo",
"finger in another's mouth",
"finger in navel",
"finger in own mouth",
"finger on trigger",
"finger tattoo",
"finger to cheek",
"finger to mouth",
"finger to own chin",
"fingering",
"fingerless gloves",
"fingernails",
"fingers to cheeks",
"fingers to mouth",
"fingers together",
"fingersmile",
"finland",
"finnish army",
"finnish clothes",
"finnish flag",
"fins",
"fire",
"fire alarm",
"fire axe",
"fire hydrant",
"firefighter",
"firefighter jacket",
"fireflies",
"firelock",
"fireplace",
"fireworks",
"firing",
"first aid",
"first aid kit",
"fish",
"fish (food)",
"fish bone",
"fish hair ornament",
"fish print",
"fish tail",
"fish tank",
"fishbowl",
"fisheye",
"fishnet armwear",
"fishnet gloves",
"fishnet pantyhose",
"fishnet sleeves",
"fishnet thighhighs",
"fishnet top",
"fishnets",
"fist pump",
"fisting",
"fitting room",
"fkey",
"flaccid",
"flag",
"flag background",
"flagpole",
"flail",
"flakpanzer gepard",
"flamberge",
"flamel symbol",
"flaming weapon",
"flanged mace",
"flare",
"flare gun",
"flared muzzle",
"flashback",
"flashbang",
"flashing",
"flashlight",
"flask",
"flat cap",
"flat chastity cage",
"flat chest",
"flat chest joke",
"flat color",
"flat envy",
"flat fuck friday (meme)",
"flatlining",
"flats",
"fleeing",
"fleur-de-lis",
"flexible",
"flick",
"flight attendant",
"flintlock",
"flip phone",
"flip-flops",
"flip-up sight",
"flipped hair",
"flippers",
"flipping food",
"flirting",
"floating",
"floating book",
"floating cape",
"floating clothes",
"floating hair",
"floating island",
"floating neckwear",
"floating object",
"floating rock",
"floating scarf",
"floating skull",
"flock",
"flood",
"floor",
"floorplan",
"floppy disk",
"floppy ears",
"floral arch",
"floral background",
"floral print",
"flower",
"flower basket",
"flower box",
"flower field",
"flower knot",
"flower on liquid",
"flower pot",
"flower tattoo",
"flower wreath",
"fluffy",
"fluorescent lamp",
"flustered",
"flute",
"fly",
"fly agaric",
"flyer",
"flying",
"flying animal",
"flying button",
"flying buttress",
"flying fish",
"flying paper",
"flying saucer",
"flying sweatdrops",
"fm 24/29",
"fn f2000",
"fn fal",
"fn fnc",
"fn mag",
"fn scar",
"fn scar 16",
"fn scar 17",
"focused",
"fog",
"fold-over boots",
"folded",
"folded clothes",
"folded hair",
"folded ponytail",
"folder",
"folding chair",
"folding fan",
"folding stock",
"foliage",
"food",
"food art",
"food fight",
"food focus",
"food in mouth",
"food insertion",
"food on body",
"food on face",
"food on head",
"food print",
"food stand",
"food wrapper",
"food-themed earrings",
"food-themed hair ornament",
"foodification",
"foot bath",
"foot focus",
"foot on head",
"foot out of frame",
"foot up",
"foot wraps",
"footjob",
"footprints",
"footsies",
"footwear bow",
"footwear flower",
"footwear focus",
"for the better right? (meme)",
"forced",
"forced orgasm",
"forced smile",
"ford",
"ford crown victoria",
"foregrip",
"forehead",
"forehead flick",
"forehead jewel",
"forehead mark",
"forehead protector",
"foreshortening",
"foreskin",
"foreskin insertion",
"foreskin pull",
"forest",
"fork",
"forked tongue",
"forklift certified (meme)",
"formal",
"forniphilia",
"fortress",
"fossil",
"fountain",
"four-leaf clover",
"fourth wall",
"fox",
"fox boy",
"fox ears",
"fox girl",
"fox mask",
"fox shadow puppet",
"fox tail",
"framed",
"framed breasts",
"framed image",
"framed insect",
"france",
"frankenstein's monster (cosplay)",
"frappuccino",
"frayed clothes",
"freckles",
"freediving",
"freedom of russia legion",
"french army",
"french braid",
"french clothes",
"french flag",
"french fries",
"french kiss",
"french text",
"fried chicken",
"fried egg",
"fried egg on toast",
"fried rice",
"fried rice prank (meme)",
"friends",
"frilled apron",
"frilled ascot",
"frilled bikini",
"frilled bonnet",
"frilled bra",
"frilled choker",
"frilled collar",
"frilled dress",
"frilled hairband",
"frilled headwear",
"frilled kimono",
"frilled leotard",
"frilled neck lizard",
"frilled panties",
"frilled pillow",
"frilled sailor collar",
"frilled shirt",
"frilled shirt collar",
"frilled shorts",
"frilled skirt",
"frilled sleeves",
"frilled socks",
"frilled thighhighs",
"frilled umbrella",
"frills",
"fringe trim",
"frisbee",
"frisbee doge (meme)",
"fritos",
"frog",
"frog boy",
"frog print",
"frog-mouth helm",
"froggy chair",
"from above",
"from behind",
"from below",
"from ground",
"from outside",
"from side",
"front-hook bra",
"front-tie bikini top",
"front-tie top",
"frottage",
"frown",
"fruit",
"fruit cup",
"fruit tart",
"fruit tree",
"frustrated",
"frying pan",
"fsb",
"ft-17",
"fu hua (azure empyrea) (cosplay)",
"fuck-me shirt",
"fucked silly",
"full armor",
"full body",
"full moon",
"full nelson",
"full stomach",
"full-body tattoo",
"full-face blush",
"full-length mirror",
"full-package futanari",
"fumo (doll)",
"fundoshi",
"fur",
"fur cape",
"fur capelet",
"fur choker",
"fur coat",
"fur collar",
"fur hat",
"fur jacket",
"fur scarf",
"fur shawl",
"fur sweater",
"fur trim",
"fur-tipped tail",
"fur-trimmed boots",
"fur-trimmed cape",
"fur-trimmed capelet",
"fur-trimmed coat",
"fur-trimmed collar",
"fur-trimmed dress",
"fur-trimmed gloves",
"fur-trimmed headwear",
"fur-trimmed hood",
"fur-trimmed jacket",
"fur-trimmed kimono",
"fur-trimmed shirt",
"fur-trimmed skirt",
"fur-trimmed sleeves",
"fur-trimmed thighhighs",
"furigana",
"furious",
"furisode",
"furoshiki",
"furrowed brow",
"furry",
"furry female",
"furry male",
"furry with furry",
"furry with non-furry",
"fusion",
"futa with female",
"futa with futa",
"futa with male",
"futakuchi-onna",
"futanari",
"futanari masturbation",
"futon",
"futuristic weapon",
"fuuin no tsue",
"g-string",
"gadsden flag",
"gag",
"gagged",
"gaiters",
"gakuran",
"galactic empire",
"galactic republic",
"galaxy",
"galil ace",
"gambeson",
"game boy",
"game boy (original)",
"game cartridge",
"game console",
"game controller",
"game link cable",
"game screenshot background",
"gamecube controller",
"gamepad",
"gameplay mechanics",
"gaming chair",
"gangbang",
"gao",
"gaping",
"garage",
"garand thumb",
"garbage truck",
"garden",
"garrison cap",
"garter belt",
"garter straps",
"gas mask",
"gas mask canister",
"gas station",
"gashadokuro",
"gate",
"gathers",
"gatling gun",
"gau-8",
"gauntlets",
"gauze",
"gaz-2975 tigr",
"gears",
"gecko",
"geforce rtx 2070 super",
"geisha",
"geissele urg-i",
"gem",
"gemini (constellation)",
"gender dysphoria",
"gender transitioning",
"genderswap",
"genderswap (ftm)",
"genderswap (mtf)",
"genre connection",
"german army",
"german clothes",
"german empire",
"german flag",
"german text",
"germany",
"germany oneesan (meme)",
"gesture",
"geta",
"getabako",
"gewehr 88",
"ghana (chocolate)",
"ghost",
"giancarlo esposito's \"i was acting\" (meme)",
"giant",
"giant monster",
"giant skeleton",
"giant snake",
"giantess",
"gibson les paul",
"gift",
"gift bag",
"gift box",
"gift wrapping",
"gigachad (meme)",
"gigantic breasts",
"gigantic penis",
"giggling",
"gign",
"gills",
"ginkgo leaf",
"ginkgo tree",
"girl on top",
"girl sandwich",
"giving",
"gladiator sandals",
"gladius",
"glaive (polearm)",
"glands of montgomery",
"glansjob",
"glaring",
"glasgow smile",
"glass",
"glass door",
"glass floor",
"glass slipper",
"glass teacup",
"glass teapot",
"glasses",
"glasses case",
"glassware",
"glenngarry cap",
"glint",
"glitch",
"globe",
"glock",
"gloom (expression)",
"glory hole",
"glove biting",
"glove bow",
"gloved handjob",
"gloves",
"glowing",
"glowing armor",
"glowing butterfly",
"glowing earrings",
"glowing eye",
"glowing eyes",
"glowing hair",
"glowing hand",
"glowing hot",
"glowing mouth",
"glowing sword",
"glowing tattoo",
"glowing weapon",
"glowstick",
"glue stick",
"goat",
"goat boy",
"goat ears",
"goat girl",
"goat horns",
"goat tail",
"goatee",
"goblin",
"goggles",
"goggles on head",
"goggles on headwear",
"gold",
"gold armor",
"gold bikini",
"gold bracelet",
"gold choker",
"gold earrings",
"gold hairband",
"gold necklace",
"gold trim",
"golden gun",
"goldfish",
"goldfish print",
"golf",
"golf ball",
"golf club",
"golf course",
"golgi apparatus",
"good end",
"goose",
"gorget",
"gorka",
"gothic",
"gothic architecture",
"gothic lolita",
"gotoh hitori (cosplay)",
"gown",
"gp-25",
"grabbing",
"grabbing another's arm",
"grabbing another's ass",
"grabbing another's breast",
"grabbing another's chin",
"grabbing another's hair",
"grabbing from behind",
"grabbing own arm",
"grabbing own ass",
"grabbing own breast",
"grabbing own thigh",
"gradient",
"gradient background",
"gradient clothes",
"gradient dress",
"gradient eyes",
"gradient hair",
"gradient horns",
"gradient skin",
"gradient sky",
"graduation",
"graffiti",
"grandfather clock",
"grapes",
"graphics card",
"graphite (medium)",
"grass",
"grasslands",
"grate",
"grave",
"graves",
"graveyard",
"great helm",
"great pyramid of giza",
"greatsword",
"greaves",
"greco-roman architecture",
"greco-roman clothes",
"greece",
"greek macedonian flag",
"greek toe",
"green apron",
"green armor",
"green background",
"green bag",
"green belt",
"green bikini",
"green bow",
"green bowtie",
"green bra",
"green bracelet",
"green camisole",
"green capelet",
"green cardigan",
"green cloak",
"green coat",
"green curtains",
"green dress",
"green eyes",
"green eyeshadow",
"green flower",
"green footwear",
"green gemstone",
"green gloves",
"green hair",
"green hairband",
"green headwear",
"green hoodie",
"green horns",
"green jacket",
"green kimono",
"green legwear",
"green lips",
"green mask",
"green nails",
"green neckerchief",
"green necktie",
"green neckwear",
"green pajamas",
"green panties",
"green pants",
"green pantyhose",
"green ribbon",
"green robe",
"green sash",
"green scarf",
"green sclera",
"green scrunchie",
"green shawl",
"green shirt",
"green shorts",
"green skin",
"green skirt",
"green sky",
"green sleeves",
"green socks",
"green sweater",
"green tail",
"green tea",
"green theme",
"green tongue",
"green tunic",
"green vest",
"greenhouse",
"grenade",
"grenade launcher",
"grey apron",
"grey background",
"grey bag",
"grey belt",
"grey border",
"grey bow",
"grey bowtie",
"grey bra",
"grey cape",
"grey capelet",
"grey car",
"grey cardigan",
"grey choker",
"grey cloak",
"grey coat",
"grey dress",
"grey eyes",
"grey eyeshadow",
"grey feathers",
"grey footwear",
"grey fur",
"grey gloves",
"grey hair",
"grey hairband",
"grey headband",
"grey headwear",
"grey hoodie",
"grey horns",
"grey jacket",
"grey kimono",
"grey legwear",
"grey leotard",
"grey lips",
"grey mask",
"grey nails",
"grey neckerchief",
"grey necktie",
"grey pajamas",
"grey panties",
"grey pants",
"grey pantyhose",
"grey pupils",
"grey ribbon",
"grey rose",
"grey sailor collar",
"grey scales",
"grey scarf",
"grey sclera",
"grey shirt",
"grey shorts",
"grey skin",
"grey skirt",
"grey sky",
"grey socks",
"grey sports bra",
"grey suit",
"grey sweater",
"grey sweater vest",
"grey tank top",
"grey theme",
"grey thighhighs",
"grey vest",
"grey-framed eyewear",
"greyscale",
"greyscale with colored background",
"griffin & kryuger",
"griffin & kryuger military uniform",
"grimace shake (meme)",
"grimoire",
"grin",
"grinding",
"grip",
"gris swimsuit",
"groceries",
"grocery bag",
"groin",
"groin tendon",
"groping",
"ground vehicle",
"group picture",
"group sex",
"growth",
"grumpy",
"guard rail",
"gucci",
"guided breast grab",
"guided penetration",
"guiding hand",
"guitar",
"guitar case",
"guitar print",
"gulf war",
"gun",
"gun on back",
"gun sling",
"gun to head",
"gusset",
"gyaru",
"gyaru v",
"gyaruo",
"gym",
"gym shirt",
"gym shorts",
"gym storeroom",
"gym uniform",
"h&k g28",
"h&k g3",
"h&k hk416",
"h&k mp5",
"h&k mp5k",
"h&k mp5sd",
"h&k mp7",
"h&k vp9",
"habit",
"hachimaki",
"haikei (le gris no9)",
"haiku",
"hair behind ear",
"hair behind eyewear",
"hair bell",
"hair between breasts",
"hair between eyes",
"hair between horns",
"hair bobbles",
"hair bow",
"hair brush",
"hair bun",
"hair censor",
"hair dryer",
"hair dye",
"hair flaps",
"hair flip",
"hair flower",
"hair flowing over",
"hair focus",
"hair in food",
"hair in own mouth",
"hair intakes",
"hair lift",
"hair on horn",
"hair ornament",
"hair over breasts",
"hair over crotch",
"hair over eyes",
"hair over face",
"hair over one eye",
"hair over shoulder",
"hair pulled back",
"hair ribbon",
"hair rings",
"hair rollers",
"hair scrunchie",
"hair slicked back",
"hair spread out",
"hair stick",
"hair straightener",
"hair strand",
"hair tie",
"hair tie in mouth",
"hair tubes",
"hair tucking",
"hair twirling",
"hair up",
"hairband",
"hairclip",
"hairdressing",
"hairpin",
"hairpods",
"hairy",
"hakama",
"hakama short skirt",
"hakama shorts",
"hakama skirt",
"halberd",
"half gloves",
"half lotus position",
"half mask",
"half updo",
"half-closed eye",
"half-closed eyes",
"half-erect",
"half-harpy",
"half-split chopsticks",
"half-swording",
"half-timbered",
"half-track",
"halftone",
"halftone background",
"halloween",
"halloween bucket",
"halloween costume",
"hallway",
"halo",
"halo behind head",
"halter dress",
"halter shirt",
"halterneck",
"ham",
"hamburger steak",
"hammer",
"hammer and sickle",
"hamsa",
"hanamaru",
"hanbok",
"hand around waist",
"hand between legs",
"hand eye",
"hand fan",
"hand focus",
"hand gesture",
"hand grab",
"hand grip",
"hand in another's hair",
"hand in another's panties",
"hand in another's pocket",
"hand in own hair",
"hand in panties",
"hand in pocket",
"hand mirror",
"hand on animal",
"hand on another's arm",
"hand on another's back",
"hand on another's cheek",
"hand on another's chest",
"hand on another's chin",
"hand on another's face",
"hand on another's head",
"hand on another's knee",
"hand on another's leg",
"hand on another's neck",
"hand on another's shoulder",
"hand on another's thigh",
"hand on another's waist",
"hand on back",
"hand on belt",
"hand on eyewear",
"hand on floor",
"hand on forehead",
"hand on glass",
"hand on hand",
"hand on handle",
"hand on headphones",
"hand on headwear",
"hand on hilt",
"hand on lap",
"hand on own arm",
"hand on own ass",
"hand on own cheek",
"hand on own chest",
"hand on own chin",
"hand on own crotch",
"hand on own ear",
"hand on own elbow",
"hand on own face",
"hand on own foot",
"hand on own head",
"hand on own hip",
"hand on own knee",
"hand on own leg",
"hand on own shoulder",
"hand on own stomach",
"hand on own thigh",
"hand on railing",
"hand on table",
"hand on wall",
"hand on weapon",
"hand over face",
"hand over own mouth",
"hand rest",
"hand sonic",
"hand tattoo",
"hand to own mouth",
"hand under clothes",
"hand under shirt",
"hand up",
"hand wraps",
"handbag",
"handcuffs",
"handgun",
"handheld fan",
"handheld game console",
"handjob",
"handjob gesture",
"handle",
"handprint",
"handrail",
"hands",
"hands in another's armpits",
"hands in hair",
"hands in opposite sleeves",
"hands in pocket",
"hands in pockets",
"hands on another's arm",
"hands on another's cheeks",
"hands on another's face",
"hands on another's hips",
"hands on another's knees",
"hands on another's leg",
"hands on another's shoulders",
"hands on another's thighs",
"hands on ass",
"hands on feet",
"hands on floor",
"hands on ground",
"hands on headwear",
"hands on hilt",
"hands on lap",
"hands on own ass",
"hands on own cheeks",
"hands on own chest",
"hands on own chin",
"hands on own face",
"hands on own head",
"hands on own hips",
"hands on own knees",
"hands on own leg",
"hands on own legs",
"hands on own stomach",
"hands on shoulder",
"hands on table",
"hands up",
"handsfree ejaculation",
"handsfree paizuri",
"handshake",
"handsome squidward (meme)",
"hanfu",
"hangar",
"hanging",
"hanging breasts",
"hanging flower",
"hanging food",
"hanging lantern",
"hanging light",
"hanging plant",
"haori",
"happy",
"happy birthday",
"happy new year",
"happy sex",
"happy tears",
"happy valentine",
"harajuku fashion",
"hard drive",
"hard hat",
"hardboiled egg",
"hardpoint",
"harem",
"harness",
"harp",
"harpoon",
"harpoon gun",
"harpy",
"hat",
"hat bow",
"hat feather",
"hat flower",
"hat loss",
"hat ornament",
"hat over eyes",
"hat ribbon",
"hat with ears",
"hatch",
"hatching (texture)",
"hatsumoude",
"hauberk",
"haunting",
"have to pee",
"hay",
"hayapi (sinsin08051)",
"hazmat suit",
"head back",
"head backwards",
"head between breasts",
"head bump",
"head chain",
"head fins",
"head grab",
"head on another's shoulder",
"head on hand",
"head on pillow",
"head on table",
"head only",
"head out of frame",
"head rest",
"head scarf",
"head steam",
"head tilt",
"head wings",
"head wreath",
"head-mounted display",
"headband",
"headboard",
"headdress",
"headdress removed",
"headgear",
"headless",
"headlight",
"headpat",
"headphones",
"headphones around neck",
"headphones removed",
"headpiece",
"heads together",
"headset",
"headwear pull",
"headwear writing",
"health bar",
"heart",
"heart ahoge",
"heart background",
"heart balloon",
"heart brooch",
"heart button",
"heart censor",
"heart choker",
"heart earrings",
"heart facial mark",
"heart hair ornament",
"heart hands",
"heart hands duo",
"heart hands failure",
"heart in eye",
"heart in mouth",
"heart necklace",
"heart o-ring",
"heart of string",
"heart pasties",
"heart print",
"heart ribbon",
"heart tail",
"heart tattoo",
"heart-shaped bag",
"heart-shaped box",
"heart-shaped buckle",
"heart-shaped chocolate",
"heart-shaped eyes",
"heart-shaped gem",
"heart-shaped ornament",
"heart-shaped pillow",
"heart-shaped pupils",
"heartbeat",
"heater",
"heattech leotard",
"heaven",
"heavy breathing",
"heavy machine gun",
"heavy metal",
"heckler & koch",
"heel pop",
"heel up",
"heel-less legwear",
"height",
"height chart",
"height difference",
"height mark",
"helicopter",
"helm",
"helmet",
"hemokinesis",
"heraldry",
"herb bundle",
"hercules (constellation)",
"hermit crab",
"hetero",
"heterochromia",
"hexagram",
"hey friend listen (meme)",
"hibiscus",
"hickey",
"hiding",
"hiding behind another",
"high collar",
"high five",
"high heel boots",
"high heels",
"high ponytail",
"high tops",
"high-visibility jacket",
"high-visibility vest",
"high-waist dress",
"high-waist pants",
"high-waist shorts",
"high-waist skirt",
"highleg",
"highleg bikini",
"highleg leotard",
"highleg panties",
"highleg swimsuit",
"highway",
"hijab",
"hikimayu",
"hikyou takarasou",
"hill",
"hime cut",
"himejoshi",
"hip bones",
"hip flask",
"hip focus",
"hip vent",
"hiroshima",
"historical american flag",
"historical event",
"hitachi magic wand",
"hitodama",
"hitting",
"hms laforey (1913)",
"hms warspite (badge)",
"hms warspite (s103)",
"hobble",
"hoe",
"hogtie",
"hogwarts school uniform",
"holding",
"holding animal",
"holding another's arm",
"holding another's leg",
"holding another's wrist",
"holding arrow",
"holding axe",
"holding baby",
"holding bag",
"holding ball",
"holding baseball bat",
"holding basket",
"holding binoculars",
"holding bird",
"holding blanket",
"holding book",
"holding bottle",
"holding bouquet",
"holding bow (music)",
"holding bow (weapon)",
"holding bowl",
"holding box",
"holding boxcutter",
"holding bra",
"holding branch",
"holding briefcase",
"holding broom",
"holding brush",
"holding camera",
"holding can",
"holding candle",
"holding candy",
"holding cane",
"holding card",
"holding carrot",
"holding carton",
"holding cat",
"holding chain",
"holding chocolate",
"holding chopsticks",
"holding cigarette",
"holding cigarette pack",
"holding clipboard",
"holding clothes",
"holding clothes hanger",
"holding clothes iron",
"holding clover",
"holding club",
"holding coin",
"holding condom",
"holding controller",
"holding crossbow",
"holding cup",
"holding dagger",
"holding dog",
"holding doll",
"holding dress",
"holding drink",
"holding duster",
"holding egg",
"holding envelope",
"holding fan",
"holding feather",
"holding fireworks",
"holding fish",
"holding flag",
"holding flail",
"holding flower",
"holding food",
"holding footwear",
"holding fork",
"holding fruit",
"holding game controller",
"holding gift",
"holding glowstick",
"holding goggles",
"holding golf ball",
"holding golf club",
"holding grenade",
"holding gun",
"holding hair",
"holding hair dryer",
"holding hair tie",
"holding hammer",
"holding hand grip",
"holding handcuffs",
"holding handheld game console",
"holding hands",
"holding hands is lewd",
"holding hat",
"holding head",
"holding heart",
"holding helmet",
"holding hoe",
"holding hose",
"holding ice cream",
"holding instrument",
"holding jacket",
"holding jar",
"holding key",
"holding knife",
"holding ladle",
"holding lamp",
"holding lantern",
"holding leaf",
"holding leash",
"holding leg",
"holding legs",
"holding letter",
"holding lighter",
"holding lipstick tube",
"holding lollipop",
"holding luggage",
"holding mace",
"holding manga",
"holding map",
"holding marker",
"holding mask",
"holding menu",
"holding microphone",
"holding mirror",
"holding money",
"holding mop",
"holding mushroom",
"holding nail",
"holding notebook",
"holding notepad",
"holding own arm",
"holding own wrist",
"holding paddle",
"holding paintbrush",
"holding panties",
"holding paper",
"holding pen",
"holding pencil",
"holding person",
"holding petal",
"holding phone",
"holding photo",
"holding pillow",
"holding pitchfork",
"holding pizza",
"holding plate",
"holding plunger",
"holding pocket watch",
"holding pokemon",
"holding polearm",
"holding popsicle",
"holding quill",
"holding rabbit",
"holding racket",
"holding rattle",
"holding razor",
"holding reins",
"holding removed eyewear",
"holding ribbon",
"holding rocket launcher",
"holding sack",
"holding saucer",
"holding scalpel",
"holding scanner",
"holding scepter",
"holding scissors",
"holding scroll",
"holding scythe",
"holding sex toy",
"holding sheath",
"holding shield",
"holding shoes",
"holding shovel",
"holding sign",
"holding skateboard",
"holding sketchbook",
"holding skewer",
"holding skull",
"holding smoking pipe",
"holding spatula",
"holding spoon",
"holding staff",
"holding stick",
"holding strap",
"holding stuffed toy",
"holding stylus",
"holding suitcase",
"holding swimsuit",
"holding sword",
"holding syringe",
"holding teapot",
"holding tennis racket",
"holding test tube",
"holding torch",
"holding towel",
"holding toy",
"holding tray",
"holding umbrella",
"holding underwear",
"holding vase",
"holding vegetable",
"holding violin",
"holding wand",
"holding water",
"holding weapon",
"holding whip",
"hole",
"hole in face",
"holed coin",
"hollow body",
"hollow eyes",
"hollowed legwear",
"holly",
"holly hair ornament",
"holographic clothing",
"holster",
"holstered",
"holy roman empire",
"homeless",
"homework",
"honey",
"honeycomb (pattern)",
"honeycomb background",
"hood",
"hood down",
"hood up",
"hooded cape",
"hooded capelet",
"hooded cloak",
"hooded coat",
"hooded jacket",
"hooded robe",
"hooded sweater",
"hoodie",
"hoodie hiding shorts",
"hoodie lift",
"hook",
"hoop",
"hoop earrings",
"hoop piercing",
"hoop skirt",
"hooves",
"horizon",
"horizontal pupils",
"horn (instrument)",
"horn cover",
"horn grab",
"horn ornament",
"horn ring",
"horned helmet",
"horns",
"horrified",
"horror (theme)",
"horse",
"horse boy",
"horse ears",
"horse girl",
"horse penis",
"horse print",
"horse tail",
"horseback riding",
"horseshoe",
"horseshoe crab",
"hose",
"hoshimachi suisei (cosplay)",
"hospital",
"hospital bed",
"hospital gown",
"hot",
"hotpot",
"houndstooth",
"hourglass",
"house",
"housewife",
"how to",
"howa type 20",
"howa type 89",
"howitzer",
"hug",
"hug from behind",
"huge ahoge",
"huge ass",
"huge breasts",
"huge dildo",
"huge horns",
"huge nipples",
"huge penis",
"huge testicles",
"huge weapon",
"hugging book",
"hugging doll",
"hugging object",
"hugging own legs",
"hugging own tail",
"hugging tail",
"hula hoop",
"human furniture",
"human head",
"human tower",
"humanization",
"humanoid robot",
"humiliation",
"humpback whale",
"hunched over",
"husband and wife",
"hut",
"hybrid sight",
"hydrangea",
"hypnosis",
"hypnotizing viewer",
"i am a surgeon (meme)",
"i have no tits (shirt)",
"i showed you my dick please respond (meme)",
"iahfy",
"ice",
"ice cream",
"ice cream cone",
"ice cream cone spill",
"ice cream float",
"ice cube",
"ice skates",
"ice skating",
"iced coffee",
"iced latte with breast milk (meme)",
"ichimegasa",
"icon (computing)",
"id card",
"identity censor",
"idol",
"if they mated",
"ikea shark",
"ikura (food)",
"image macro (meme)",
"imagining",
"imitating",
"immersed",
"imminent anal",
"imminent cunnilingus",
"imminent death",
"imminent fellatio",
"imminent fingering",
"imminent kiss",
"imminent netorare",
"imminent penetration",
"imminent rape",
"imminent vaginal",
"immobilization",
"impact (font)",
"impaled",
"imperial german flag",
"imperial japanese navy",
"imperium of man",
"implied after sex",
"implied ass grab",
"implied cannibalism",
"implied cheating (relationship)",
"implied fellatio",
"implied futanari",
"implied handjob",
"implied incest",
"implied kiss",
"implied murder",
"implied orgasm",
"implied penetration",
"implied prostitution",
"implied sex",
"implied yuri",
"impossible architecture",
"impossible clothes",
"impossible dress",
"impossible shirt",
"impregnation",
"impressionism",
"improvised gag",
"in box",
"in container",
"in crane game",
"in heat",
"in mouth",
"in the face",
"in the walls (meme)",
"in water",
"in-ear earphones",
"ina's back (meme)",
"incense",
"incense burner",
"incest",
"incoming attack",
"incoming food",
"incoming gift",
"incoming hug",
"incoming letter",
"inconvenient ass",
"inconvenient breasts",
"index finger raised",
"index fingers together",
"indian",
"indian style",
"indirect kiss",
"indonesia",
"indonesian army",
"indoors",
"industrial",
"industrial piercing",
"industrial pipe",
"infirmary",
"initial",
"injury",
"ink",
"ink bottle",
"inline skates",
"inn",
"inner ego",
"innertube",
"innie navel",
"insect hair ornament",
"insect wings",
"insecticide",
"insemination",
"inset",
"inset border",
"inside-out",
"insignia",
"insomnia",
"instagram logo",
"instagram username",
"instant loss",
"instant soba",
"instrument",
"instrument case",
"instrument on back",
"intercom",
"interior",
"interlocked fingers",
"internal cumshot",
"internet",
"interracial",
"interrogation",
"interspecies",
"intertwined tails",
"interview",
"intravenous drip",
"introduction",
"inugami-ke no ichizoku pose",
"inverted colors",
"inverted cross",
"inverted nipples",
"inverted pentagram",
"invisible",
"invisible chair",
"invisible floor",
"ipad",
"iphone",
"iphone 12",
"iphone 13",
"ips cells",
"ireland",
"iridescent",
"irish army",
"iron blood (emblem)",
"iron cross",
"ironing",
"ironing board",
"ironwork",
"irrumatio",
"irs",
"isekai truck",
"island",
"isometric",
"isopod",
"it's morbin' time (meme)",
"itabag",
"italian army",
"italian flag",
"italy",
"iv stand",
"iwi tavor",
"j-20",
"j-31",
"j.k.",
"jack daniel's",
"jack-o' challenge",
"jack-o'-lantern",
"jack-o'-lantern hair ornament",
"jackal ears",
"jacket",
"jacket around waist",
"jacket on shoulders",
"jacket over shoulder",
"jacket partially removed",
"jacket pull",
"jacques de molay (foreigner) (fate) (cosplay)",
"jacques de molay (foreigner) (third ascension) (fate) (cosplay)",
"jagariko",
"jaggy lines",
"jam",
"january",
"japan",
"japan ground self-defense force",
"japan self-defense force",
"japanese armor",
"japanese clothes",
"jar",
"javelin (spear)",
"jealous",
"jeans",
"jeep (company)",
"jeep wrangler",
"jellyfish",
"jersey",
"jersey maid",
"jet",
"jewel butt plug",
"jewelry",
"jiangshi",
"jimiko",
"jingasa",
"jingle bell",
"jinyiwei",
"jirai kei",
"jitome",
"jocelin carmes",
"joints",
"jojo pose",
"joker (dc) (cosplay)",
"jolly roger",
"joseon dynasty",
"josou seme",
"journey in the auspicious snow (girls' frontline)",
"joy-con",
"judge",
"juice",
"juice box",
"juliana (pokemon) (cosplay)",
"juliet sleeves",
"jumping",
"jumpsuit",
"jumpsuit around waist",
"june",
"jungle gym",
"just as planned (meme)",
"just shoes",
"just the tip",
"ka-52",
"kabedon",
"kabedon on viewer",
"kabuto (helmet)",
"kac sr-15",
"kaga (aircraft carrier)",
"kaisendon",
"kaitan",
"kalashnikov rifle",
"kamehameha (dragon ball)",
"kaneda shoutarou's bike",
"kanji",
"kanzashi",
"karaoke",
"karin (blue archive) (cosplay)",
"katana",
"katsudon (food)",
"katsushika hokusai (1760)",
"kebab",
"keffiyeh",
"kelp",
"kemonomimi mode",
"kendo",
"kendo mask",
"kepi",
"kerchief",
"ketchup",
"ketchup bottle",
"kettenkrad",
"kettle",
"kettle helm",
"key",
"key necklace",
"keyboard (computer)",
"keyboard (instrument)",
"keychain",
"keyhole",
"keyring",
"khakis",
"kicking",
"kikouken",
"kimono",
"kimono around waist",
"kimono pull",
"kindergarten teacher",
"kindergarten uniform",
"kiseru",
"kiss",
"kiss day",
"kissing animal",
"kissing cheek",
"kissing forehead",
"kissing neck",
"kissing penis",
"kitagawa marin (cosplay)",
"kitchen",
"kitchen knife",
"kite shield",
"kitsune",
"kneading",
"knee boots",
"knee pads",
"knee to chest",
"knee up",
"kneehighs",
"kneeling",
"kneepits",
"knees",
"knees apart feet together",
"knees to chest",
"knees together feet apart",
"knees up",
"knife",
"knife block",
"knife in mouth",
"knight",
"knights templar",
"knit hat",
"knitting",
"knitting needle",
"knocking",
"knolling",
"knotted penis",
"kobold",
"kogal",
"koi",
"koito yuu (cosplay)",
"kongou (battleship)",
"kooribata",
"korean armor",
"korean clothes",
"korean fire noodles",
"korean text",
"korsehut",
"kotatsu",
"kote",
"kotwica (symbol)",
"koyuki (kotatsu358)",
"kraken",
"kremlin",
"kriegsmarine",
"kubrick stare",
"kurokote",
"kusazuri",
"kyuubi",
"kyuudou",
"la chancla",
"lab coat",
"laboratory",
"labret piercing",
"lace",
"lace bra",
"lace choker",
"lace gloves",
"lace panties",
"lace shirt",
"lace trim",
"lace-trimmed apron",
"lace-trimmed bra",
"lace-trimmed collar",
"lace-trimmed dress",
"lace-trimmed garter belt",
"lace-trimmed hairband",
"lace-trimmed legwear",
"lace-trimmed panties",
"lace-trimmed skirt",
"lace-trimmed sleeves",
"lace-trimmed thighhighs",
"lace-up boots",
"lactation",
"lactation through clothes",
"ladder",
"ladle",
"ladybug",
"ladybug girl",
"lake",
"lamb",
"lamellar armor",
"lamia",
"lamp",
"lamppost",
"lampshade",
"lance",
"landing",
"landing gear",
"landscape",
"landship",
"landsknecht",
"lane line",
"lantern",
"lantern festival",
"lanyard",
"lap pillow",
"lap pov",
"lapels",
"laptop",
"large areolae",
"large bow",
"large breasts",
"large ears",
"large hat",
"large insertion",
"large penis",
"large tail",
"large testicles",
"laser",
"laser pointer projection",
"laser sight",
"latex",
"latex bodysuit",
"latex gloves",
"latex legwear",
"latex thighhighs",
"latin cross",
"latin text",
"latte art",
"laughing",
"laundromat",
"laundry basket",
"laurel crown",
"lava",
"lava cake",
"lavender (flower)",
"lay's (potato chips)",
"layered armor",
"layered bikini",
"layered clothes",
"layered dress",
"layered kimono",
"layered shirt",
"layered skirt",
"layered sleeves",
"lazy",
"lead pipe",
"leaf",
"leaf hair ornament",
"leaf hat ornament",
"leaf on head",
"leaf print",
"leaning",
"leaning against vehicle",
"leaning back",
"leaning forward",
"leaning on object",
"leaning on person",
"leaning on table",
"leaning on weapon",
"leaning to the side",
"leash",
"leather",
"leather armor",
"leather belt",
"leather boots",
"leather gloves",
"leather jacket",
"leather pants",
"leather skirt",
"leather strap",
"leather vest",
"lee-enfield",
"left-hand drive",
"left-handed",
"left-to-right manga",
"leg armor",
"leg belt",
"leg between thighs",
"leg cast",
"leg grab",
"leg hair",
"leg hold",
"leg holster",
"leg lift",
"leg lock",
"leg ribbon",
"leg tattoo",
"leg up",
"leg warmers",
"leg wrap",
"leggings",
"leggings pull",
"legionnaire",
"lego",
"lego minifig",
"legs",
"legs apart",
"legs folded",
"legs on table",
"legs together",
"legs up",
"legwear cutout",
"legwear garter",
"lemon",
"lemon earrings",
"lemon print",
"lemon slice",
"lenny face",
"lens",
"lens eye",
"lens flare",
"leopard 2",
"leopard gecko",
"leopard print",
"leotard",
"leotard aside",
"leotard peek",
"leotard under clothes",
"lesbian flag",
"let him cook (meme)",
"letter",
"letterboxed",
"letterman jacket",
"lettuce",
"leucochloridium paradoxum",
"lever action",
"levitation",
"lgbt pride",
"library",
"license plate",
"licking",
"licking another's cheek",
"licking another's face",
"licking armpit",
"licking blade",
"licking blood",
"licking breast",
"licking ear",
"licking finger",
"licking lips",
"licking nipple",
"licking panties",
"licking penis",
"licking testicle",
"licking weapon",
"lifeboat",
"lifebuoy",
"lifted by another",
"lifted by self",
"lifted by tail",
"lifting person",
"lifting vehicle",
"light",
"light areolae",
"light blue background",
"light blue hair",
"light blush",
"light brown background",
"light brown hair",
"light frown",
"light machine gun",
"light particles",
"light purple hair",
"light rays",
"light smile",
"light switch",
"lighter",
"lighthouse",
"lighting cigarette",
"lightning",
"lightning bolt symbol",
"lights",
"ligne claire",
"like and retweet",
"lilac",
"lily (flower)",
"lily of the valley",
"lily pad",
"lily print",
"limited palette",
"linea alba",
"linear hatching",
"lineup",
"lingerie",
"linked collar",
"linked piercing",
"lion ears",
"lion print",
"lion tail",
"lionel messi (cosplay)",
"lip piercing",
"lipgloss",
"lips",
"lipstick",
"lipstick mark",
"lipstick mark on ass",
"lipstick mark on pussy",
"lipstick ring",
"lipstick tube",
"liquid",
"liquid hair",
"liquid silver",
"liquid weapon",
"list",
"listening to music",
"little red riding hood (grimm) (cosplay)",
"live hood",
"livestream",
"living armor",
"living clothes",
"living hair",
"living plush",
"living room",
"living shadow",
"lizard",
"lizard tail",
"lizardman",
"lk (lunar lander)",
"llama",
"load bearing equipment",
"load bearing vest",
"loaded interior",
"loading screen",
"loaf of bread",
"loafers",
"lobster",
"lock",
"lock earrings",
"locked arms",
"locker",
"locker room",
"lockheed have blue",
"locking",
"locomotive",
"log",
"logo",
"logo parody",
"loincloth",
"lolita fashion",
"lolita hairband",
"lollipop",
"london",
"lonely",
"long arms",
"long bangs",
"long beard",
"long braid",
"long coat",
"long dress",
"long eyebrows",
"long eyelashes",
"long fingernails",
"long fingers",
"long hair",
"long hair between eyes",
"long hoodie",
"long labia",
"long legs",
"long neck",
"long pointy ears",
"long scarf",
"long shirt",
"long skirt",
"long sleeves",
"long sword",
"long tail",
"long toenails",
"long toes",
"long tongue",
"longcat (meme)",
"looking afar",
"looking ahead",
"looking at animal",
"looking at another",
"looking at breasts",
"looking at food",
"looking at mirror",
"looking at object",
"looking at penis",
"looking at phone",
"looking at screen",
"looking at viewer",
"looking away",
"looking back",
"looking down",
"looking outside",
"looking over eyewear",
"looking to the side",
"looking up",
"loose bowtie",
"loose clothes",
"loose hair strand",
"loose necktie",
"loose shirt",
"loose socks",
"los angeles county sheriff's department",
"los angeles police department",
"lotion",
"lotion bottle",
"lotus leaf",
"louis vuitton (brand)",
"lounge chair",
"loungewear",
"love ball",
"love hotel",
"love letter",
"lovebird",
"low ponytail",
"low tied hair",
"low twin braids",
"low twintails",
"low wings",
"low-tied long hair",
"lowe (tank)",
"lower body",
"lower teeth only",
"lowered eyelids",
"lowleg",
"lowleg bikini",
"lowleg panties",
"lr-300",
"lube",
"lucerne hammer",
"luftwaffe",
"luger p08",
"luggage",
"lunar surface",
"lunch",
"lunchbox",
"lying",
"lying on person",
"lynus",
"lyrics",
"m legs",
"m1 abrams",
"m1 carbine",
"m1 garand",
"m1 helmet",
"m142 himars",
"m16",
"m16a1",
"m16a2",
"m16a4",
"m1903 springfield",
"m1911",
"m1918 bar",
"m203",
"m4 carbine",
"m4 sherman",
"m4 sopmod ii",
"m43 field cap",
"m5 stuart",
"m8 greyhound",
"m87 black hole",
"maboroshi no ginzuishou",
"macaron",
"macaron background",
"mace",
"machine",
"machine gun",
"machinery",
"maebari",
"mafia",
"magatama",
"magatama necklace",
"magazine (object)",
"magazine (weapon)",
"mage staff",
"magic",
"magic circle",
"magical boy",
"magical girl",
"magnet",
"magpul fmg-9",
"maid",
"maid apron",
"maid bikini",
"maid cafe",
"maid day",
"maid headdress",
"maintenance",
"makeup",
"male focus",
"male hand",
"male masturbation",
"male playboy bunny",
"male pubic hair",
"male underwear",
"mallet",
"malyuk",
"mamerakkkkko",
"manboobs",
"mandarin collar",
"mandarin duck",
"mandarin orange",
"mandragora",
"mandrake",
"manga (object)",
"manhole cover",
"manly",
"mannequin",
"mannlicher m1886",
"mantelpiece",
"map",
"map background",
"maple leaf",
"maracas",
"marble (stone)",
"march",
"margherita pizza",
"mari (blue archive) (cosplay)",
"mariachi",
"marker",
"marker (dead space)",
"market",
"marriage certificate (object)",
"marriage proposal",
"mars symbol",
"marshall amplification",
"marshmallow",
"martini-henry",
"maruchan midori no tanuki tensoba",
"mary janes",
"mas-36",
"mascara",
"mask",
"mask around neck",
"mask bikini",
"mask lift",
"mask on head",
"mask pull",
"masked",
"mast",
"mastercard",
"masturbation",
"matchbox",
"matches",
"matching outfits",
"matchlock",
"materia",
"math",
"mating press",
"mature female",
"mature male",
"maus (tank)",
"mauser 98",
"mauser c96",
"may",
"maz-537",
"mazda rx-7",
"mazda rx-7 fc",
"meadow",
"meal",
"measurements",
"measuring",
"meat",
"mecha",
"mecha musume",
"mechanic",
"mechanical arms",
"mechanical ears",
"mechanical eye",
"mechanical hands",
"mechanical horns",
"mechanical legs",
"mechanical parts",
"mechanical pencil",
"mechanical spine",
"mechanical tail",
"mechanical wings",
"mechanix wear",
"mechanization",
"medal",
"median furrow",
"medic",
"medical eyepatch",
"medical scrubs",
"medicine",
"medicine cabinet",
"medieval",
"meditation",
"medium bangs",
"medium breasts",
"medium dress",
"medium hair",
"medium skirt",
"medium tank",
"megastructure",
"melodica",
"melon bread",
"melon soda",
"melting",
"melting halo",
"meme",
"meme attire",
"memorial on desk",
"menacing (jojo)",
"menpoo",
"menu",
"menu board",
"merchandise",
"mercury (spacecraft)",
"mermaid",
"merry christmas",
"meruccubus (merunyaa) (cosplay)",
"mess kit",
"messenger bag",
"messy",
"messy hair",
"messy room",
"mesugaki",
"metal",
"metal collar",
"metal detector",
"metal pipe falling (meme)",
"meteor",
"mexican clothes",
"mexican revolution",
"mexico",
"mexico ufo alien bodies hearing (meme)",
"mg08/15",
"mg08/18",
"mg42",
"mi-24",
"micro bikini",
"micro bra",
"micro panties",
"micro shorts",
"microdress",
"microphone",
"microphone stand",
"microskirt",
"microsoft excel",
"microsoft outlook",
"microsoft paint (software)",
"microsoft teams",
"microwave",
"midair",
"middle finger",
"midriff",
"midriff peek",
"mig-21",
"mig-29",
"mig-31",
"miko",
"milestone celebration",
"military",
"military coat",
"military hat",
"military jacket",
"military operator",
"military police",
"military rank insignia",
"military truck",
"military uniform",
"military vehicle",
"milk",
"milk bottle",
"milk carton",
"milk churn",
"milking handjob",
"milking machine",
"milkor mgl",
"milkshake",
"milky way",
"mimic",
"mimic chest",
"mimikaki",
"mind control",
"mine (weapon)",
"minecraft pickaxe",
"mini balloon",
"mini crown",
"mini dragon",
"mini hat",
"mini person",
"mini top hat",
"mini wings",
"minigirl",
"minigun",
"miniskirt",
"minnie mouse ears",
"miqo'te",
"mirror",
"mismatched earrings",
"mismatched eyebrows",
"mismatched gloves",
"mismatched legwear",
"mismatched pubic hair",
"mismatched sclera",
"mismatched socks",
"miso soup",
"missile",
"missile pod",
"missionary",
"misunderstanding",
"mitarashi dango",
"mitochondria",
"mitosis",
"mitsubishi motors",
"mitsubishi pajero",
"mitsudomoe (shape)",
"mitsudoue",
"mittens",
"mixed maids",
"mixed media",
"mixed-language text",
"mixed-sex bathing",
"mk 18 carbine",
"mk 22 pistol",
"mk2 grenade",
"mmf threesome",
"mmm threesome",
"moaning",
"moat",
"mob cap",
"mobile suit",
"mochi",
"mochi trail",
"model ship",
"modeling",
"moe moe kyun!",
"moe2021",
"moe2022",
"mohawk",
"mojo",
"moka pot",
"mole",
"mole above eye",
"mole above mouth",
"mole on areola",
"mole on arm",
"mole on armpit",
"mole on ass",
"mole on body",
"mole on breast",
"mole on cheek",
"mole on collarbone",
"mole on crotch",
"mole on forehead",
"mole on leg",
"mole on neck",
"mole on pussy",
"mole on stomach",
"mole on thigh",
"mole under each eye",
"mole under eye",
"mole under mouth",
"molestation",
"money",
"mongolian clothes",
"monitor",
"monk",
"monochrome",
"monochrome background",
"monocle",
"monoglove",
"monokubo",
"monolith (object)",
"monster",
"monster boy",
"monster energy",
"monster girl",
"monsterification",
"mont blanc (food)",
"moon",
"moon rabbit",
"moon stick",
"moonlight",
"mop",
"moped",
"morion",
"morning",
"morning glory",
"morning glory print",
"mortar (bowl)",
"mortar (weapon)",
"mortarboard",
"mosaic censoring",
"moscow",
"mosin-nagant",
"mosquito",
"mosquito girl",
"moss",
"mossberg 590",
"moth",
"moth antennae",
"moth girl",
"mother and child",
"mother and daughter",
"mother and son",
"motion blur",
"motion lines",
"motor vehicle",
"motorcycle",
"motorcycle helmet",
"mount fuji",
"mountain",
"mountainous horizon",
"mourning",
"mouse",
"mouse (computer)",
"mouse ears",
"mouse girl",
"mouse tail",
"mousepad (object)",
"mousse (food)",
"mouth drool",
"mouth focus",
"mouth hold",
"mouth mask",
"mouth mirror",
"mouth pull",
"mouth veil",
"movie poster",
"movie poster (object)",
"movie theater",
"mp28",
"mp40",
"mp443",
"mpi-kms-72",
"muffin top",
"mug",
"multi-strapped bikini",
"multi-strapped bikini bottom",
"multi-strapped panties",
"multicolored background",
"multicolored bow",
"multicolored bra",
"multicolored clothes",
"multicolored dress",
"multicolored eyes",
"multicolored footwear",
"multicolored gloves",
"multicolored hair",
"multicolored headwear",
"multicolored hoodie",
"multicolored horns",
"multicolored jacket",
"multicolored legwear",
"multicolored nails",
"multicolored panties",
"multicolored scarf",
"multicolored shirt",
"multicolored skin",
"multicolored tail",
"multicolored thighhighs",
"multilingual",
"multiple belts",
"multiple boys",
"multiple bracelets",
"multiple cats",
"multiple condoms",
"multiple crossover",
"multiple dogs",
"multiple drawing challenge",
"multiple earrings",
"multiple girls",
"multiple hair bows",
"multiple hairpins",
"multiple heads",
"multiple horns",
"multiple legs",
"multiple moles",
"multiple monitors",
"multiple moons",
"multiple others",
"multiple penises",
"multiple persona",
"multiple piercings",
"multiple riders",
"multiple rings",
"multiple scars",
"multiple swords",
"multiple tails",
"multiple views",
"multiple wings",
"mummy costume",
"mumyou-sakanagare",
"muneate",
"muscle cuirass",
"muscular",
"muscular female",
"muscular male",
"museum",
"mushroom",
"mushroom on head",
"music",
"musical note",
"musical note print",
"musket",
"musketeer",
"mustache",
"mutation",
"muted color",
"muzzle",
"muzzle device",
"muzzle flash",
"my little pogchamp (meme)",
"n1 (rocket)",
"nagasaki",
"nagato (battleship)",
"naginata",
"nail",
"nail art",
"nail bat",
"nail clippers",
"nail polish",
"nail polish bottle",
"naked apron",
"naked bandage",
"naked belt",
"naked chocolate",
"naked cloak",
"naked coat",
"naked hoodie",
"naked kimono",
"naked raincoat",
"naked ribbon",
"naked robe",
"naked sheet",
"naked shirt",
"naked sweater",
"naked tabard",
"naked towel",
"name connection",
"name tag",
"nanami touko (cosplay)",
"nape",
"napkin",
"naranja academy school uniform",
"narrow waist",
"narrowed eyes",
"nasa",
"nasa logo",
"nationale volksarmee",
"native american",
"native american clothes",
"nattou",
"nature",
"naughty face",
"naval uniform",
"navel",
"navel cutout",
"navel focus",
"navel piercing",
"navy",
"navy seal copypasta",
"nazar (amulet)",
"nazi",
"nazi flag",
"nebula",
"neck",
"neck bell",
"neck fur",
"neck ribbon",
"neck ring",
"neck ruff",
"neck tassel",
"neck tattoo",
"neckerchief",
"necklace",
"necktie",
"necktie between breasts",
"necktie grab",
"necktie in mouth",
"neckwear grab",
"neco spirit",
"needle",
"negligee",
"nejime",
"nemes",
"nengajou",
"neon lights",
"neon palette",
"neon trim",
"nerd emoji",
"nerv",
"nervous",
"nervous smile",
"nervous sweating",
"net",
"netherlands",
"netorare",
"netorase",
"new year",
"new york city police department",
"news",
"newspaper",
"nib pen (object)",
"night",
"night sky",
"night vision device",
"nightgown",
"nightstand",
"nigirizushi",
"nihonga",
"nihongami",
"nike",
"nikon (company)",
"ninja",
"nintendo 3ds",
"nintendo ds",
"nintendo switch",
"nipple bar",
"nipple bells",
"nipple chain",
"nipple clamps",
"nipple cutout",
"nipple jewelry",
"nipple piercing",
"nipple rings",
"nipple slip",
"nipple stimulation",
"nipple tag",
"nipple tweak",
"nipples",
"nipples pressed together",
"nishizawa",
"nissan",
"nissan 180sx",
"nissan 300zx",
"nissan 300zx (z32)",
"nissan fairlady z",
"nissan s13 silvia",
"nissan silvia",
"nissan skyline",
"nissan skyline gt-r",
"nissan skyline r32",
"nissan skyline r34",
"nissin cup noodle",
"nlaw",
"no ai logo",
"no anus",
"no bra",
"no eyes",
"no feet",
"no headwear",
"no heterochromia",
"no horny (meme)",
"no humans",
"no legwear",
"no mouth",
"no nose",
"no panties",
"no pants",
"no parking sign",
"no pupils",
"no pussy",
"no sclera",
"no shirt",
"no shoes",
"no smoking",
"no socks",
"no stopping sign",
"no symbol",
"no-show socks",
"noh mask",
"non-binary flag",
"non-humanoid robot",
"nontraditional miko",
"nontraditional playboy bunny",
"noodles",
"nori (seaweed)",
"north korean flag",
"northrop tacit blue",
"nose",
"nose art",
"nose blush",
"nose bubble",
"nose grab",
"nose mask",
"nose piercing",
"nose ring",
"nosebleed",
"noses touching",
"nostrils",
"notched ear",
"notched lapels",
"note",
"notebook",
"notepad",
"nothing personnel kid (meme)",
"notice lines",
"notorious b.i.g. (cosplay)",
"notre dame de paris",
"novel cover",
"now draw her giving birth (meme)",
"nozzle",
"nsv",
"nude",
"nude cover",
"nudist",
"number tattoo",
"numbered",
"nun",
"nurse",
"nurse cap",
"nursing handjob",
"nuzzle",
"nva uniform",
"nyan",
"nyantcha (style)",
"o-ring",
"o-ring belt",
"o-ring bikini",
"o-ring choker",
"o-ring dress",
"o-ring thigh strap",
"o-ring top",
"o3o",
"o_o",
"obese",
"obi",
"obiage",
"obidome",
"obijime",
"object head",
"object in pocket",
"object insertion",
"object on head",
"objectification",
"obliques",
"obrez",
"obs studio",
"occult",
"ocean",
"octopus",
"oekaki",
"oerlikon 20mm gun",
"off shoulder",
"off-shoulder dress",
"off-shoulder jacket",
"off-shoulder shirt",
"off-shoulder sweater",
"office",
"office chair",
"office lady",
"official alternate costume",
"official art inset",
"ofuda",
"ofuda between fingers",
"ogre",
"oh-58 kiowa",
"ohisashiburi (style)",
"oi parking area",
"oil",
"oil painting (medium)",
"oil-paper umbrella",
"ojou-sama pose",
"ok sign",
"ok sign over eye",
"okamoto condoms",
"okobo",
"old",
"old man",
"old woman",
"omelet",
"omikuji",
"omurice",
"on back",
"on bed",
"on bench",
"on chair",
"on couch",
"on desk",
"on floor",
"on grass",
"on ground",
"on head",
"on lap",
"on moon",
"on motorcycle",
"on one knee",
"on person",
"on pillow",
"on railing",
"on rock",
"on roof",
"on scooter",
"on shoulder",
"on side",
"on stairs",
"on stomach",
"on table",
"on top of pole",
"on vehicle",
"on water",
"one breast out",
"one eye closed",
"one eye covered",
"one finger selfie challenge (meme)",
"one side up",
"one-eyed",
"one-hour drawing challenge",
"one-piece swimsuit",
"one-piece swimsuit pull",
"one-piece tan",
"one-piece thong",
"onee-loli",
"onee-shota",
"onesie",
"oni",
"oni horns",
"oni mask",
"onigiri",
"onion",
"onmyouji",
"onsen",
"onsen symbol",
"opaque glasses",
"open bag",
"open book",
"open box",
"open bra",
"open can",
"open cardigan",
"open clothes",
"open coat",
"open collar",
"open door",
"open drawer",
"open dress",
"open fly",
"open hand",
"open hands",
"open hatch",
"open hoodie",
"open jacket",
"open kimono",
"open mouth",
"open pajamas",
"open robe",
"open shirt",
"open shorts",
"open sign",
"open vest",
"open window",
"open-chest sweater",
"opened by self",
"opening door",
"opera cake",
"opossum",
"oppai challenge",
"oppai loli",
"optical illusion",
"optical sight",
"oral",
"oral invitation",
"orange (fruit)",
"orange apron",
"orange background",
"orange bag",
"orange bikini",
"orange bow",
"orange bowtie",
"orange cat",
"orange choker",
"orange dress",
"orange eyes",
"orange flower",
"orange footwear",
"orange gloves",
"orange hair",
"orange headwear",
"orange hoodie",
"orange jacket",
"orange moon",
"orange nails",
"orange necktie",
"orange nipples",
"orange panties",
"orange pants",
"orange penis",
"orange print",
"orange pupils",
"orange ribbon",
"orange robe",
"orange scarf",
"orange sclera",
"orange scrunchie",
"orange shirt",
"orange shorts",
"orange skin",
"orange skirt",
"orange sky",
"orange slice",
"orange sports bra",
"orange sweater",
"orange tail",
"orange theme",
"orange thighhighs",
"orange tongue",
"orange tree",
"orange vest",
"orange-shaped earrings",
"orange-tinted eyewear",
"orb",
"orc",
"orgasm",
"orgy",
"origami",
"orion (constellation)",
"ork (warhammer)",
"ornament",
"ornament focus",
"ornate ring",
"orrery",
"otaku",
"otaku room",
"other focus",
"other with female",
"otoko no ko",
"ots-38 stechkin",
"otto von bismarck (cosplay)",
"ottoman empire",
"out of frame",
"out-of-frame censoring",
"outdoors",
"outline",
"outside border",
"outstretched arm",
"outstretched arms",
"outstretched hand",
"outstretched leg",
"outstretched legs",
"oven",
"oven mitts",
"over shoulder",
"over-kneehighs",
"over-rim eyewear",
"overall shorts",
"overalls",
"overcast",
"overexposure",
"overgrown",
"overhead line",
"overpass",
"oversized animal",
"oversized clothes",
"oversized food",
"oversized forearms",
"oversized limbs",
"oversized object",
"oversized shirt",
"oviraptor",
"ovum",
"owl girl",
"own a musket for home defense (meme)",
"own hands clasped",
"own hands together",
"oxygen tank",
"pacifier",
"package",
"padded armor",
"padded jacket",
"padded pants",
"padded vest",
"paddle",
"padlock",
"page number",
"pain",
"paint",
"paint on clothes",
"paint roller",
"paint splatter",
"paint splatter on face",
"paint tube",
"paintbrush",
"paintbrush hair ornament",
"painterly",
"painting (action)",
"painting (medium)",
"painting (object)",
"paisley",
"paizuri",
"paizuri on lap",
"paizuri under clothes",
"pajamas",
"paladin",
"palantir",
"pale color",
"pale skin",
"palette (object)",
"palette knife",
"palm leaf",
"palm tree",
"palms together",
"pancake",
"panda print",
"pandakorya",
"paneled background",
"panicking",
"pant suit",
"pantaloons",
"panther (tank)",
"panties",
"panties around one ankle",
"panties around one leg",
"panties aside",
"panties day",
"panties under pantyhose",
"pantograph",
"pants",
"pants around one leg",
"pants pull",
"pants rolled up",
"pants tucked in",
"pants under dress",
"pants under shorts",
"panty lift",
"panty peek",
"panty pull",
"panty straps",
"pantyhose",
"pantyhose pull",
"pantyhose under shorts",
"pantyhose under trousers",
"pantylines",
"pantyshot",
"panzer i",
"panzer ii",
"panzer iii",
"panzer iv",
"panzerfaust",
"panzerfaust 3",
"paper",
"paper airplane",
"paper bag",
"paper crane",
"paper fan",
"paper lantern",
"paper stack",
"parachute",
"parasite",
"parasol",
"paratrooper",
"parent and child",
"parfait",
"paris",
"park",
"park bench",
"parka",
"parking lot",
"parody",
"parrot",
"parted bangs",
"parted hair",
"parted lips",
"partially blind",
"partially colored",
"partially fingerless gloves",
"partially opaque glasses",
"partially submerged",
"partially unbuttoned",
"partially undressed",
"partially unzipped",
"partially visible vulva",
"partisan",
"party hat",
"party whistle",
"pasgt helmet",
"pasta",
"pastel colors",
"pasties",
"pastry",
"pastry box",
"patch",
"path",
"patreon logo",
"patreon username",
"patriotism",
"patterned",
"patterned background",
"patterned clothing",
"patting back",
"pauldrons",
"pause button",
"pavise",
"paw gloves",
"paw pose",
"paw print",
"paw print background",
"paw print soles",
"paw shoes",
"pawpads",
"pc engine",
"pc-98 (computer)",
"pc-98 (style)",
"peaked cap",
"peanut",
"pearl (gemstone)",
"pearl bracelet",
"pearl earrings",
"pearl necklace",
"pearl thong",
"peas",
"pectorals",
"pedal board",
"pedestrian bridge",
"pedicure",
"pee",
"pee in container",
"peeing",
"peeking",
"peeking out",
"peeking through fingers",
"peephole",
"pelt",
"pelvic curtain",
"pelvic curtain aside",
"pelvic curtain lift",
"pen",
"pen (medium)",
"pen to mouth",
"pencil",
"pencil as mustache",
"pencil case",
"pencil dress",
"pencil sharpener",
"pencil skirt",
"pendant",
"pendant choker",
"pendulum",
"penguin",
"penis",
"penis awe",
"penis biting",
"penis grab",
"penis in pantyhose",
"penis on face",
"penis on tongue",
"penis out",
"penis over eyes",
"penis peek",
"penis size difference",
"penis tentacle",
"penis under another's clothes",
"penis under clothing",
"penises touching",
"penny loafers",
"pennywise in the sewer (meme)",
"pensive",
"pentagram",
"pentagram necklace",
"people",
"people's liberation army",
"pepper (spice)",
"pepper shaker",
"peril",
"perineum",
"perky breasts",
"persimmon",
"personification",
"perspective",
"pervert",
"pestle",
"pet",
"pet bowl",
"petals",
"peterprime",
"petite",
"petticoat",
"petting",
"phallic symbol",
"phimosis",
"phoenix",
"phone",
"phone booth",
"phone with ears",
"photo (medium)",
"photo (object)",
"photo album",
"photo background",
"photo inset",
"photo stand-in",
"photorealistic",
"physics",
"piano",
"piano bench",
"pickelhaube",
"pickle",
"pickup truck",
"picnic",
"picnic basket",
"pico de orizaba",
"picture frame",
"pie",
"piercing",
"pig ears",
"pig print",
"pigeon",
"pigeon-toed",
"piggy bank",
"piggyback",
"pike (weapon)",
"piledriver (sex)",
"pill",
"pillar",
"pillarboxed",
"pillow",
"pillow grab",
"pillow hug",
"pilot",
"pilot helmet",
"pilot suit",
"pilot uniform",
"pilum",
"pimple",
"pinafore dress",
"pinching",
"pinching sleeves",
"pindad ss2",
"pine tree",
"pink apron",
"pink armband",
"pink background",
"pink bag",
"pink belt",
"pink bikini",
"pink blood",
"pink border",
"pink bow",
"pink bowtie",
"pink bra",
"pink camisole",
"pink cardigan",
"pink choker",
"pink clouds",
"pink coat",
"pink collar",
"pink dress",
"pink eyes",
"pink flower",
"pink footwear",
"pink fur",
"pink hair",
"pink hairband",
"pink hakama",
"pink headwear",
"pink hoodie",
"pink jacket",
"pink kimono",
"pink legwear",
"pink lips",
"pink lipstick tube",
"pink mask",
"pink nails",
"pink neckerchief",
"pink necktie",
"pink pajamas",
"pink panties",
"pink pants",
"pink ribbon",
"pink rose",
"pink sailor collar",
"pink scarf",
"pink scrunchie",
"pink shirt",
"pink shorts",
"pink skin",
"pink skirt",
"pink sky",
"pink socks",
"pink suit",
"pink sweater",
"pink sweater vest",
"pink tank top",
"pink theme",
"pink thighhighs",
"pink umbrella",
"pink vest",
"pink-framed eyewear",
"pink-tinted eyewear",
"pinky out",
"pinky swear",
"pinned",
"pinstripe bow",
"pinstripe jacket",
"pinstripe pattern",
"pinstripe suit",
"pinup (style)",
"pipe bomb",
"pipe wrench",
"pirate",
"pirate hat",
"pirate ship",
"piss drawer",
"piston",
"pitcher (container)",
"pitchfork",
"pith helmet",
"pixel art",
"pixelated",
"pixie cut",
"pixiv id",
"pixiv logo",
"pixiv username",
"pizza",
"pizza box",
"pizza slice",
"pkm",
"plague doctor",
"plague doctor mask",
"plaid",
"plaid ascot",
"plaid background",
"plaid bikini",
"plaid bow",
"plaid bowtie",
"plaid dress",
"plaid jacket",
"plaid kimono",
"plaid necktie",
"plaid pants",
"plaid pillow",
"plaid ribbon",
"plaid scarf",
"plaid shirt",
"plaid shorts",
"plaid skirt",
"plaid vest",
"plain",
"planet",
"plank",
"planking",
"plant",
"plant girl",
"plantar flexion",
"planted",
"planted arrow",
"planted axe",
"planted knife",
"planted shield",
"planted spear",
"planted sword",
"planted umbrella",
"planting",
"plap",
"plasma cutter",
"plasma sword",
"plastic bag",
"plastic bottle",
"plastic wrap",
"plate",
"plate armor",
"plate carrier",
"plate stack",
"platform boots",
"platform footwear",
"platinum blonde hair",
"play button",
"playboy bunny",
"playground",
"playing bass",
"playing card",
"playing card print",
"playing card theme",
"playing games",
"playing instrument",
"playing sports",
"playing with own hair",
"playstation controller",
"playstation portable",
"pleading face emoji",
"pleated dress",
"pleated shirt",
"pleated skirt",
"pleated sleeves",
"plug",
"plug (piercing)",
"plugsuit",
"plume",
"plumeria",
"plump",
"plunger",
"plunging neckline",
"pm md 63/65",
"pocket",
"pocket square",
"pocket watch",
"pocky",
"pocky day",
"pocky in mouth",
"pocky kiss",
"podium",
"poem",
"pointer",
"pointing",
"pointing at another",
"pointing at self",
"pointing at viewer",
"pointing down",
"pointing forward",
"pointing up",
"pointless censoring",
"pointless condom",
"pointy breasts",
"pointy ears",
"pointy nose",
"poke ball",
"poke ball (basic)",
"poke ball symbol",
"poke ball theme",
"pokemon (creature)",
"poker chip",
"poking",
"poland",
"polar bear",
"polaroid",
"pole",
"pole dancing",
"polearm",
"polehammer",
"poleyn",
"police",
"police badge",
"police car",
"police hat",
"police uniform",
"policeman",
"policewoman",
"polish army",
"polish flag",
"polish text",
"politics",
"polka dot",
"polka dot background",
"polka dot bow",
"polka dot dress",
"polka dot legwear",
"polka dot pajamas",
"polka dot pants",
"polka dot scrunchie",
"polka dot shirt",
"polo shirt",
"pom pom (cheerleading)",
"pom pom (clothes)",
"pom pom beanie",
"pom pom hair ornament",
"pommel",
"poncho",
"pond",
"pondering my orb (meme)",
"ponytail",
"pool",
"pool ladder",
"pool of blood",
"poolside",
"popcorn",
"popped button",
"popsicle",
"popsicle stick",
"porch",
"pork",
"pornography",
"porsche",
"porsche 911",
"porsche 964",
"portal (object)",
"portrait",
"portrait (object)",
"portuguese text",
"pose",
"possessed",
"possum ears",
"possum girl",
"possum tail",
"post and rail fence",
"post-apocalypse",
"postcard",
"poster (medium)",
"poster (object)",
"potato chips",
"potion",
"potted plant",
"pouch",
"pouncing",
"pouring",
"pout",
"pouty lips",
"pov",
"pov across bed",
"pov across table",
"pov bullying",
"pov cheek warming (meme)",
"pov crotch",
"pov dating",
"pov doorway",
"pov hands",
"pov legs",
"pov peephole",
"pov shadow",
"power armor",
"power bank",
"power cord",
"power lines",
"power suit",
"powering up",
"pp-19-01",
"ppsh-41",
"praetor suit",
"prattkeeping (meme)",
"praying",
"precum",
"precum drip",
"precum string",
"precum through clothes",
"predicament bondage",
"pregnant",
"prehensile tongue",
"presenting",
"presenting foot",
"presenting removed panties",
"price",
"price tag",
"priest",
"priestess",
"princess",
"princess carry",
"pringles can",
"print (medium)",
"print bikini",
"print bra",
"print coat",
"print dress",
"print gloves",
"print hair",
"print headwear",
"print hoodie",
"print jacket",
"print kimono",
"print necktie",
"print robe",
"print sheath",
"print shirt",
"print skirt",
"print socks",
"print thighhighs",
"print vest",
"printer",
"prison",
"prison cell",
"prison clothes",
"procrastination",
"product placement",
"profanity",
"profile",
"profile picture",
"programming (topic)",
"progression",
"projected inset",
"projectile cum",
"prone bone",
"propaganda",
"propeller",
"prostate",
"prostate milking",
"prosthesis",
"prosthetic arm",
"prosthetic hand",
"prosthetic leg",
"prostitution",
"protecting",
"pteruges",
"ptsd",
"pubic hair",
"pubic hair peek",
"pubic stubble",
"pubic tattoo",
"public indecency",
"public masturbation",
"public nudity",
"public service announcement",
"puckered anus",
"puckered lips",
"puddle",
"puff of air",
"puffy cheeks",
"puffy chest",
"puffy lips",
"puffy long sleeves",
"puffy nipples",
"puffy short sleeves",
"puffy sleeves",
"pulled by another",
"pulled by self",
"pulling",
"pulling back",
"pump action",
"pumpkin",
"pumps",
"pun",
"punching",
"punishment",
"punk",
"puppet",
"puppet strings",
"purity seal",
"purple armband",
"purple background",
"purple bikini",
"purple blood",
"purple bodysuit",
"purple bow",
"purple bowtie",
"purple bra",
"purple butterfly",
"purple cardigan",
"purple choker",
"purple collar",
"purple dress",
"purple eyeliner",
"purple eyes",
"purple eyeshadow",
"purple flower",
"purple footwear",
"purple garter belt",
"purple gemstone",
"purple gloves",
"purple hair",
"purple hakama",
"purple headwear",
"purple hoodie",
"purple horns",
"purple jacket",
"purple kimono",
"purple legwear",
"purple leotard",
"purple lips",
"purple liquid",
"purple nails",
"purple neckerchief",
"purple necktie",
"purple one-piece swimsuit",
"purple outline",
"purple pajamas",
"purple panties",
"purple pants",
"purple pantyhose",
"purple pubic hair",
"purple pupils",
"purple ribbon",
"purple robe",
"purple rose",
"purple sash",
"purple scales",
"purple scarf",
"purple scrunchie",
"purple shirt",
"purple shorts",
"purple skin",
"purple skirt",
"purple sky",
"purple socks",
"purple sweater",
"purple tail",
"purple theme",
"purple thighhighs",
"purple tongue",
"purple vest",
"purple-framed eyewear",
"purring",
"pursed lips",
"push-button",
"pussy",
"pussy juice",
"pussy juice drip through clothes",
"pussy juice on fingers",
"pussy juice puddle",
"pussy juice stain",
"pussy juice trail",
"pussy peek",
"pussy piercing",
"puttee",
"putting on gloves",
"putting on shoes",
"puzzle piece hair ornament",
"pyramid (structure)",
"pyramid head (cosplay)",
"qbz-191",
"qinghua (porcelain)",
"qixiong ruqun",
"qr code",
"quad tails",
"quadruplets",
"quarter note",
"queen",
"queen (chess)",
"queen of spades symbol",
"quest",
"queue",
"quick ball",
"quill",
"quintuplets",
"quiver",
"rabbit",
"rabbit boy",
"rabbit ears",
"rabbit girl",
"rabbit hair ornament",
"rabbit hood",
"rabbit mask",
"rabbit pajamas",
"rabbit pose",
"rabbit print",
"rabbit tail",
"raccoon ears",
"raccoon girl",
"race queen",
"race vehicle",
"racecar",
"racetrack",
"racism",
"racket",
"radar",
"radiation symbol",
"radio",
"radio antenna",
"raft",
"raglan sleeves",
"rags",
"railing",
"railroad crossing",
"railroad signal",
"railroad tracks",
"rain",
"rainbow",
"rainbow gradient",
"rainbow hair",
"rainbow text",
"raincoat",
"raised eyebrow",
"raised eyebrows",
"raised fist",
"rally car",
"ram (computer)",
"ramen",
"randoseru",
"ranguage",
"rape",
"rapier",
"rattle",
"ravenclaw",
"ray-ban",
"razor",
"razor blade",
"reaching",
"reaching towards viewer",
"reactive armor",
"reading",
"ready to draw",
"real life insert",
"real world location",
"realistic",
"rear naked choke",
"rear-view mirror",
"rearing",
"rebar",
"receipt",
"reclining",
"recoil",
"record",
"recorder",
"recording",
"rectangular eyewear",
"rectangular mouth",
"rectangular pupils",
"recurring image",
"recursion",
"recycling",
"recycling symbol",
"red apple",
"red apron",
"red armband",
"red armor",
"red ascot",
"red background",
"red bag",
"red bikini",
"red bodysuit",
"red border",
"red bow",
"red bowtie",
"red bra",
"red camisole",
"red cape",
"red capelet",
"red cardigan",
"red carpet",
"red choker",
"red circle",
"red cloak",
"red coat",
"red collar",
"red cross",
"red curtains",
"red dress",
"red eyes",
"red eyeshadow",
"red flag",
"red flower",
"red footwear",
"red fur",
"red garter belt",
"red gemstone",
"red gloves",
"red hair",
"red hairband",
"red hakama",
"red halo",
"red headband",
"red headwear",
"red hood",
"red hoodie",
"red horns",
"red jacket",
"red kimono",
"red legwear",
"red leotard",
"red lips",
"red moon",
"red nails",
"red neckerchief",
"red necktie",
"red oni",
"red outline",
"red pajamas",
"red panda",
"red panda ears",
"red panda girl",
"red panda tail",
"red panties",
"red pants",
"red pupils",
"red ribbon",
"red robe",
"red rope",
"red rose",
"red sailor collar",
"red sash",
"red scales",
"red scarf",
"red sclera",
"red scrunchie",
"red shirt",
"red shorts",
"red skin",
"red skirt",
"red sky",
"red sleeves",
"red socks",
"red sports bra",
"red square",
"red star",
"red stripes",
"red sweater",
"red tail",
"red tassel",
"red theme",
"red thighhighs",
"red track suit",
"red trim",
"red tulip",
"red tunic",
"red umbrella",
"red vest",
"red wine",
"red wings",
"red-crowned crane",
"red-framed eyewear",
"red-tinted eyewear",
"redesign",
"reeds",
"reference inset",
"reference sheet",
"reflection",
"reflective floor",
"reflective surface",
"reflective table",
"reflective water",
"reflex sight",
"refraction",
"refrigerator",
"refrigerator interior",
"refrigerator magnet",
"refueling",
"reichsadler",
"reichstag",
"reins",
"rejected kiss",
"religion",
"reloading",
"remembering",
"remington model 700",
"remote control",
"remote control vibrator",
"removing bra",
"removing eyewear",
"removing jacket",
"removing legwear",
"removing shoes",
"removing sock",
"reptile",
"reptile girl",
"reptilian",
"republic of china army",
"republic of korea army",
"rerebrace",
"respirator",
"rest in peace (phrase)",
"restaurant",
"restrained",
"restraints",
"retort stand",
"retro artstyle",
"revealing clothes",
"reverse cowgirl position",
"reverse grip",
"reverse outfit",
"reverse palettes",
"reverse suspended congress",
"reverse trap",
"reverse upright straddle",
"revolver",
"rewind button",
"rgd-33",
"rgd-5",
"ribbed dress",
"ribbed legwear",
"ribbed panties",
"ribbed shirt",
"ribbed sleeves",
"ribbed socks",
"ribbed sweater",
"ribbed swimsuit",
"ribbed thighhighs",
"ribbon",
"ribbon bar",
"ribbon braid",
"ribbon choker",
"ribbon earrings",
"ribbon in mouth",
"ribbon of saint george",
"ribbon spool",
"ribbon trim",
"ribbon-trimmed legwear",
"ribbon-trimmed sleeves",
"ribbon-trimmed thighhighs",
"ribs",
"rice",
"rice bowl",
"rice cooker",
"rice hat",
"rice paddy",
"riding",
"riding bicycle",
"riding boots",
"riding crop",
"rifle",
"rifle cartridge",
"rifleman's creed",
"right-over-left kimono",
"right-to-left comic",
"rimless eyewear",
"ring",
"ring 411",
"ring bell",
"ring box",
"ring pull",
"ringed eyes",
"ringlets",
"riot shield",
"ripping",
"ripples",
"river",
"rivets",
"rk62",
"road",
"road sign",
"roasted sweet potato",
"robbery",
"robe",
"robot",
"robot animal",
"robot ears",
"robot fish",
"robot girl",
"robot joints",
"rock",
"rock paper scissors",
"rocket",
"rocket launcher",
"rocking chair",
"roe",
"rogatywka",
"rogue",
"role reversal",
"rolleiflex",
"roller coaster",
"roller skates",
"rolling eyes",
"rolling suitcase",
"romaji text",
"roman armor",
"roman clothes",
"roman empire",
"roman numeral",
"romania",
"romanian flag",
"rome (city)",
"ronald mcdonald (cosplay)",
"rondel",
"rooftop",
"room",
"rooster",
"roots",
"rope",
"rosary",
"rose",
"rose bush",
"rose print",
"rosemary (herb)",
"rotor",
"rough sex",
"round eyewear",
"round image",
"round table",
"round teeth",
"round window",
"round-bottom flask",
"roundel",
"route 66",
"royal navy",
"rpd",
"rpg (weapon)",
"rpg-7",
"rpk-16",
"rubber band",
"rubber boots",
"rubber duck",
"rubber duck hair ornament",
"rubber gloves",
"rubbing eyes",
"rubble",
"rudder footwear",
"ruffling hair",
"rug",
"ruins",
"ruler",
"runes",
"running",
"runny makeup",
"runny nose",
"runway",
"rural",
"russia",
"russian air force",
"russian anti-war flag",
"russian army",
"russian empire",
"russian flag",
"russian text",
"russo-ukrainian war",
"rust",
"rv",
"s&w m29",
"s&w m39",
"sabaton",
"saber (weapon)",
"sacabambaspis",
"sack",
"sad",
"sad keanu (meme)",
"sad smile",
"saddle",
"safety glasses",
"safety pin",
"safety pin piercing",
"safety razor",
"sagging breasts",
"sailboat",
"sailor",
"sailor collar",
"sailor dress",
"sailor hat",
"sailor moon (cosplay)",
"sailor senshi uniform",
"sailor shirt",
"saint",
"saizeriya",
"sajkaca",
"sakazuki",
"sake",
"sake bottle",
"sakura no tomoru hi e",
"sakuragaoka high school uniform",
"sakuramon",
"salad",
"salad bowl",
"saliva",
"saliva drip",
"saliva on breasts",
"saliva trail",
"sallet",
"salmon",
"salmon (fish)",
"salt",
"salt shaker",
"salute",
"sam browne belt",
"same-hada",
"same-sex bathing",
"sample watermark",
"samurai",
"sanbou",
"sand",
"sand castle",
"sand sculpture",
"sandals",
"sandbag",
"sandwich",
"sandwiched",
"sangvis ferri",
"sanpaku",
"santa bikini",
"santa costume",
"santa dress",
"santa gloves",
"santa hat",
"santa leotard",
"sapporo beer",
"sarashi",
"sash",
"sashimi",
"satchel",
"satellite",
"satellite dish",
"satin",
"satin bra",
"satin panties",
"satire",
"sattou (style)",
"sauce",
"saucer",
"sauna",
"sausage",
"sausage slice",
"savannah",
"saw",
"saya (scabbard)",
"sbd dauntless",
"scabbard",
"scale armor",
"scales",
"scalpel",
"scanlines",
"scar",
"scar across eye",
"scar on arm",
"scar on cheek",
"scar on chest",
"scar on face",
"scar on forehead",
"scar on hand",
"scar on leg",
"scar on neck",
"scar on nose",
"scar on stomach",
"scare",
"scared",
"scarf",
"scarf grab",
"scarf over mouth",
"scary maze game",
"scene reference",
"scenery",
"scepter",
"school",
"school bag",
"school chair",
"school desk",
"school hat",
"school nurse",
"school swimsuit",
"school uniform",
"science",
"science fiction",
"scientist",
"scissors",
"scolding",
"sconce",
"scooter",
"scope",
"scoreboard",
"scorpion",
"scout trooper",
"scowl",
"scratched",
"scratches",
"screaming",
"screen",
"screen light",
"screencap inset",
"screentones",
"screw",
"screw hair ornament",
"screwdriver",
"scroll",
"scrunchie",
"scuba",
"scuba gear",
"scuba tank",
"sculpting",
"sculpture",
"scylla",
"scythe",
"sea monster",
"sea spray",
"seagull",
"seal (animal)",
"seal script",
"seal team six",
"seamed legwear",
"seams",
"seaplane",
"search bar",
"searchlight",
"seashell",
"seaweed",
"security checkpoint",
"security guard",
"seductive smile",
"see-through",
"see-through bra",
"see-through dress",
"see-through legwear",
"see-through panties",
"see-through shawl",
"see-through shirt",
"see-through silhouette",
"see-through skirt",
"see-through skirt layer",
"see-through sleeves",
"see-through swimsuit",
"sega mega drive",
"seggs (meme)",
"segment display",
"segmented comic",
"seigaiha",
"seiza",
"self bondage",
"self breast sucking",
"self exposure",
"self-harm scar",
"selfie",
"semi truck",
"semi-circular eyewear",
"semi-rimless eyewear",
"sennheiser",
"sepia",
"sepia background",
"sequential",
"serafuku",
"seraph",
"serbia",
"serious",
"server",
"serving",
"sesame seeds",
"sesshouseki",
"seven-segment display",
"sewer grate",
"sewing machine",
"sex",
"sex ed",
"sex from behind",
"sex machine",
"sex toy",
"sex toy pull",
"sexting",
"sexual coaching",
"sexually suggestive",
"shackles",
"shade",
"shaded face",
"shading eyes",
"shadow",
"shadow company",
"shadow puppet",
"shaking",
"shako cap",
"shallow water",
"shampoo bottle",
"shanghai",
"shared artificial vagina",
"shared bathing",
"shared blanket",
"shared drink",
"shared earphones",
"shared umbrella",
"sharing",
"sharing food",
"sharingan",
"shark",
"shark bag",
"shark fin",
"shark girl",
"shark tail",
"sharp fingernails",
"sharp teeth",
"sharp toenails",
"shaved head",
"shaving",
"shaving another",
"shaving cream",
"shaving crotch",
"shawl",
"she versus he thought bubble (meme)",
"shearing",
"shears",
"sheath",
"sheathed",
"sheathing",
"shedding",
"shedding fur",
"sheep",
"sheep ears",
"sheep girl",
"sheep horns",
"sheep tail",
"sheet grab",
"sheet music",
"shelf",
"shell",
"shell casing",
"shell earrings",
"shelving book",
"shepherd",
"sheriff",
"shiba inu",
"shibari",
"shibari over clothes",
"shibuya (tokyo)",
"shibuya 109",
"shibuya scramble crossing",
"shide",
"shield",
"shiitake",
"shikoro",
"shimakaze (kancolle) (cosplay)",
"shimenawa",
"shin guards",
"shinai",
"shiny",
"shiny clothes",
"shiny footwear",
"shiny legwear",
"shiny lips",
"shiny pantyhose",
"shiny pokemon",
"shiny skin",
"ship",
"shiromuku",
"shirt",
"shirt bow",
"shirt grab",
"shirt in mouth",
"shirt lift",
"shirt on shoulders",
"shirt overhang",
"shirt partially tucked in",
"shirt pull",
"shirt slip",
"shirt tucked in",
"shirt tug",
"shoal",
"shoe dangle",
"shoe flower",
"shoe locker pov",
"shoe loss",
"shoe soles",
"shoelaces",
"shoes",
"shooing",
"shooting range",
"shooting star",
"shop",
"shopping",
"shopping bag",
"shopping basket",
"shopping cart",
"shore",
"short bangs",
"short dress",
"short eyebrows",
"short hair",
"short hair with long locks",
"short kimono",
"short necktie",
"short over long sleeves",
"short ponytail",
"short shorts",
"short sidetail",
"short sleeves",
"short sword",
"short twintails",
"shorts",
"shorts around one leg",
"shorts aside",
"shorts pull",
"shorts under shorts",
"shorts under skirt",
"shortstack",
"shot down",
"shot glass",
"shotgun",
"shotgun shell",
"shouji",
"shoulder angel",
"shoulder armor",
"shoulder bag",
"shoulder belt",
"shoulder blades",
"shoulder blush",
"shoulder boards",
"shoulder cannon",
"shoulder cutout",
"shoulder devil",
"shoulder pads",
"shoulder patch",
"shoulder plates",
"shoulder spikes",
"shoulder strap",
"shoulder tattoo",
"shout lines",
"shouting",
"shovel",
"shower (place)",
"shower curtain",
"shower head",
"showering",
"showgirl skirt",
"shrimp",
"shrimp fried this rice (meme)",
"shrimp tempura",
"shrine",
"shrug (clothing)",
"shrugging",
"shuriken hair ornament",
"shushing",
"shy",
"siblings",
"sick",
"side ahoge",
"side braid",
"side braids",
"side cape",
"side ponytail",
"side slit",
"side-by-side",
"side-seamed gloves",
"side-seamed legwear",
"side-tie bikini bottom",
"side-tie panties",
"side-tie peek",
"side-view mirror",
"sideboob",
"sideburns",
"sidecar",
"sidelighting",
"sidelocks",
"sidewalk",
"sideways",
"sideways glance",
"sideways hat",
"sideways mouth",
"sig 556",
"sig mcx",
"sig p220/p226",
"sig sauer",
"sig sauer p320",
"sig ssg3000",
"sight magnifier",
"sign",
"signal bar",
"signal flag",
"signal lamp",
"signature",
"sigrunen",
"silent comic",
"silhouette",
"silhouette target",
"silk",
"silver footwear",
"silver hair",
"simple background",
"simple bird",
"simulated fellatio",
"simulated paizuri",
"singing",
"single bare leg",
"single bare shoulder",
"single boot",
"single braid",
"single bridal gauntlet",
"single couter",
"single earphone removed",
"single earring",
"single elbow glove",
"single elbow pad",
"single empty eye",
"single epaulette",
"single fingerless glove",
"single flame",
"single garter strap",
"single gauntlet",
"single glove",
"single hair bun",
"single hair intake",
"single half glove",
"single hand",
"single horizontal stripe",
"single horn",
"single knee pad",
"single kneehigh",
"single leg pantyhose",
"single mechanical arm",
"single mechanical hand",
"single mitten",
"single pauldron",
"single shoe",
"single side bun",
"single sidelock",
"single sleeve",
"single sock",
"single stripe",
"single thighhigh",
"single-lens reflex camera",
"sink",
"sir arthur (makaimura) (cosplay)",
"sisters",
"site of grace",
"sitting",
"sitting backwards",
"sitting in window",
"sitting on bench",
"sitting on box",
"sitting on car",
"sitting on desk",
"sitting on face",
"sitting on lap",
"sitting on object",
"sitting on person",
"sitting on pillow",
"sitting on rock",
"sitting on shoulder",
"sitting on stairs",
"sitting on table",
"sitting sideways",
"size difference",
"skate park",
"skateboard",
"skateboarding",
"skates",
"skating",
"skeleton",
"sketch",
"sketchbook",
"sketching",
"skewer",
"skid mark",
"skin fang",
"skin fangs",
"skin tight",
"skin-covered horns",
"skindentation",
"skinny",
"skirt",
"skirt grab",
"skirt hold",
"skirt lift",
"skirt pull",
"skirt set",
"skirt suit",
"skirt tug",
"skirt under dress",
"skort",
"sks",
"skull",
"skull and crossbones",
"skull and crossed swords",
"skull earrings",
"skull hair ornament",
"skull hat ornament",
"skull mask",
"skull necklace",
"skull ornament",
"skull print",
"skull ring",
"skullcap",
"sky",
"skylight",
"skylight (architecture)",
"skyline",
"skyscraper",
"slam dunk (basketball)",
"slap mark",
"slaps roof of car (meme)",
"slave",
"sleep mask",
"sleeping",
"sleeping animal",
"sleeping on desk",
"sleeping on person",
"sleeping upright",
"sleepwear",
"sleepy",
"sleeve bow",
"sleeve cuffs",
"sleeve garter",
"sleeve grab",
"sleeve pull",
"sleeve ribbon",
"sleeve rolled up",
"sleeveless",
"sleeveless dress",
"sleeveless jacket",
"sleeveless kimono",
"sleeveless shirt",
"sleeveless sweater",
"sleeveless turtleneck",
"sleeveless turtleneck leotard",
"sleeves past elbows",
"sleeves past fingers",
"sleeves past wrists",
"sleeves pushed up",
"sleeves rolled up",
"sleigh",
"sliced cheese",
"sliding doors",
"slim legs",
"slime (creature)",
"slime (substance)",
"slime girl",
"slimification",
"slingshot swimsuit",
"slippers",
"slit pupils",
"slouch hat",
"slouching",
"slovenly",
"slug girl",
"slums",
"small breasts",
"small head",
"small penis",
"small testicles",
"smartphone",
"smartphone case",
"smartwatch",
"smeared lipstick",
"smell",
"smelling",
"smelling clothes",
"smelling hair",
"smelling underwear",
"smersh",
"smile",
"smiley face",
"smirk",
"smoke",
"smoke grenade",
"smoke trail",
"smokestack",
"smoking",
"smoking gun",
"smoking pipe",
"smother",
"smug",
"smug trap (meme)",
"snack",
"snail girl",
"snail hands",
"snail shell",
"snake",
"snake hair",
"snake mouth",
"snake tattoo",
"snap-fit buckle",
"snapchat",
"sneakers",
"sneaking",
"sneaking suit",
"sneed's feed and seed (meme)",
"sniper rifle",
"sniper team",
"sniping",
"snoot challenge",
"snorkel",
"snot",
"snout",
"snow",
"snow globe",
"snow rabbit",
"snow shovel",
"snowball",
"snowball fight",
"snowflake earrings",
"snowflake hair ornament",
"snowflake ornament",
"snowflake print",
"snowflakes",
"snowing",
"snowman",
"snowman print",
"soaking feet",
"soap bubbles",
"soap censor",
"sobbing",
"soccer",
"soccer ball",
"soccer uniform",
"sock pull",
"socks",
"socks over pantyhose",
"soda",
"soda bottle",
"soda can",
"sode",
"softboiled egg",
"solar system",
"soldering iron",
"soldier",
"soles",
"solid circle eyes",
"solid circle pupils",
"solid eyes",
"solid oval eyes",
"solid state drive",
"solo",
"solo focus",
"solraka",
"sombrero",
"song name",
"sos",
"sound effects",
"soup",
"soup ladle",
"soviet",
"soviet army",
"soviet flag",
"space",
"space cat (meme)",
"space helmet",
"space marine",
"space print",
"space shuttle",
"space station",
"spacecraft",
"spacecraft interior",
"spacesuit",
"spade (shape)",
"spade tattoo",
"spaghetti",
"spaghetti strap",
"spanish inquisition",
"spanish text",
"spanked",
"spanking",
"sparkle",
"sparkle background",
"sparkler",
"sparkling eyes",
"sparks",
"sparrow",
"spartan (halo)",
"spatula",
"speaker",
"spear",
"special air service",
"speech bubble",
"speed limit sign",
"speed lines",
"sperm cell",
"spetsnaz",
"sphinx",
"spider",
"spider girl",
"spider print",
"spider web",
"spider web print",
"spidersona",
"spiked armlet",
"spiked armor",
"spiked belt",
"spiked boots",
"spiked bracelet",
"spiked choker",
"spiked collar",
"spiked dildo",
"spiked ear piercing",
"spiked footwear",
"spiked gauntlets",
"spiked hair",
"spiked hairband",
"spiked hood",
"spiked knuckles",
"spiked legwear",
"spiked mace",
"spiked pauldrons",
"spikes",
"spill",
"spilling",
"spine",
"spinning",
"spinning top",
"spinosaurus",
"spiral",
"spiral staircase",
"spire",
"spirit",
"spitroast",
"splashing",
"splatter",
"split",
"split mouth",
"split screen",
"split theme",
"split-color hair",
"spoken anger vein",
"spoken blush",
"spoken character",
"spoken ellipsis",
"spoken emoji",
"spoken exclamation mark",
"spoken flower",
"spoken flying sweatdrops",
"spoken heart",
"spoken interrobang",
"spoken mars symbol",
"spoken musical note",
"spoken object",
"spoken question mark",
"spoken squiggle",
"spoken sweatdrop",
"spoken symbol",
"sponsor",
"spoon",
"spooning",
"sports bikini",
"sports bra",
"sports bra lift",
"sports bra pull",
"sports car",
"sports festival",
"sports utility vehicle",
"sportswear",
"spot color",
"spotify",
"spotlight",
"spotting scope",
"sprain",
"spray bottle",
"spray can",
"spray paint",
"spraying",
"spread anus",
"spread arms",
"spread ass",
"spread fingers",
"spread legs",
"spread navel",
"spread pussy",
"spread pussy under clothes",
"spread toes",
"spreader bar",
"spring (season)",
"spring onion",
"spurs",
"square",
"square 4koma",
"squatting",
"squatting cowgirl position",
"squeezing",
"squeezing testicles",
"squiggle",
"squinting",
"sr-25",
"ss insignia",
"ss uniform",
"st basil's cathedral",
"st. andrew's cross",
"staccato 2011",
"stacking",
"stadium",
"staff",
"stag beetle",
"stage lights",
"stahlhelm",
"stained clothes",
"stained glass",
"stained panties",
"stairs",
"stairwell",
"stalking",
"stance",
"stand (jojo)",
"standard bearer",
"standing",
"standing cunnilingus",
"standing on box",
"standing on one leg",
"standing on three legs",
"standing sex",
"standing split",
"star (sky)",
"star (symbol)",
"star balloon",
"star censor",
"star earrings",
"star facial mark",
"star hair ornament",
"star halo",
"star hat ornament",
"star in eye",
"star ornament",
"star tattoo",
"star-shaped pupils",
"staring",
"starry sky",
"starry sky print",
"state of puebla",
"state of veracruz",
"station",
"station attendant",
"stationery",
"stats",
"statue",
"steak",
"steal her look (meme)",
"stealth fingering",
"stealth masturbation",
"stealth paizuri",
"stealth sex",
"steam",
"steam censor",
"steaming body",
"steampunk",
"steel beam",
"steelseries",
"steepled fingers",
"steering wheel",
"stencil (object)",
"step-brother and step-sister",
"step-siblings",
"stepped on",
"stepping stones",
"stereo",
"sterling smg",
"stethoscope",
"stew",
"stg44",
"stick",
"stick figure",
"sticker",
"sticker on face",
"sticks",
"sticky",
"sticky note",
"stiletto heels",
"still life",
"stinger",
"stirrup legwear",
"stirrups (riding)",
"stitches",
"stocks",
"stole",
"stomach",
"stomach bulge",
"stomach day",
"stomach tattoo",
"stone",
"stone floor",
"stone stairs",
"stone walkway",
"stone wall",
"stool",
"stop sign",
"stopwatch",
"storefront",
"storm",
"storm cloud",
"storm drain",
"stove",
"straddling",
"straight hair",
"straight-on",
"straitjacket",
"strangling",
"strap",
"strap between breasts",
"strap gap",
"strap lift",
"strap pull",
"strap slip",
"strap-on",
"strapless",
"strapless bikini",
"strapless bottom",
"strapless bra",
"strapless dress",
"strapless leotard",
"strapless shirt",
"strappy heels",
"straw hat",
"strawberry",
"strawberry cake",
"strawberry earrings",
"strawberry hair ornament",
"strawberry milk",
"strawberry pocky",
"strawberry shortcake",
"strawberry slice",
"stray pubic hair",
"streaked hair",
"stream",
"streaming tears",
"street",
"stress",
"stretching",
"strichtarn",
"string",
"string bikini",
"string bra",
"string choker",
"string lights",
"string of fate",
"string of flags",
"string panties",
"string phone",
"striped",
"striped ascot",
"striped background",
"striped bikini",
"striped bow",
"striped bowtie",
"striped clothes",
"striped dress",
"striped hair",
"striped headwear",
"striped hoodie",
"striped horns",
"striped jacket",
"striped kimono",
"striped necktie",
"striped pajamas",
"striped panties",
"striped pants",
"striped pantyhose",
"striped ribbon",
"striped scarf",
"striped shirt",
"striped shorts",
"striped skin",
"striped skirt",
"striped sleeves",
"striped socks",
"striped sweater",
"striped tail",
"striped tank top",
"striped thighhighs",
"stripper",
"stripper pole",
"stroking own chin",
"strong",
"strong zero",
"stubble",
"stuck",
"stud earrings",
"studded belt",
"studded bracelet",
"studded choker",
"studded collar",
"studded trim",
"studying",
"stuffed animal",
"stuffed bird",
"stuffed cat",
"stuffed cow",
"stuffed crocodile",
"stuffed dog",
"stuffed dragon",
"stuffed mouse",
"stuffed mushroom",
"stuffed panda",
"stuffed penguin",
"stuffed pig",
"stuffed rabbit",
"stuffed shark",
"stuffed squirrel",
"stuffed toy",
"stuffed whale",
"stun gun",
"style parody",
"stylus",
"su-27",
"su-57",
"submachine gun",
"submarine",
"submerged",
"subtitled",
"subway",
"subway station",
"sucking both nipples",
"sucking on multiple breasts",
"suction cups",
"suggestive fluid",
"sugoi dekai",
"suit",
"suit jacket",
"suitcase",
"sukeban",
"sukiyaki",
"sumida (tokyo)",
"summer",
"summer festival",
"summer uniform",
"summoning",
"sun",
"sun hair ornament",
"sun hat",
"sun shower",
"sun symbol",
"sun tattoo",
"sunbathing",
"sunbeam",
"sunburst",
"sundae",
"sundress",
"sunflower",
"sunflower field",
"sunglasses",
"sunken cheeks",
"sunlight",
"sunrise",
"sunset",
"super mushroom",
"superhero",
"supermarket",
"suppressor",
"supreme (brand)",
"surcoat",
"surfboard",
"surgical mask",
"surprise kiss",
"surprised",
"surreal",
"surrounded",
"surrounded by penises",
"sushi",
"suspended congress",
"suspender shorts",
"suspender skirt",
"suspenders",
"suspenders pull",
"sv-98",
"svt-38",
"swaddled",
"swallow (bird)",
"swan",
"swastika",
"swat",
"sweat",
"sweatband",
"sweatdrop",
"sweater",
"sweater around waist",
"sweater dress",
"sweater lift",
"sweater pull",
"sweater tucked in",
"sweater vest",
"sweater vest lift",
"sweatpants",
"sweaty clothes",
"sweden",
"swedish flag",
"swedish text",
"swedish uniform",
"sweet potato",
"sweets",
"swept bangs",
"swim ring",
"swimming",
"swimsuit",
"swimsuit around one leg",
"swimsuit aside",
"swimsuit cover-up",
"swimsuit hanger",
"swimsuit lift",
"swimsuit tug",
"swing",
"swing set",
"swinging",
"swinging legs",
"swirl lollipop",
"swiss roll",
"switchblade",
"swivel chair",
"sword",
"sword behind back",
"sword on back",
"sword over shoulder",
"sword writing",
"swordbreaker (weapon)",
"symbol in eye",
"symbol-shaped eyes",
"symbol-shaped pupils",
"symbolism",
"symmetrical docking",
"synthesizer",
"syria",
"syrian civil war",
"syringe",
"t-34",
"t-64",
"t-64bv",
"t-72",
"t-pose",
"t-shirt",
"t91 assault rifle",
"tabard",
"tabby cat",
"tabi",
"table",
"tablecloth",
"tablet pc",
"tachi (weapon)",
"tachi-e",
"tack (riding)",
"tactical clothes",
"tactical maid",
"tactile paving",
"tag",
"tail",
"tail bell",
"tail between legs",
"tail biting",
"tail bow",
"tail brushing",
"tail grab",
"tail insertion",
"tail lights",
"tail ornament",
"tail piercing",
"tail raised",
"tail removed",
"tail ribbon",
"tail ring",
"tail wagging",
"tail wrap",
"tailcoat",
"tailjob",
"taiwan",
"taiyaki",
"take it home",
"take your pick",
"taking notes",
"taking picture",
"taking shelter",
"tako-san wiener",
"takoyaki",
"talisman",
"talking",
"talking animal",
"talking on phone",
"tall",
"tall female",
"tall grass",
"tall lady shopping in japanese store (meme)",
"tally",
"talons",
"tamagoyaki",
"tan",
"tank",
"tank helmet",
"tank top",
"tank turret",
"tankard",
"tanline peek",
"tanlines",
"tanuki",
"tape",
"tape dispenser",
"tape gag",
"tape on nipples",
"taped note",
"tapir",
"tareme",
"target practice",
"tart (food)",
"tassel",
"tassel choker",
"tassel earrings",
"tassel hair ornament",
"tasting",
"tasuki",
"tatami",
"tattoo",
"taur",
"taut clothes",
"taut dress",
"taut pants",
"taut shirt",
"taut skirt",
"taut swimsuit",
"tavern",
"tea",
"teacher",
"teacher and student",
"teacup",
"teapot",
"teardrop",
"tearing clothes",
"tearing up",
"tears",
"teasing",
"tech support",
"teddy (lingerie)",
"teddy bear",
"teddy bear hair ornament",
"teeth",
"teleport",
"teleporter",
"telescope",
"television",
"telnyashka",
"telogreika",
"temari ball",
"template",
"temple",
"tempura",
"tenga",
"tennis",
"tennis ball",
"tennis racket",
"tennis skirt",
"tennis uniform",
"tent",
"tentacle hair",
"tentacle sex",
"tentacles",
"tentacles under clothes",
"tented shirt",
"teruterubouzu",
"test",
"test score (paper)",
"test tube",
"testicle grab",
"testicle sucking",
"testicles",
"testicles touching",
"tetrapod",
"text background",
"text focus",
"text messaging",
"text print",
"textbook",
"thai student uniform",
"thank you",
"thatched roof",
"the cooler daniel (meme)",
"the north face",
"the pose",
"the west has fallen (meme)",
"theater seating",
"theft",
"thermal paste",
"thermos",
"thick eyebrows",
"thick eyelashes",
"thick lips",
"thick thighs",
"thigh belt",
"thigh boots",
"thigh gap",
"thigh grab",
"thigh holster",
"thigh pouch",
"thigh ribbon",
"thigh sex",
"thigh strap",
"thighband pantyhose",
"thighhighs",
"thighhighs over pantyhose",
"thighhighs under boots",
"thighlet",
"thighs",
"thinking",
"thinkpad",
"third eye",
"this egg got me acting unwise (meme)",
"thompson submachine gun",
"thong",
"thong aside",
"thong bikini",
"thong leotard",
"thorns",
"thought bubble",
"thread",
"three sizes",
"threesome",
"throat microphone",
"throne",
"through clothes",
"through door",
"through medium",
"through screen",
"throwing",
"thumb ring",
"thumbs up",
"thumbtack",
"thurible",
"tiara",
"tiara removed",
"ticket",
"tickling",
"tickling stomach",
"tie clip",
"tie fighter",
"tied hair",
"tied shirt",
"tied to chair",
"tied up (nonsexual)",
"tiered skirt",
"tiered tray",
"tiger",
"tiger costume",
"tiger cub",
"tiger ears",
"tiger i",
"tiger print",
"tiger tail",
"tight clothes",
"tight pants",
"tight shirt",
"tights day",
"tile ceiling",
"tile floor",
"tile roof",
"tile wall",
"tiles",
"tilted headwear",
"tiltrotor",
"tim hortons",
"timbougami",
"time lapse",
"time paradox",
"timestamp",
"tinted eyewear",
"tiptoe kiss",
"tiptoes",
"tire",
"tire swing",
"tissue",
"tissue box",
"title",
"title parody",
"tnt",
"tnt block (minecraft)",
"to be continued",
"toast",
"toddler",
"toe cleavage",
"toe ring",
"toe scrunch",
"toe seam",
"toeless footwear",
"toeless legwear",
"toenail polish",
"toenails",
"toes",
"toga",
"toggles",
"toilet",
"toilet brush",
"toilet paper",
"toilet paper tube",
"toilet stall",
"toilet use",
"tokkuri",
"tokusatsu",
"tokyo (city)",
"tokyo skytree",
"tomato",
"tomboy",
"tomoe (symbol)",
"toned",
"toned male",
"tongue",
"tongue out",
"tongue piercing",
"tonguejob",
"tony hawk's existential nightmare (meme)",
"too bad! it was just me! (meme)",
"too literal",
"too long",
"too many",
"too many books",
"too many butterflies",
"too many condoms",
"too many sex toys",
"too much food",
"toolbox",
"toon (style)",
"tooth",
"tooth gap",
"tooth necklace",
"toothbrush",
"toothpaste",
"top hat",
"top-down bottom-up",
"topknot",
"topless",
"topless male",
"torch",
"torii",
"torn",
"torn buruma",
"torn cape",
"torn cloak",
"torn clothes",
"torn dress",
"torn hat",
"torn jeans",
"torn legwear",
"torn pants",
"torn pantyhose",
"torn scarf",
"torn shirt",
"torn shorts",
"torn sleeves",
"torn swimsuit",
"torn thighhighs",
"torn wings",
"tornado",
"torogao",
"torpedo",
"torso flash",
"torso grab",
"torture",
"tossing",
"tote bag",
"totenkopf",
"touching another's back",
"tourbox",
"towel",
"towel around neck",
"tower",
"town",
"toy",
"toy airplane",
"toy car",
"toy gun",
"toy tank",
"toyota",
"toyota supra",
"toyota supra mk iii",
"track and field",
"track jacket",
"track marks",
"track pants",
"track suit",
"track uniform",
"traction beam",
"tractor",
"trade offer (meme)",
"traditional chinese text",
"traditional clothes",
"traditional dress",
"traditional media",
"traditional nun",
"traffic",
"traffic barrier",
"traffic baton",
"traffic cone",
"traffic light",
"trail",
"trailer",
"train",
"train (clothing)",
"train interior",
"train station",
"train station platform",
"trans rights",
"transformation",
"transgender flag",
"translation note",
"translucent",
"translucent skin",
"transmission tower",
"transparent",
"transparent background",
"transparent curtains",
"transparent footwear",
"transparent raincoat",
"transparent umbrella",
"trash",
"trash bag",
"trash can",
"travel attendant",
"tray",
"treasure chest",
"tree",
"tree shade",
"tree stump",
"trellis",
"trembling",
"trench",
"trench coat",
"trial",
"triangle",
"triangle earrings",
"triangle mouth",
"triangle print",
"triangular headpiece",
"tribal",
"tribal tattoo",
"tricorne",
"tricycle",
"trident print",
"trigger discipline",
"trigram",
"triple penetration",
"triplets",
"tripod",
"tripping",
"triptych (art)",
"troll face",
"trophy",
"tropical drink",
"trowel",
"truck",
"truth",
"trying on clothes",
"tryzub",
"tsubasa tsubasa (style)",
"tsumami kanzashi",
"tsundere",
"tsurime",
"tube",
"tube top",
"tucked penis",
"tulip",
"tumbleweed",
"tunic",
"tunnel",
"turkey (country)",
"turkish clothes",
"turkish flag",
"turkish text",
"turn pale",
"turnaround",
"turning head",
"turret",
"turtle",
"turtle costume",
"turtleneck",
"turtleneck dress",
"turtleneck sweater",
"tusks",
"tutu",
"tuxedo",
"tweaking own nipple",
"tweezers",
"twig",
"twilight",
"twin braids",
"twin drills",
"twin-lens reflex camera",
"twins",
"twintails",
"twintails day",
"twisted breasts",
"twisted hair",
"twisted torso",
"twitching",
"twitter logo",
"twitter strip game (meme)",
"twitter username",
"twitter verified checkmark",
"two side up",
"two soyjaks pointing (meme)",
"two tails",
"two-footed footjob",
"two-handed",
"two-handed handjob",
"two-handed sword",
"two-sided cape",
"two-sided fabric",
"two-sided headwear",
"two-sided hoodie",
"two-tone background",
"two-tone bikini",
"two-tone bow",
"two-tone dress",
"two-tone eyes",
"two-tone footwear",
"two-tone gloves",
"two-tone hair",
"two-tone headwear",
"two-tone hoodie",
"two-tone jacket",
"two-tone legwear",
"two-tone necktie",
"two-tone panties",
"two-tone scarf",
"two-tone shirt",
"two-tone skin",
"two-tone skirt",
"two-tone socks",
"two-tone sports bra",
"two-tone sweater",
"two-tone swimsuit",
"tying",
"tying apron",
"tying footwear",
"tying hair",
"typing",
"typo",
"u_u",
"uc card",
"uchikake",
"uchiwa",
"udon",
"ufo",
"ugly man",
"uh-60 blackhawk",
"ukiyo-e",
"ukraine",
"ukrainian flag",
"ukrainian text",
"ultramarines",
"umbrella",
"umbrella octopus",
"umbrella stand",
"unamused",
"unbuttoned",
"unbuttoned jacket",
"unbuttoned shirt",
"uncensored",
"unconscious",
"unconventional maid",
"unconventional media",
"undead",
"under armour",
"under covers",
"under kotatsu",
"under table",
"under tree",
"under-rim eyewear",
"underbarrel grenade launcher",
"underboob",
"underboob cutout",
"underbust",
"underbutt",
"undercut",
"underground",
"underlighting",
"undershirt",
"undersized animal",
"undersized clothes",
"undertaker standing behind aj styles (meme)",
"underwater",
"underwear",
"underwear only",
"undone bra",
"undone necktie",
"undressing",
"undressing another",
"uneven eyes",
"uneven legwear",
"uneven sleeves",
"unfastened",
"uniform",
"union jack",
"unit patch",
"united kingdom",
"united nations",
"united states",
"united states army",
"united states marine corps",
"united states navy",
"universe",
"unkempt",
"unmoving pattern",
"unsheathed",
"unsheathing",
"untied bikini",
"untucked shirt",
"untying hair",
"unwanted creampie",
"unworn apron",
"unworn armor",
"unworn backpack",
"unworn bag",
"unworn belt",
"unworn bikini",
"unworn bikini top",
"unworn boots",
"unworn bra",
"unworn clothes",
"unworn dress",
"unworn earring",
"unworn eyewear",
"unworn goggles",
"unworn hair ornament",
"unworn hairclip",
"unworn hat",
"unworn headwear",
"unworn helmet",
"unworn jacket",
"unworn jewelry",
"unworn kimono",
"unworn legwear",
"unworn male underwear",
"unworn mask",
"unworn necktie",
"unworn panties",
"unworn pantyhose",
"unworn sandals",
"unworn scarf",
"unworn scrunchie",
"unworn shirt",
"unworn shoes",
"unworn shorts",
"unworn skirt",
"unworn socks",
"unworn sweater",
"unworn swimsuit",
"unzipped",
"uohhhhhhhhh! (meme)",
"updo",
"upper body",
"upper teeth only",
"upright straddle",
"upside-down",
"upskirt",
"upturned eyes",
"urban",
"urban camouflage",
"urban legend",
"urethra",
"uriko (baseball)",
"usb",
"used condom",
"used condom on penis",
"used tissue",
"usekh collar",
"user interface",
"ushanka",
"uss brooklyn (cl-40)",
"uss enterprise (cv-6)",
"uss essex (cv-9)",
"uss independence (cvl-22)",
"uss south dakota (bb-57)",
"uss washington (bb-56)",
"utensil in mouth",
"uterus",
"utility belt",
"utility pole",
"utility vest",
"uva academy school uniform",
"uwabaki",
"uwagi",
"uwu",
"v",
"v arms",
"v over eye",
"v-22 osprey",
"v-neck",
"v-shaped eyebrows",
"v-shaped eyes",
"vacation",
"vaginal",
"vaginal object insertion",
"valentine",
"vambraces",
"vampire",
"vampire costume",
"van",
"vanishing point",
"vanripper (style)",
"vans",
"vaporeon (cosplay)",
"varangian guard",
"varia suit",
"variations",
"vase",
"vaulting horse",
"vdv",
"vegetable",
"vegetation",
"vehicle and personification",
"vehicle focus",
"veil",
"veins",
"veiny arms",
"veiny hands",
"veiny penis",
"vending cart",
"vending machine",
"venice",
"venom snake (cosplay)",
"venus symbol",
"veranda",
"vertical foregrip",
"vertical stripes",
"vertical-striped clothes",
"vertical-striped dress",
"vertical-striped kimono",
"vertical-striped pants",
"vertical-striped pantyhose",
"vertical-striped scarf",
"vertical-striped shirt",
"vertical-striped skirt",
"very dark skin",
"very long fingernails",
"very long hair",
"very long sleeves",
"very long tail",
"very short hair",
"very sweaty",
"very wide shot",
"vest",
"vhs artifacts",
"vial",
"vibrator",
"vibrator cord",
"vibrator in thighhighs",
"vibrator under clothes",
"vibrator under panties",
"victorian",
"victorian maid",
"victory",
"video call",
"video camera",
"video game",
"videocassette",
"vietnam",
"vietnam war",
"vietnamese dress",
"vietnamese high school uniform",
"view between legs",
"viewer holding leash",
"viewfinder",
"vignetting",
"viking",
"village",
"vines",
"violin",
"virgin destroyer sweater",
"virgin killer outfit",
"virgin killer sweater",
"virgin mary (cosplay)",
"virgin vs chad (meme)",
"virtual reality",
"virtual youtuber",
"virus",
"visor",
"visor (armor)",
"visor cap",
"visor lift",
"visual basic (programming)",
"vkontakte username",
"vodka",
"voice recorder",
"volcano",
"volleyball",
"volleyball (object)",
"volleyball uniform",
"vorpal sword (day of wrath)",
"vss vintorez",
"vz. 58",
"w",
"w arms",
"wa lolita",
"wading",
"waffen-ss",
"wagashi",
"wagon",
"waist apron",
"waist cape",
"waistcoat",
"waistpack",
"wait a minute... this isn't tennis! this is anal sex!! (meme)",
"waiting",
"waitress",
"wake",
"waking up",
"walk-in",
"walkie-talkie",
"walking",
"walking away",
"walking bike",
"walking on liquid",
"walkman",
"wall",
"wall clock",
"wall lamp",
"wall of text",
"wallet chain",
"wallpaper (object)",
"wand",
"war",
"war flag",
"war hammer",
"wardrobe malfunction",
"warehouse",
"wariza",
"warning sign",
"warped",
"warrior",
"warship",
"washbowl",
"washing",
"washing machine",
"wataboushi",
"watch",
"watching",
"watching television",
"water",
"water bottle",
"water dragon",
"water drop",
"water lily flower",
"water pipe",
"watercolor (medium)",
"watercolor effect",
"watercraft",
"waterfall",
"watermark",
"watermelon",
"watermelon bar",
"watermelon print",
"watson amelia (cosplay)",
"watson cross",
"waves",
"waving",
"wavy eyes",
"wavy hair",
"wavy mouth",
"weapon",
"weapon behind back",
"weapon case",
"weapon family",
"weapon focus",
"weapon name",
"weapon on back",
"weapon over shoulder",
"weapon rack",
"weasel",
"weathergirl",
"web address",
"webley revolver",
"wedding",
"wedding dress",
"wedding ring",
"wedgie",
"wehrmacht",
"weibo logo",
"weibo username",
"weighing breasts",
"weighing scale",
"weight conscious",
"weightlifting",
"weights",
"welsh corgi",
"werewolf",
"western comics (style)",
"western dragon",
"wet",
"wet clothes",
"wet dream",
"wet dress",
"wet face",
"wet floor",
"wet ground",
"wet hair",
"wet panties",
"wet pantyhose",
"wet shirt",
"wet skirt",
"wet swimsuit",
"wetland",
"wetsuit",
"whale",
"whale tail (clothing)",
"what",
"wheat",
"wheat field",
"wheelchair",
"wheellock",
"when you see it",
"where's the cat? (meme)",
"whip",
"whip marks",
"whipped cream",
"whisk",
"whisker markings",
"whiskers",
"whiskey",
"whispering",
"whistle",
"whistle around neck",
"whistling",
"white apron",
"white armband",
"white ascot",
"white background",
"white bag",
"white belt",
"white bikini",
"white bird",
"white bodysuit",
"white border",
"white bow",
"white bowtie",
"white bra",
"white butterfly",
"white camisole",
"white cane",
"white cape",
"white capelet",
"white cardigan",
"white cat",
"white choker",
"white cloak",
"white coat",
"white collar",
"white dress",
"white ensign",
"white eyes",
"white feathers",
"white flower",
"white footwear",
"white fur",
"white garter belt",
"white gloves",
"white hair",
"white hairband",
"white headband",
"white headdress",
"white headwear",
"white hoodie",
"white horns",
"white jacket",
"white kimono",
"white leggings",
"white legwear",
"white leotard",
"white lily",
"white mask",
"white mittens",
"white nails",
"white neckerchief",
"white necktie",
"white nightgown",
"white one-piece swimsuit",
"white outline",
"white overalls",
"white pajamas",
"white panties",
"white pants",
"white pantyhose",
"white pupils",
"white rabbit (animal)",
"white ribbon",
"white robe",
"white rose",
"white sailor collar",
"white sash",
"white scales",
"white scarf",
"white scrunchie",
"white serafuku",
"white shawl",
"white shirt",
"white shorts",
"white skin",
"white skirt",
"white sky",
"white sleeves",
"white snake",
"white sneakers",
"white socks",
"white sports bra",
"white stripes",
"white suit",
"white sweater",
"white sweater vest",
"white tail",
"white tank top",
"white theme",
"white thighhighs",
"white towel",
"white trim",
"white tulip",
"white tunic",
"white umbrella",
"white vest",
"white wings",
"white wrist cuffs",
"white wristband",
"whiteboard",
"wide brim",
"wide hips",
"wide oval eyes",
"wide shot",
"wide sleeves",
"wide spread legs",
"wide-eyed",
"wife and wife",
"wiffle gag",
"wifi symbol",
"wii remote",
"willow",
"wimple",
"wince",
"winch",
"winchester model 1866",
"winchester model 1887",
"winchester model 1897",
"wind",
"wind chime",
"wind lift",
"wind turbine",
"winding",
"windmill",
"window",
"window (computing)",
"window blinds",
"window shade",
"window shutter",
"windowsill",
"wine",
"wine bottle",
"wine glass",
"wing collar",
"wing hair ornament",
"wing ornament",
"wing tattoo",
"winged arms",
"winged footwear",
"winged helmet",
"wings",
"winter",
"winter clothes",
"winter coat",
"winter gloves",
"winter uniform",
"wiping face",
"wiping forehead",
"wiping sweat",
"wiping tears",
"wire",
"wireless earphones",
"wisteria",
"witch",
"witch hat",
"with a car you can go anywhere you want (meme)",
"wizard",
"wok",
"wolf",
"wolf boy",
"wolf cut",
"wolf ears",
"wolf girl",
"wolf hat",
"wolf pelt",
"wolf tail",
"woman shouting knives (meme)",
"woman yelling at cat (meme)",
"women want me fish fear me (meme)",
"women's wallet",
"wood",
"wood art",
"wooden bench",
"wooden box",
"wooden bucket",
"wooden chair",
"wooden fence",
"wooden floor",
"wooden horse",
"wooden lantern",
"wooden railing",
"wooden shield",
"wooden staff",
"wooden sword",
"wooden table",
"wooden wall",
"woodland camouflage",
"wool",
"woollen cap",
"working",
"workshop",
"world map",
"world war i",
"world war ii",
"worm",
"worried",
"wreath",
"wreckage",
"wrench",
"wringing clothes",
"wringing skirt",
"wrinkled fabric",
"wrinkled skin",
"wrist cuffs",
"wrist flower",
"wrist ribbon",
"wrist scrunchie",
"wrist wrap",
"wristband",
"wristwatch",
"writing",
"wrong foot",
"wrong hand",
"x",
"x hair ornament",
"x-2 shinshin",
"x-cross (bdsm)",
"x-ray",
"x-shaped pupils",
"xd",
"xm2010",
"y-wing",
"yakiniku",
"yamamura sadako (cosplay)",
"yami kawaii",
"yandere",
"yandere trance",
"yaoi",
"yawning",
"year of the ox",
"year of the pig",
"year of the rabbit",
"year of the tiger",
"yearbook",
"yellow apron",
"yellow armband",
"yellow ascot",
"yellow background",
"yellow bikini",
"yellow bow",
"yellow bowtie",
"yellow butterfly",
"yellow camisole",
"yellow cardigan",
"yellow choker",
"yellow dress",
"yellow eyes",
"yellow flower",
"yellow footwear",
"yellow gloves",
"yellow hairband",
"yellow halo",
"yellow headwear",
"yellow hoodie",
"yellow jacket",
"yellow kimono",
"yellow legwear",
"yellow lips",
"yellow nails",
"yellow neckerchief",
"yellow necktie",
"yellow outline",
"yellow pajamas",
"yellow panties",
"yellow pants",
"yellow pantyhose",
"yellow pupils",
"yellow raincoat",
"yellow ribbon",
"yellow rose",
"yellow sash",
"yellow scarf",
"yellow sclera",
"yellow scrunchie",
"yellow shawl",
"yellow shirt",
"yellow skin",
"yellow skirt",
"yellow sky",
"yellow socks",
"yellow sweater",
"yellow sweater vest",
"yellow tail",
"yellow tank top",
"yellow theme",
"yellow thighhighs",
"yellow tongue",
"yellow vest",
"yellow-framed eyewear",
"yellow-tinted eyewear",
"yes",
"yes-no pillow",
"yf-23",
"yin yang",
"yin yang earrings",
"yin yang orb",
"yin yang print",
"yoga",
"yoga mat",
"yoga pants",
"yokozuwari",
"yor briar (cosplay)",
"yoshi egg",
"you died",
"you gonna get raped",
"you're doing it wrong",
"young michael scott shaking ed truck's hand (meme)",
"yugake",
"yukata",
"yuki onna",
"yume kawaii",
"yume no tsue",
"yumi (bow)",
"yuri",
"z (russian symbol)",
"zebra print",
"zeon",
"zero gravity",
"zettai ryouiki",
"zipper",
"zipper footwear",
"zipper pull tab",
"zipping",
"zodiac",
"zombie",
"zombie pose",
"zoolander stare (meme)",
"zoom layer",
"zouri",
"zweihander",
"zzz",
"|_"
] |
Dimasnoufal/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0801
- Accuracy: 0.675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 23 | 1.0917 | 0.625 |
| No log | 2.0 | 46 | 1.1605 | 0.6125 |
| No log | 3.0 | 69 | 1.0543 | 0.6375 |
| No log | 4.0 | 92 | 1.1663 | 0.6 |
| No log | 5.0 | 115 | 1.2546 | 0.5875 |
| No log | 6.0 | 138 | 1.0580 | 0.6 |
| No log | 7.0 | 161 | 1.1193 | 0.6125 |
| No log | 8.0 | 184 | 1.2297 | 0.525 |
| No log | 9.0 | 207 | 1.2295 | 0.55 |
| No log | 10.0 | 230 | 1.0842 | 0.6125 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
SonishMaharjan/ditmodel
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ditmodel
This model was fintuned on DiT model for document classification on custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1482
- Accuracy: 0.9523
- Weighted f1: 0.9524
- Micro f1: 0.9523
- Macro f1: 0.9505
- Weighted recall: 0.9523
- Micro recall: 0.9523
- Macro recall: 0.9523
- Weighted precision: 0.9544
- Micro precision: 0.9523
- Macro precision: 0.9506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2337 | 1.0 | 78 | 0.2668 | 0.9087 | 0.9098 | 0.9087 | 0.9058 | 0.9087 | 0.9087 | 0.9040 | 0.9229 | 0.9087 | 0.9220 |
| 0.1711 | 2.0 | 156 | 0.1820 | 0.9376 | 0.9380 | 0.9376 | 0.9331 | 0.9376 | 0.9376 | 0.9403 | 0.9416 | 0.9376 | 0.9292 |
| 0.1297 | 3.0 | 234 | 0.1482 | 0.9523 | 0.9524 | 0.9523 | 0.9505 | 0.9523 | 0.9523 | 0.9523 | 0.9544 | 0.9523 | 0.9506 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.15.1
|
[
"citizenship",
"license",
"others",
"passport"
] |
GGital/vit-SUPER02
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-SUPER02
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0798 | 0.16 | 50 | 0.0393 | 0.9904 |
| 0.0161 | 0.31 | 100 | 0.0176 | 0.9936 |
| 0.0017 | 0.47 | 150 | 0.0020 | 0.9984 |
| 0.0012 | 0.62 | 200 | 0.0026 | 0.9985 |
| 0.0001 | 0.78 | 250 | 0.0001 | 1.0 |
| 0.0001 | 0.93 | 300 | 0.0001 | 1.0 |
| 0.0001 | 1.09 | 350 | 0.0001 | 1.0 |
| 0.0 | 1.24 | 400 | 0.0000 | 1.0 |
| 0.0 | 1.4 | 450 | 0.0000 | 1.0 |
| 0.0 | 1.55 | 500 | 0.0000 | 1.0 |
| 0.0 | 1.71 | 550 | 0.0000 | 1.0 |
| 0.0 | 1.86 | 600 | 0.0000 | 1.0 |
| 0.0 | 2.02 | 650 | 0.0000 | 1.0 |
| 0.0 | 2.17 | 700 | 0.0000 | 1.0 |
| 0.0 | 2.33 | 750 | 0.0000 | 1.0 |
| 0.0 | 2.48 | 800 | 0.0000 | 1.0 |
| 0.0 | 2.64 | 850 | 0.0000 | 1.0 |
| 0.0 | 2.8 | 900 | 0.0000 | 1.0 |
| 0.0 | 2.95 | 950 | 0.0000 | 1.0 |
| 0.0 | 3.11 | 1000 | 0.0000 | 1.0 |
| 0.0 | 3.26 | 1050 | 0.0000 | 1.0 |
| 0.0 | 3.42 | 1100 | 0.0000 | 1.0 |
| 0.0 | 3.57 | 1150 | 0.0000 | 1.0 |
| 0.0 | 3.73 | 1200 | 0.0000 | 1.0 |
| 0.0 | 3.88 | 1250 | 0.0000 | 1.0 |
| 0.0 | 4.04 | 1300 | 0.0000 | 1.0 |
| 0.0 | 4.19 | 1350 | 0.0000 | 1.0 |
| 0.0 | 4.35 | 1400 | 0.0000 | 1.0 |
| 0.0 | 4.5 | 1450 | 0.0000 | 1.0 |
| 0.0 | 4.66 | 1500 | 0.0000 | 1.0 |
| 0.0 | 4.81 | 1550 | 0.0000 | 1.0 |
| 0.0 | 4.97 | 1600 | 0.0000 | 1.0 |
| 0.0 | 5.12 | 1650 | 0.0000 | 1.0 |
| 0.0 | 5.28 | 1700 | 0.0000 | 1.0 |
| 0.0 | 5.43 | 1750 | 0.0000 | 1.0 |
| 0.0 | 5.59 | 1800 | 0.0000 | 1.0 |
| 0.0 | 5.75 | 1850 | 0.0000 | 1.0 |
| 0.0 | 5.9 | 1900 | 0.0000 | 1.0 |
| 0.0 | 6.06 | 1950 | 0.0000 | 1.0 |
| 0.0 | 6.21 | 2000 | 0.0000 | 1.0 |
| 0.0 | 6.37 | 2050 | 0.0000 | 1.0 |
| 0.0 | 6.52 | 2100 | 0.0000 | 1.0 |
| 0.0 | 6.68 | 2150 | 0.0000 | 1.0 |
| 0.0 | 6.83 | 2200 | 0.0000 | 1.0 |
| 0.0 | 6.99 | 2250 | 0.0000 | 1.0 |
| 0.0 | 7.14 | 2300 | 0.0000 | 1.0 |
| 0.0 | 7.3 | 2350 | 0.0000 | 1.0 |
| 0.0 | 7.45 | 2400 | 0.0000 | 1.0 |
| 0.0 | 7.61 | 2450 | 0.0000 | 1.0 |
| 0.0 | 7.76 | 2500 | 0.0000 | 1.0 |
| 0.0 | 7.92 | 2550 | 0.0000 | 1.0 |
| 0.0 | 8.07 | 2600 | 0.0000 | 1.0 |
| 0.0 | 8.23 | 2650 | 0.0000 | 1.0 |
| 0.0 | 8.39 | 2700 | 0.0000 | 1.0 |
| 0.0 | 8.54 | 2750 | 0.0000 | 1.0 |
| 0.0 | 8.7 | 2800 | 0.0000 | 1.0 |
| 0.0 | 8.85 | 2850 | 0.0000 | 1.0 |
| 0.0 | 9.01 | 2900 | 0.0000 | 1.0 |
| 0.0 | 9.16 | 2950 | 0.0000 | 1.0 |
| 0.0 | 9.32 | 3000 | 0.0000 | 1.0 |
| 0.0 | 9.47 | 3050 | 0.0000 | 1.0 |
| 0.0 | 9.63 | 3100 | 0.0000 | 1.0 |
| 0.0 | 9.78 | 3150 | 0.0000 | 1.0 |
| 0.0 | 9.94 | 3200 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"0",
"1",
"18",
"19",
"2",
"20",
"21",
"22",
"3",
"4",
"5",
"6",
"10",
"7",
"8",
"9",
"11",
"12",
"13",
"14",
"15",
"16",
"17"
] |
tom-beer/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [microsoft/swin-base-patch4-window12-384](https://huggingface.co/microsoft/swin-base-patch4-window12-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2147
- Accuracy: 0.921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2326 | 0.99 | 62 | 0.2147 | 0.921 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
ruanwz/autotrain-image-classification-for-slides-240203
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.31477582454681396
f1: 0.7499999999999999
precision: 1.0
recall: 0.6
auc: 0.915
accuracy: 0.8666666666666667
|
[
"keep",
"remove"
] |
TTNVXX/CartoonOrNotV2
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.15279646217823029
f1: 0.9732620320855614
precision: 0.9891304347826086
recall: 0.9578947368421052
auc: 0.9932718393922951
accuracy: 0.9739583333333334
|
[
"cartoon",
"not_cartoon"
] |
superlazycoder/vit-base-beans-demo-v5
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans-demo-v5
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0367
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0475 | 1.54 | 100 | 0.0625 | 0.9850 |
| 0.0038 | 3.08 | 200 | 0.0367 | 0.9850 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
danangy/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9492
- Accuracy: 0.5312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.3627 | 0.4313 |
| No log | 2.0 | 80 | 1.3275 | 0.4875 |
| No log | 3.0 | 120 | 1.2246 | 0.5188 |
| No log | 4.0 | 160 | 1.3181 | 0.5437 |
| No log | 5.0 | 200 | 1.2843 | 0.55 |
| No log | 6.0 | 240 | 1.3726 | 0.4938 |
| No log | 7.0 | 280 | 1.4959 | 0.475 |
| No log | 8.0 | 320 | 1.4542 | 0.4875 |
| No log | 9.0 | 360 | 1.7002 | 0.4625 |
| No log | 10.0 | 400 | 1.5043 | 0.5 |
| No log | 11.0 | 440 | 1.5684 | 0.5062 |
| No log | 12.0 | 480 | 1.6611 | 0.5 |
| 0.5862 | 13.0 | 520 | 1.7354 | 0.4688 |
| 0.5862 | 14.0 | 560 | 1.7357 | 0.4813 |
| 0.5862 | 15.0 | 600 | 1.7006 | 0.4875 |
| 0.5862 | 16.0 | 640 | 1.8564 | 0.4938 |
| 0.5862 | 17.0 | 680 | 1.8633 | 0.475 |
| 0.5862 | 18.0 | 720 | 1.7142 | 0.5062 |
| 0.5862 | 19.0 | 760 | 1.9792 | 0.4562 |
| 0.5862 | 20.0 | 800 | 1.8761 | 0.5 |
| 0.5862 | 21.0 | 840 | 2.0587 | 0.45 |
| 0.5862 | 22.0 | 880 | 2.0288 | 0.4813 |
| 0.5862 | 23.0 | 920 | 1.6472 | 0.5563 |
| 0.5862 | 24.0 | 960 | 2.0372 | 0.5 |
| 0.1675 | 25.0 | 1000 | 1.8781 | 0.5312 |
| 0.1675 | 26.0 | 1040 | 2.0097 | 0.5062 |
| 0.1675 | 27.0 | 1080 | 1.8897 | 0.5188 |
| 0.1675 | 28.0 | 1120 | 1.8845 | 0.5188 |
| 0.1675 | 29.0 | 1160 | 1.9099 | 0.5312 |
| 0.1675 | 30.0 | 1200 | 1.9492 | 0.5312 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
p1atdev/siglip-tagger-test-3
|
# siglip-tagger-test-3
This model is a fine-tuned version of [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 692.4745
- Accuracy: 0.3465
- F1: 0.9969
## Model description
This model is an experimental model that predicts danbooru tags of images.
## Example
### Use a pipeline
```py
from transformers import pipeline
pipe = pipeline("image-classification", model="p1atdev/siglip-tagger-test-3", trust_remote_code=True)
pipe(
"image.jpg", # takes str(path) or numpy array or PIL images as input
threshold=0.5, #optional parameter defaults to 0
return_scores = False #optional parameter defaults to False
)
```
* `threshold`: confidence intervale, if it's specified, the pipeline will only return tags with a confidence >= threshold
* `return_scores`: if specified the pipeline will return the labels and their confidences in a dictionary format.
### Load model directly
```py
from PIL import Image
import torch
from transformers import (
AutoModelForImageClassification,
AutoImageProcessor,
)
import numpy as np
MODEL_NAME = "p1atdev/siglip-tagger-test-3"
model = AutoModelForImageClassification.from_pretrained(
MODEL_NAME, torch_dtype=torch.bfloat16, trust_remote_code=True
)
model.eval()
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
image = Image.open("sample.jpg") # load your image
inputs = processor(image, return_tensors="pt").to(model.device, model.dtype)
logits = model(**inputs).logits.detach().cpu().float()[0]
logits = np.clip(logits, 0.0, 1.0)
results = {
model.config.id2label[i]: logit for i, logit in enumerate(logits) if logit > 0
}
results = sorted(results.items(), key=lambda x: x[1], reverse=True)
for tag, score in results:
print(f"{tag}: {score*100:.2f}%")
```
## Intended uses & limitations
This model is for research use only and is not recommended for production.
Please use wd-v1-4-tagger series by SmilingWolf:
- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2)
etc.
## Training and evaluation data
High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500. (Same as p1atdev/siglip-tagger-test-2)
|Name|Description|
|-|-|
|Images count|5000|
|Supported tags|9517 general tags. Character and rating tags are not included. See all labels in [config.json](config.json)|
|Image rating|4000 for `general` and 1000 for `sensitive,questionable,explicit`|
|Copyright tags|`original` only|
|Image score range (on search)|min:10, max150|
## Training procedure
- Loss function: AsymmetricLossOptimized ([Asymmetric Loss](https://github.com/Alibaba-MIIL/ASL))
- `gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False`
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1066.981 | 1.0 | 71 | 1873.5417 | 0.1412 | 0.9939 |
| 547.3158 | 2.0 | 142 | 934.3269 | 0.1904 | 0.9964 |
| 534.6942 | 3.0 | 213 | 814.0771 | 0.2170 | 0.9966 |
| 414.1278 | 4.0 | 284 | 774.0230 | 0.2398 | 0.9967 |
| 365.4994 | 5.0 | 355 | 751.2046 | 0.2459 | 0.9967 |
| 352.3663 | 6.0 | 426 | 735.6580 | 0.2610 | 0.9967 |
| 414.3976 | 7.0 | 497 | 723.2065 | 0.2684 | 0.9968 |
| 350.8201 | 8.0 | 568 | 714.0453 | 0.2788 | 0.9968 |
| 364.5016 | 9.0 | 639 | 706.5261 | 0.2890 | 0.9968 |
| 309.1184 | 10.0 | 710 | 700.7808 | 0.2933 | 0.9968 |
| 288.5186 | 11.0 | 781 | 695.7027 | 0.3008 | 0.9968 |
| 287.4452 | 12.0 | 852 | 691.5306 | 0.3037 | 0.9968 |
| 280.9088 | 13.0 | 923 | 688.8063 | 0.3084 | 0.9969 |
| 296.8389 | 14.0 | 994 | 686.1077 | 0.3132 | 0.9968 |
| 265.1467 | 15.0 | 1065 | 683.7382 | 0.3167 | 0.9969 |
| 268.5263 | 16.0 | 1136 | 682.1683 | 0.3206 | 0.9969 |
| 309.7871 | 17.0 | 1207 | 681.1995 | 0.3199 | 0.9969 |
| 307.6475 | 18.0 | 1278 | 680.1700 | 0.3230 | 0.9969 |
| 262.0677 | 19.0 | 1349 | 679.2177 | 0.3270 | 0.9969 |
| 275.3823 | 20.0 | 1420 | 678.9730 | 0.3294 | 0.9969 |
| 273.984 | 21.0 | 1491 | 678.6031 | 0.3318 | 0.9969 |
| 273.5361 | 22.0 | 1562 | 678.1285 | 0.3332 | 0.9969 |
| 279.6474 | 23.0 | 1633 | 678.4264 | 0.3348 | 0.9969 |
| 232.5045 | 24.0 | 1704 | 678.3773 | 0.3357 | 0.9969 |
| 269.621 | 25.0 | 1775 | 678.4922 | 0.3372 | 0.9969 |
| 289.8389 | 26.0 | 1846 | 679.0094 | 0.3397 | 0.9969 |
| 256.7373 | 27.0 | 1917 | 679.5618 | 0.3407 | 0.9969 |
| 262.3969 | 28.0 | 1988 | 680.1168 | 0.3414 | 0.9969 |
| 266.2439 | 29.0 | 2059 | 681.0101 | 0.3421 | 0.9969 |
| 247.7932 | 30.0 | 2130 | 681.9800 | 0.3422 | 0.9969 |
| 246.8083 | 31.0 | 2201 | 682.8550 | 0.3416 | 0.9969 |
| 270.827 | 32.0 | 2272 | 683.9250 | 0.3434 | 0.9969 |
| 256.4384 | 33.0 | 2343 | 685.0451 | 0.3448 | 0.9969 |
| 270.461 | 34.0 | 2414 | 686.2427 | 0.3439 | 0.9969 |
| 253.8104 | 35.0 | 2485 | 687.4274 | 0.3441 | 0.9969 |
| 265.532 | 36.0 | 2556 | 688.4856 | 0.3451 | 0.9969 |
| 249.1426 | 37.0 | 2627 | 689.5027 | 0.3457 | 0.9969 |
| 229.5651 | 38.0 | 2698 | 690.4455 | 0.3455 | 0.9969 |
| 251.9008 | 39.0 | 2769 | 691.2324 | 0.3463 | 0.9969 |
| 281.8228 | 40.0 | 2840 | 691.7993 | 0.3464 | 0.9969 |
| 242.5272 | 41.0 | 2911 | 692.1788 | 0.3465 | 0.9969 |
| 229.5605 | 42.0 | 2982 | 692.3799 | 0.3465 | 0.9969 |
| 245.0876 | 43.0 | 3053 | 692.4745 | 0.3465 | 0.9969 |
| 271.22 | 44.0 | 3124 | 692.5084 | 0.3465 | 0.9969 |
| 244.3045 | 45.0 | 3195 | 692.5108 | 0.3465 | 0.9969 |
| 243.9542 | 46.0 | 3266 | 692.5128 | 0.3465 | 0.9969 |
| 274.6664 | 47.0 | 3337 | 692.5095 | 0.3465 | 0.9969 |
| 231.1361 | 48.0 | 3408 | 692.5107 | 0.3465 | 0.9969 |
| 274.5513 | 49.0 | 3479 | 692.5108 | 0.3465 | 0.9969 |
| 316.0833 | 50.0 | 3550 | 692.5107 | 0.3465 | 0.9969 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"!",
"!!",
"!?",
"+++",
"+_+",
"...",
"...?",
"0_0",
"0w0",
"101st airborne",
"1945",
"1980s (style)",
"1990s (style)",
"19vodnik",
"1boy",
"1girl",
"1koma",
"1other",
"1st infantry division (us army)",
"2000s (style)",
"2010s (style)",
"2018",
"2019",
"2021",
"2022",
"2023",
"2023 titan submersible incident",
"2boys",
"2girls",
"2koma",
"2others",
"3.7 cm pak 36",
"300 blackout",
"3:",
"3boys",
"3d",
"3d background",
"3d printer",
"3girls",
"3koma",
"3others",
"4boys",
"4girls",
"4koma",
"4others",
"4shi",
"5boys",
"5girls",
"6+boys",
"6+girls",
"6+others",
"666",
"69",
"88 flak",
"9a-91 (girls' frontline) (cosplay)",
":/",
":3",
":<",
":>",
":>=",
":d",
":i",
":o",
":p",
":q",
":s",
":t",
":|",
";)",
";d",
";i",
";o",
";p",
"<o>_<o>",
"<|>_<|>",
"=_=",
">:(",
">:)",
">_<",
">_o",
">o<",
"?",
"??",
"@_@",
"\\m/",
"\\||/",
"^^^",
"^_^",
"^o^",
"a-10 thunderbolt ii",
"a6m zero",
"aa-12",
"abandoned",
"abduction",
"above clouds",
"abs",
"abstract",
"abstract background",
"absurdly long hair",
"accident",
"accidental exposure",
"accordion",
"ace (playing card)",
"ace of spades",
"acoustic guitar",
"acrobatics",
"action figure",
"ad",
"adam's apple",
"adapted costume",
"adapted uniform",
"adidas",
"adjusting another's clothes",
"adjusting apron",
"adjusting clothes",
"adjusting collar",
"adjusting earphones",
"adjusting eyewear",
"adjusting footwear",
"adjusting gloves",
"adjusting hair",
"adjusting headwear",
"adjusting hood",
"adjusting legwear",
"adjusting necktie",
"adjusting panties",
"adjusting scarf",
"adjusting shoe",
"adjusting shorts",
"adjusting strap",
"adjusting swimsuit",
"adobe acrobat",
"adobe after effects",
"adobe bridge",
"adobe photoshop",
"adrian helmet",
"adult baby",
"aegyo sal",
"aerial bomb",
"aerovity",
"afdian username",
"affectionate",
"afloat",
"afro",
"after anal",
"after bathing",
"after battle",
"after cunnilingus",
"after ejaculation",
"after fellatio",
"after handjob",
"after kiss",
"after masturbation",
"after paizuri",
"after rain",
"after sex",
"after vaginal",
"afterimage",
"against bookshelf",
"against door",
"against glass",
"against pillar",
"against railing",
"against tree",
"against wall",
"agave",
"age comparison",
"age conscious",
"age difference",
"age progression",
"aged down",
"aged up",
"agent 47 (cosplay)",
"agm-65 maverick",
"ah eto... bleh (meme)",
"ah-64 apache",
"ahegao",
"ahoge",
"ahoge wag",
"ai ai gasa",
"ai drawing anime characters eating ramen (meme)",
"ai-generated art (topic)",
"aiguillette",
"aim-120 amraam",
"aim-7 sparrow",
"aiming",
"aiming at viewer",
"aimpoint",
"air bubble",
"air conditioner",
"air freshener",
"air pods",
"airborne",
"aircraft",
"aircraft carrier",
"airfield",
"airplane",
"airplane interior",
"airplane wing",
"airport",
"airship",
"airsoft",
"ajirogasa",
"ak 5",
"ak-103",
"ak-105",
"ak-12",
"ak-47",
"ak-74",
"ak-74m",
"akabeko",
"akagi (aircraft carrier)",
"akg",
"akg k-series headphones",
"akira movie poster",
"akm",
"akms",
"aks-74",
"aks-74u",
"al rihla",
"alarm clock",
"alarm siren",
"albino",
"album cover",
"alchemy",
"alcohol",
"alice gear",
"alien",
"all fours",
"alley",
"alligator",
"alphabet",
"alraune",
"altar",
"alternate breast size",
"alternate color",
"alternate costume",
"alternate flag color",
"alternate form",
"alternate hair color",
"alternate hair length",
"alternate hairstyle",
"alternate size",
"alternate skin color",
"altyn helmet",
"amagi shino",
"amazake (drink)",
"ambiguous gender",
"ambiguous red liquid",
"amd ryzen",
"american flag",
"american football (object)",
"ammunition",
"ammunition belt",
"ammunition box",
"ammunition pouch",
"amnesia",
"among us drip (meme)",
"among us eyes (meme)",
"amor",
"amplifier",
"amputee",
"amusement park",
"an-22",
"anachronism",
"anal",
"anal beads",
"anal fingering",
"anal fisting",
"anal fluid",
"anal invitation",
"anal object insertion",
"anal only",
"anal tail",
"analog clock",
"anato finnstark",
"anatomical nonsense",
"anatomy",
"anchor",
"anchor hair ornament",
"anchor symbol",
"ancient egyptian hat",
"androgynous",
"android",
"angel",
"angel and devil",
"angel wings",
"anger vein",
"angle grinder",
"angled foregrip",
"angry",
"anilingus",
"animal",
"animal bag",
"animal collar",
"animal costume",
"animal ear fluff",
"animal ear headphones",
"animal ear headwear",
"animal ear legwear",
"animal ears",
"animal ears helmet",
"animal feet",
"animal focus",
"animal hair ornament",
"animal hands",
"animal hat",
"animal hood",
"animal hug",
"animal nose",
"animal on chest",
"animal on hand",
"animal on head",
"animal on lap",
"animal on shoulder",
"animal penis",
"animal print",
"animal skull",
"animal slippers",
"animal with human ears",
"animal-themed vehicle",
"animalization",
"anime girl hiding from a terminator (meme)",
"animification",
"ankh",
"ankh necklace",
"ankha zone (meme)",
"ankle boots",
"ankle cuffs",
"ankle grab",
"ankle lace-up",
"ankle ribbon",
"ankle socks",
"anklet",
"anniversary",
"annoyed",
"ant",
"ant girl",
"antenna hair",
"antennae",
"anti-aircraft",
"anti-aircraft gun",
"anti-aircraft missile",
"anti-materiel rifle",
"anti-tank grenade",
"anti-tank gun",
"anti-tank mine",
"anti-war",
"antique cannon",
"antique firearm",
"antlers",
"antonov an-225",
"anus",
"anus peek",
"anvil",
"anya's heh face (meme)",
"apartment",
"aphid",
"aphid girl",
"apocalypse",
"apollo lunar module",
"apple",
"apple earrings",
"apple watch",
"applying bandages",
"applying makeup",
"april",
"april fools",
"apron",
"apron aside",
"apron hold",
"apron lift",
"apron tug",
"aqua background",
"aqua bow",
"aqua bowtie",
"aqua bra",
"aqua dress",
"aqua eyes",
"aqua gloves",
"aqua hair",
"aqua jacket",
"aqua nails",
"aqua necktie",
"aqua panties",
"aqua pants",
"aqua pupils",
"aqua ribbon",
"aqua sailor collar",
"aqua shirt",
"aqua shorts",
"aqua skin",
"aqua skirt",
"aqua theme",
"aquarium",
"aquarius (constellation)",
"aquarius (symbol)",
"aquarius (zodiac)",
"aqueduct",
"ar-15",
"arabic text",
"arachne",
"aran sweater",
"arc de triomphe",
"arcade",
"arcade cabinet",
"arch",
"arched back",
"archery",
"architecture",
"are you winning son? (meme)",
"areola slip",
"areolae",
"argentina",
"argentinian flag",
"argyle",
"argyle background",
"argyle legwear",
"argyle pantyhose",
"argyle sweater",
"arm armor",
"arm around shoulder",
"arm around waist",
"arm at side",
"arm behind back",
"arm behind head",
"arm behind leg",
"arm belt",
"arm between breasts",
"arm between legs",
"arm blade",
"arm cannon",
"arm garter",
"arm grab",
"arm guards",
"arm hair",
"arm held back",
"arm hug",
"arm mounted weapon",
"arm on knee",
"arm pillow",
"arm rest",
"arm ribbon",
"arm shield",
"arm strap",
"arm support",
"arm tattoo",
"arm under breasts",
"arm up",
"arm warmers",
"arm wrap",
"armband",
"armchair",
"armillary sphere",
"armlet",
"armor",
"armor under clothes",
"armored bodysuit",
"armored boots",
"armored dress",
"armored gloves",
"armored personnel carrier",
"armored vehicle",
"armpit crease",
"armpit cutout",
"armpit peek",
"armpits",
"arms around neck",
"arms around waist",
"arms at sides",
"arms behind back",
"arms behind head",
"arms between legs",
"arms under breasts",
"arms up",
"army",
"around corner",
"aroused",
"arrest",
"arrow (projectile)",
"arrow (symbol)",
"arrow hair ornament",
"art brush",
"art gallery",
"art shift",
"arthropod",
"arthropod girl",
"arthropod limbs",
"artificial eye",
"artificial vagina",
"artificial vagina with body",
"artillery",
"artillery shell",
"artist logo",
"artist name",
"artist self-insert",
"artist self-reference",
"artistic error",
"artoria pendragon (swimsuit ruler) (fate) (cosplay)",
"artstation username",
"arx-160",
"asa no ha (pattern)",
"asahi breweries",
"ascot",
"ashes",
"ashiyu",
"ashtray",
"asian",
"asmr",
"asphyxiation",
"ass",
"ass focus",
"ass freckles",
"ass grab",
"ass press",
"ass ripple",
"ass support",
"ass tattoo",
"ass visible through thighs",
"ass-to-ass",
"assault rifle",
"assault visor",
"assembling",
"assertive female",
"assisted exposure",
"assisted stretching",
"asteroid",
"asteroid ill",
"astronaut",
"astronomical clock",
"asymmetrical armor",
"asymmetrical arms",
"asymmetrical bangs",
"asymmetrical clothes",
"asymmetrical docking",
"asymmetrical gloves",
"asymmetrical hair",
"asymmetrical irises",
"asymmetrical legwear",
"asymmetrical limbs",
"asymmetrical sleeves",
"at computer",
"at knifepoint",
"athletic leotard",
"attack",
"au ra",
"audience",
"audio jack",
"aunt and nephew",
"aunt and niece",
"aura",
"aurora",
"austria",
"austria-hungary",
"austro-hungarian army",
"autoarousal",
"autocannon",
"autofacial",
"autopenetration",
"autumn",
"autumn leaves",
"averting eyes",
"aviator sunglasses",
"awkward",
"awning",
"axe",
"axolotl",
"azazel (helltaker) (cosplay)",
"azula (cosplay)",
"b-2 spirit",
"bababooey",
"baby",
"baby bottle",
"babydoll",
"babywearing",
"back",
"back bow",
"back cutout",
"back focus",
"back hair",
"back slit",
"back tattoo",
"back-to-back",
"backboob",
"backless dress",
"backless outfit",
"backless panties",
"backlighting",
"backpack",
"backpack basket",
"backwards hat",
"bacon",
"bacteria",
"bad apple!!",
"bad end",
"bad food",
"bad gun anatomy",
"bad hands",
"bad perspective",
"bad proportions",
"bad reflection",
"badge",
"bag",
"bag charm",
"bag of chips",
"bag on lap",
"baggy clothes",
"baggy pants",
"bagpipes",
"bags under eyes",
"bagua",
"baguette",
"bait and switch",
"bakery",
"balaclava",
"balance scale",
"balance scale print",
"balancing",
"balcony",
"bald",
"balding",
"balisong",
"ball",
"ball and chain restraint",
"ball gag",
"ballerina",
"ballet",
"ballet slippers",
"ballistic shield",
"balloon",
"bamboo",
"bamboo broom",
"bamboo forest",
"bamboo screen",
"bamboo steamer",
"banana",
"banana peel",
"band shirt",
"bandage on face",
"bandage over one eye",
"bandaged ankle",
"bandaged arm",
"bandaged ear",
"bandaged fingers",
"bandaged foot",
"bandaged hand",
"bandaged head",
"bandaged leg",
"bandaged wrist",
"bandages",
"bandaid",
"bandaid hair ornament",
"bandaid on ahoge",
"bandaid on arm",
"bandaid on cheek",
"bandaid on face",
"bandaid on foot",
"bandaid on hand",
"bandaid on head",
"bandaid on knee",
"bandaid on leg",
"bandaid on neck",
"bandaid on nose",
"bandaid on pussy",
"bandaid on thigh",
"bandaids on nipples",
"bandana",
"bandolier",
"bangle",
"bangs pinned back",
"banister",
"banknote",
"banner",
"baozi",
"bar (place)",
"bar censor",
"bar stool",
"barbed wire",
"barbell",
"barbell piercing",
"barcode",
"barcode scanner",
"barcode tattoo",
"barding",
"bare arms",
"bare back",
"bare bush",
"bare hips",
"bare legs",
"bare shoulders",
"bare tree",
"barefoot",
"barefoot sandals (jewelry)",
"barista",
"barking",
"barn",
"barre",
"barrel",
"barrel shroud",
"barrett m82",
"barrett mrad",
"bars",
"bartender",
"baseball",
"baseball bat",
"baseball cap",
"baseball mitt",
"baseball stadium",
"baseball uniform",
"bashlik",
"basket",
"basket hilt",
"basketball",
"basketball (object)",
"basketball court",
"basketball hoop",
"basketball jersey",
"basketball uniform",
"bass guitar",
"bassinet",
"bat (animal)",
"bat bowtie",
"bat ears",
"bat girl",
"bat hair ornament",
"bat ornament",
"bat wings",
"bath",
"bath stool",
"bath yukata",
"bathing",
"bathrobe",
"bathroom",
"bathroom scale",
"bathtub",
"baton (weapon)",
"battery indicator",
"battle",
"battle axe",
"battle belt",
"battle damage",
"battle of berlin",
"battle of britain",
"battle of midway",
"battle rifle",
"battle standard",
"battlefield",
"battleship",
"baumkuchen",
"bayonet",
"bdsm",
"beach",
"beach mat",
"beach towel",
"beach umbrella",
"beach volleyball",
"beachball",
"beached",
"bead bracelet",
"bead necklace",
"beads",
"beak",
"beak hold",
"beaker",
"beam rifle",
"beamed eighth notes",
"beanie",
"beans",
"bear",
"bear ears",
"bear girl",
"bear hat",
"bear print",
"beard",
"beauty treatment",
"beckoning",
"bed",
"bed frame",
"bed invitation",
"bed sheet",
"bedroom",
"bedwetting",
"bee",
"bee girl",
"bee wings",
"beehive",
"beer",
"beer bottle",
"beer can",
"beer glass",
"beer mug",
"beetle",
"before and after",
"behelit",
"behind another",
"beige background",
"beige cardigan",
"beige jacket",
"beige sweater",
"belgium",
"bell",
"bell tower",
"belly",
"belly grab",
"belly poke",
"belly rub",
"belt",
"belt boots",
"belt buckle",
"belt chain",
"belt choker",
"belt collar",
"belt pouch",
"bench",
"bending",
"bendy straw",
"benelli m4",
"bent over",
"bentley",
"bentley continental gt",
"bento",
"beret",
"beretta 92",
"beretta px4",
"berlin",
"bernese mountain dog",
"berry",
"bespectacled",
"bestiality",
"between breasts",
"between fingers",
"between legs",
"between toes",
"bevor",
"bf 109",
"bib",
"biblically accurate angel",
"biceps",
"bicorne",
"bicycle",
"bicycle basket",
"bicycle helmet",
"big belly",
"big enough (meme)",
"big hair",
"big mac",
"big nose",
"bike shorts",
"bike shorts under skirt",
"bikini",
"bikini armor",
"bikini around one leg",
"bikini bottom aside",
"bikini bottom only",
"bikini pull",
"bikini skirt",
"bikini tan",
"bikini top lift",
"bikini top only",
"bikini under clothes",
"bilingual",
"billboard",
"binaural microphone",
"binder",
"binoculars",
"biohazard symbol",
"biology",
"bioluminescence",
"biopunk",
"biplane",
"bipod",
"birch tree",
"bird",
"bird hair ornament",
"bird legs",
"bird on hand",
"bird on head",
"bird on shoulder",
"bird print",
"bird shadow puppet",
"bird tail",
"bird wings",
"birdcage",
"birthday",
"birthday cake",
"birthday party",
"bisected",
"bisexual female",
"bisexual flag",
"bisexual male",
"bit gag",
"bite mark",
"biting",
"biting another's finger",
"biting own lip",
"black apron",
"black arm warmers",
"black armband",
"black armor",
"black ascot",
"black background",
"black bag",
"black belt",
"black bikini",
"black bird",
"black blindfold",
"black bodysuit",
"black border",
"black bow",
"black bowtie",
"black bra",
"black bracelet",
"black bridal gauntlets",
"black buruma",
"black camisole",
"black cape",
"black capelet",
"black cardigan",
"black cat",
"black choker",
"black cloak",
"black coat",
"black collar",
"black corset",
"black dress",
"black eyeliner",
"black eyes",
"black flower",
"black footwear",
"black fur",
"black garter belt",
"black garter straps",
"black gloves",
"black hair",
"black hairband",
"black hakama",
"black halo",
"black headband",
"black headwear",
"black hole",
"black hood",
"black hoodie",
"black horns",
"black jacket",
"black kimono",
"black leggings",
"black legwear",
"black leotard",
"black lips",
"black mask",
"black mittens",
"black mouth",
"black nails",
"black neckerchief",
"black necktie",
"black neckwear",
"black one-piece swimsuit",
"black overalls",
"black pajamas",
"black panties",
"black pants",
"black pantyhose",
"black pasties",
"black pubic hair",
"black ribbon",
"black robe",
"black rose",
"black sailor collar",
"black sash",
"black scarf",
"black sclera",
"black scrunchie",
"black serafuku",
"black shirt",
"black shorts",
"black skin",
"black skirt",
"black sky",
"black sleeves",
"black snake",
"black socks",
"black sports bra",
"black suit",
"black swan (bird)",
"black sweater",
"black sweater vest",
"black tail",
"black tank top",
"black tears",
"black theme",
"black thighhighs",
"black umbrella",
"black vest",
"black vs white",
"black wetsuit",
"black wings",
"black wristband",
"black-framed eyewear",
"blacked (phrase)",
"blackmail",
"blacksmith",
"blank censor",
"blank eyes",
"blank speech bubble",
"blank stare",
"blanket",
"blanket hug",
"blazer",
"blender logo",
"blind",
"blindfold",
"bliss (image)",
"blizzard",
"blob",
"blob (google)",
"block (object)",
"blocking",
"blonde hair",
"blonde pubic hair",
"blood",
"blood bag",
"blood from eyes",
"blood from mouth",
"blood on arm",
"blood on clothes",
"blood on dress",
"blood on face",
"blood on gloves",
"blood on ground",
"blood on hands",
"blood on knife",
"blood on wall",
"blood on weapon",
"blood splatter",
"blood stain",
"blood trail",
"blood type",
"bloodshot eyes",
"bloom",
"bloomers",
"blouse",
"blowgun",
"blowing smoke",
"blue anus",
"blue apron",
"blue armband",
"blue armor",
"blue ascot",
"blue background",
"blue bag",
"blue belt",
"blue bikini",
"blue bird",
"blue bow",
"blue bowtie",
"blue bra",
"blue bracelet",
"blue buruma",
"blue butterfly",
"blue cape",
"blue capelet",
"blue cardigan",
"blue choker",
"blue cloak",
"blue coat",
"blue collar",
"blue corset",
"blue dress",
"blue eyes",
"blue eyeshadow",
"blue feathers",
"blue flower",
"blue footwear",
"blue fur",
"blue gemstone",
"blue gloves",
"blue hair",
"blue hairband",
"blue hakama",
"blue headband",
"blue headwear",
"blue hoodie",
"blue horns",
"blue jacket",
"blue kimono",
"blue leggings",
"blue legwear",
"blue leotard",
"blue lips",
"blue lipstick tube",
"blue mask",
"blue nails",
"blue neckerchief",
"blue necktie",
"blue neckwear",
"blue one-piece swimsuit",
"blue outline",
"blue overalls",
"blue pajamas",
"blue panties",
"blue pants",
"blue pantyhose",
"blue pupils",
"blue ribbon",
"blue robe",
"blue rose",
"blue sailor collar",
"blue sash",
"blue scarf",
"blue sclera",
"blue screen of death",
"blue scrunchie",
"blue serafuku",
"blue shirt",
"blue shorts",
"blue skin",
"blue skirt",
"blue sky",
"blue sleeves",
"blue socks",
"blue sports bra",
"blue stripes",
"blue suit",
"blue sweater",
"blue tabard",
"blue tank top",
"blue theme",
"blue thighhighs",
"blue tongue",
"blue track suit",
"blue vest",
"blue-framed eyewear",
"blue-tinted eyewear",
"bluebell (flower)",
"blueberry",
"blunt bangs",
"blunt ends",
"blur censor",
"blurry",
"blurry background",
"blurry foreground",
"blurry vision",
"blush",
"blush stickers",
"blush visible through hair",
"bmw",
"boar",
"board eraser",
"board game",
"boat",
"bob cut",
"bobby pin",
"bobby socks",
"body armor",
"body blush",
"body cam",
"body control",
"body freckles",
"body fur",
"body markings",
"body pillow",
"body writing",
"bodypaint",
"bodystocking",
"bodysuit",
"bodysuit under clothes",
"boeing bird of prey",
"boeing x-32",
"boeing x-45",
"bofors 40 mm gun",
"bokeh",
"bold and brash (spongebob squarepants)",
"bolt",
"bolt action",
"bolter",
"bomb",
"bomb suit",
"bomber",
"bomber jacket",
"bondage",
"bondage gear",
"bondage mask",
"bone",
"bone hair ornament",
"boned meat",
"bonk",
"bonnet",
"boobplate",
"book",
"book on head",
"book on lap",
"book stack",
"bookbag",
"bookend",
"booklet",
"bookmark",
"bookshelf",
"boom barrier",
"boombox",
"boot knife",
"booth seating",
"boots",
"border",
"borrowed character",
"borrowed clothes",
"borrowed weapon",
"bottle",
"bottle to cheek",
"bottomless",
"bougu",
"bouncing ass",
"bouncing breasts",
"bound",
"bound ankles",
"bound arms",
"bound legs",
"bound thighs",
"bound together",
"bound torso",
"bound wrists",
"bouquet",
"boustrophedon order",
"bow",
"bow (music)",
"bow (weapon)",
"bow bikini",
"bow bra",
"bow choker",
"bow earrings",
"bow hairband",
"bow legwear",
"bow panties",
"bowing",
"bowl",
"bowl cut",
"bowl hat",
"bowlegged pose",
"bowtie",
"box",
"box of chocolates",
"boxcutter",
"boxer briefs",
"boy on top",
"bra",
"bra lift",
"bra peek",
"bra pull",
"bra strap",
"bra visible through clothes",
"bracelet",
"bracer",
"braces",
"braid",
"braided bangs",
"braided beard",
"braided bun",
"braided ponytail",
"braiding hair",
"bralines",
"branch",
"brand name imitation",
"brass knuckles",
"brassard",
"brazier",
"bread",
"bread bun",
"bread slice",
"breakfast",
"breaking",
"breast conscious",
"breast curtains",
"breast cutout",
"breast envy",
"breast expansion",
"breast focus",
"breast hold",
"breast lift",
"breast milk",
"breast pillow",
"breast pocket",
"breast press",
"breast pull",
"breast pump",
"breast rest",
"breast smother",
"breast strap",
"breast sucking",
"breast tattoo",
"breastfeeding",
"breastless clothes",
"breastplate",
"breasts",
"breasts apart",
"breasts on another's back",
"breasts on glass",
"breasts on head",
"breasts on table",
"breasts out",
"breasts squeezed together",
"breaststrap (saddle)",
"breath",
"breathing fire",
"breeding mount",
"brick",
"brick floor",
"brick road",
"brick wall",
"bridal garter",
"bridal gauntlets",
"bridal veil",
"bride",
"bridge",
"briefcase",
"brigandine (armor)",
"bright pupils",
"british army",
"brn-180",
"broad shoulders",
"broccoli",
"brodie helmet",
"broken",
"broken chain",
"broken cup",
"broken glass",
"broken heart",
"broken horn",
"broken mirror",
"broken neck",
"broken shield",
"broken sword",
"broken umbrella",
"broken wall",
"broken weapon",
"broken window",
"brooch",
"broom",
"broom riding",
"brother and sister",
"brown apron",
"brown background",
"brown bag",
"brown belt",
"brown border",
"brown bow",
"brown bowtie",
"brown bra",
"brown cape",
"brown capelet",
"brown cardigan",
"brown cat",
"brown choker",
"brown cloak",
"brown coat",
"brown collar",
"brown dress",
"brown eyes",
"brown facial hair",
"brown feathers",
"brown flower",
"brown footwear",
"brown fur",
"brown gloves",
"brown hair",
"brown hairband",
"brown headwear",
"brown hoodie",
"brown horns",
"brown jacket",
"brown legwear",
"brown lips",
"brown mittens",
"brown nails",
"brown necktie",
"brown panties",
"brown pants",
"brown pantyhose",
"brown poncho",
"brown pubic hair",
"brown ribbon",
"brown robe",
"brown sailor collar",
"brown scarf",
"brown scrunchie",
"brown shirt",
"brown shorts",
"brown skirt",
"brown sky",
"brown sleeves",
"brown socks",
"brown sweater",
"brown sweater vest",
"brown tank top",
"brown theme",
"brown thighhighs",
"brown vest",
"brown wings",
"brown-framed eyewear",
"browning auto 5",
"browning m1919",
"browning m2",
"bruise",
"bruise on face",
"brush",
"brush stroke",
"brushing hair",
"brushing own hair",
"brushing teeth",
"btr",
"btr-4 bucephalus",
"btr-80",
"bubble",
"bubble bath",
"bubble blowing",
"bubble tea",
"buck teeth",
"bucket",
"bucket of water",
"bucket on head",
"bucket-wheel excavator",
"buckle",
"buckler",
"budenovka",
"bug",
"bug spray",
"bugatti",
"bugatti type 57 sc atlantic",
"building",
"bukkake",
"bulgaria",
"bulge",
"bulge press",
"bulges touching",
"bullet",
"bulletin board",
"bulletproof vest",
"bullpup",
"bullying",
"bun cover",
"bunching hair",
"bundesheer (austria)",
"bundeswehr",
"bunk bed",
"bunker",
"buran (spacecraft)",
"burger",
"burglar",
"burn scar",
"burning",
"burning building",
"burnt",
"burnt clothes",
"burnt food",
"bursting ass",
"bursting breasts",
"buruma",
"buruma around one leg",
"buruma pull",
"bus",
"bus interior",
"bus stop",
"bush",
"business suit",
"bust (sculpture)",
"buster sword",
"bustier",
"butler",
"butt crack",
"butt plug",
"butter",
"butterfly",
"butterfly choker",
"butterfly earrings",
"butterfly hair ornament",
"butterfly hat ornament",
"butterfly on hand",
"butterfly on head",
"butterfly ornament",
"butterfly print",
"butterfly tattoo",
"buttjob",
"buttjob over clothes",
"buttjob under clothes",
"button badge",
"button eyes",
"button gap",
"buttoned cuffs",
"buttons",
"buying condoms",
"c-string",
"c:",
"cabbage",
"cabbie hat",
"cabin",
"cabinet",
"cable",
"cable knit",
"cable tail",
"cable tie",
"cactus",
"cafe",
"cafeteria",
"cage",
"cake",
"cake slice",
"calculator",
"calendar (medium)",
"calendar (object)",
"calico",
"call an ambulance but not for me (meme)",
"calla lily",
"calligraphy",
"calligraphy brush",
"calling",
"camcorder",
"camellia",
"cameltoe",
"camembert (headgear)",
"cameo",
"camera",
"camisole",
"camouflage",
"camouflage coat",
"camouflage headwear",
"camouflage jacket",
"camouflage paint",
"camouflage pants",
"camouflage shirt",
"campaign hat",
"campbell's",
"camper",
"can",
"canada",
"canadian army",
"canadian flag",
"canal",
"candle",
"candlelight",
"candlestand",
"candy",
"candy apple",
"candy heart",
"candy store",
"cane",
"canned coffee",
"canned food",
"cannon",
"canon (company)",
"canopy (aircraft)",
"canopy bed",
"canteen",
"canvas (object)",
"cape",
"capelet",
"caplock",
"cappello alpino",
"capri pants",
"capsule corp",
"captcha",
"captured",
"car",
"car crash",
"car interior",
"car keys",
"carabiner",
"caramelldansen",
"carapace",
"carbonara (food)",
"carcano",
"card",
"card (medium)",
"cardboard",
"cardboard box",
"cardigan",
"cardigan around waist",
"cardigan on shoulders",
"cardigan vest",
"cardiogram",
"caressing testicles",
"cargo",
"cargo aircraft",
"cargo pants",
"carl gustaf recoilless rifle",
"carpet",
"carrot",
"carrot hair ornament",
"carrying",
"carrying bag",
"carrying over shoulder",
"carrying overhead",
"carrying person",
"carrying under arm",
"cart",
"carton",
"cartoon bone",
"cartridge",
"carving fork",
"case",
"cash register",
"cashier",
"casing ejection",
"cassette tape",
"cassock",
"cast",
"casting spell",
"castle",
"casual",
"cat",
"cat bag",
"cat boy",
"cat day",
"cat ear headphones",
"cat ear legwear",
"cat ears",
"cat girl",
"cat hair ornament",
"cat hat",
"cat hood",
"cat lingerie",
"cat on head",
"cat on lap",
"cat on shoulder",
"cat ornament",
"cat paw",
"cat paws",
"cat print",
"cat slippers",
"cat tail",
"cat tattoo",
"cat teaser",
"catapult",
"caterpillar tracks",
"cathedral",
"catholic",
"caught",
"cauldron",
"caustics",
"caution",
"cavalier hat",
"cavalry",
"cave",
"cave interior",
"cd",
"cd case",
"ceiling",
"ceiling light",
"cell (biology)",
"cell nucleus",
"cellphone",
"cellphone charm",
"cellphone photo",
"cellular tower",
"celtic",
"censer",
"censored",
"censored nipples",
"censored symbol",
"centaur",
"centauroid",
"center frills",
"center opening",
"center-flap bangs",
"centurii-chan (artist)",
"cephalopod eyes",
"cereal",
"cervix",
"ch-53",
"chain",
"chain earrings",
"chain leash",
"chain necklace",
"chain-link fence",
"chained",
"chained wrists",
"chainmail",
"chainsaw",
"chair",
"chalk",
"chalkboard",
"chalkboard sign",
"champagne flute",
"chandelier",
"chanel",
"chaneque",
"chaps",
"char-siu",
"character age",
"character charm",
"character doll",
"character hair ornament",
"character name",
"character pin",
"character print",
"character profile",
"charger",
"charging device",
"charging forward",
"charm (object)",
"chart",
"chasing",
"chastity belt",
"chastity cage",
"chat log",
"cheating (relationship)",
"checkerboard cookie",
"checkered",
"checkered background",
"checkered clothes",
"checkered floor",
"checkered scarf",
"checkered skirt",
"checkered trim",
"checkered wall",
"cheek bulge",
"cheek pinching",
"cheek poking",
"cheek press",
"cheek rest",
"cheek squash",
"cheek-to-cheek",
"cheering",
"cheerleader",
"cheese",
"cheese vs. cheese (meme)",
"chef",
"chef hat",
"chemise",
"chemistry",
"chemistry set",
"cherry",
"cherry blossom print",
"cherry blossoms",
"cherry print",
"cherub",
"chess",
"chess piece",
"chessboard",
"chest armor",
"chest guard",
"chest hair",
"chest harness",
"chest of drawers",
"chest rig",
"chest tattoo",
"chestnut mouth",
"chevrolet",
"chevrolet nova",
"chevron (symbol)",
"chewing",
"chewing gum",
"chibi",
"chibi inset",
"chicken",
"chicken leg",
"chihaya (clothing)",
"chikaretsu",
"child",
"child carry",
"child's drawing",
"chimerism",
"chimney",
"chin strap",
"china",
"china dress",
"chinese armor",
"chinese clothes",
"chinese empire",
"chinese spoon",
"chinese text",
"chinese zodiac",
"chip star",
"chips (food)",
"chisato and takina kicking each other's butt (meme)",
"chloroplast",
"chocolate",
"chocolate bar",
"chocolate cake",
"chocolate on body",
"chocolate on breasts",
"chocolate on clothes",
"chocolate on head",
"chocolate on legs",
"choke hold",
"choker",
"choker removed",
"chopping",
"chopsticks",
"christianity",
"christmas",
"christmas cake",
"christmas lights",
"christmas ornaments",
"christmas tree",
"christmas tree print",
"chromatic aberration",
"chrysanthemum",
"church",
"cicada",
"cigar",
"cigarette",
"cigarette butt",
"cigarette case",
"cigarette pack",
"circle",
"circlet",
"circuit board",
"circular border",
"circular saw",
"city",
"city lights",
"cityscape",
"clapping",
"clash",
"classroom",
"claw pose",
"claw ring",
"clawed boots",
"clawed gauntlets",
"claws",
"clay",
"claymore (sword)",
"cleaning",
"cleaning brush",
"clear insertion",
"clear sky",
"cleavage",
"cleavage cutout",
"cleavage reach",
"cleaver",
"cleft of venus",
"clenched hand",
"clenched hands",
"clenched teeth",
"cleric",
"cliff",
"climbing wall",
"clip studio paint",
"clipboard",
"clipping nails",
"clitoral hood",
"clitoral stimulation",
"clitoris",
"clitoris piercing",
"cloak",
"clock",
"clock hands",
"clock tower",
"clone trooper",
"close-up",
"closed eyes",
"closed mouth",
"closed umbrella",
"closing door",
"cloth",
"cloth gag",
"clothed animal",
"clothed female nude female",
"clothed female nude male",
"clothed male nude female",
"clothed masturbation",
"clothed robot",
"clothed sex",
"clothes",
"clothes around waist",
"clothes between breasts",
"clothes down",
"clothes dryer",
"clothes grab",
"clothes hanger",
"clothes in front",
"clothes in mouth",
"clothes iron",
"clothes lift",
"clothes on floor",
"clothes on shoulders",
"clothes on/clothes off",
"clothes only",
"clothes pin",
"clothes pull",
"clothes tug",
"clothes writing",
"clothesline",
"clothing aside",
"clothing cutout",
"cloud",
"cloud hair ornament",
"cloud print",
"cloudy sky",
"clover",
"clown",
"club (shape)",
"clubroom",
"clueless",
"clutter",
"coach",
"coat",
"coat of arms",
"coat on shoulders",
"coat partially removed",
"coat tug",
"coattails",
"cobblestone",
"coca-cola",
"cock ring",
"cockade",
"cocking gun",
"cockroach",
"cockroach girl",
"cocktail",
"cocktail dress",
"cocktail flower",
"cocktail glass",
"cocktail shaker",
"coconut tree",
"cocoon",
"coffee",
"coffee cup",
"coffee grinder",
"coffee maker",
"coffee mug",
"coffee pot",
"coffee table",
"coffin",
"coif",
"coiled",
"coin",
"coin on string",
"coke-bottle glasses",
"cola",
"cold",
"cold pack",
"collage",
"collar",
"collar grab",
"collar tabs",
"collarbone",
"collared apron",
"collared cape",
"collared coat",
"collared dress",
"collared jacket",
"collared shirt",
"colonnade",
"color guide",
"colored anus",
"colored eyelashes",
"colored inner hair",
"colored nipples",
"colored pencil",
"colored pencil (medium)",
"colored pubic hair",
"colored sclera",
"colored skin",
"colored text",
"colored tips",
"colored tongue",
"colorful",
"colosseum",
"colt canada c7",
"column",
"comb",
"combat boots",
"combat helmet",
"combat knife",
"combat shirt",
"come hither",
"comet",
"comic",
"comic sans",
"comiket",
"coming out",
"company connection",
"company name",
"comparison",
"competition school swimsuit",
"competition swimsuit",
"completely nude",
"compound eyes",
"computer",
"computer tower",
"computer virus",
"concert",
"condensation",
"condom",
"condom belt",
"condom box",
"condom in mouth",
"condom on nipples",
"condom on penis",
"condom packet strip",
"condom wrapper",
"confession",
"confessional",
"confetti",
"confetti balloon",
"confused",
"conifer",
"conjoined",
"conquistador",
"consensual tentacles",
"console war",
"constellation",
"constricted pupils",
"construction",
"construction site",
"construction worker",
"contemporary",
"content rating",
"contrail",
"contrapposto",
"contrast",
"controller",
"convenience store",
"convenient censoring",
"convenient leg",
"convention",
"convention greeting",
"converse",
"convertible",
"conveyor belt",
"conveyor belt sushi",
"cookie",
"cooking",
"cooking pot",
"cooler",
"cooperative fellatio",
"copyright name",
"copyright notice",
"core",
"cork",
"corked bottle",
"corn dog",
"cornrows",
"corrugated galvanised iron sheet",
"corruption",
"corset",
"corset piercing",
"cosmetics",
"cosmos (flower)",
"cosplay",
"cotton candy",
"cotton swab",
"couch",
"counter",
"country connection",
"couple",
"courtroom",
"cousins",
"couter",
"cover",
"cover image",
"cover page",
"covered abs",
"covered anus",
"covered clitoris",
"covered collarbone",
"covered eyes",
"covered face",
"covered mouth",
"covered navel",
"covered nipples",
"covered penis",
"covered pussy",
"covered testicles",
"covering anus",
"covering body",
"covering breasts",
"covering crotch",
"covering face",
"covering head",
"covering nipples",
"covering one breast",
"covering one eye",
"covering own eyes",
"covering own mouth",
"covering privates",
"cow",
"cow ears",
"cow girl",
"cow horns",
"cow print",
"cow print bikini",
"cow print gloves",
"cow tail",
"cowbell",
"cowboy",
"cowboy boots",
"cowboy hat",
"cowboy shot",
"cowboy western",
"cowering",
"cowgirl (western)",
"cowgirl position",
"cowlick",
"cpu",
"crab",
"crack",
"crack of light",
"cracked floor",
"cracked glass",
"cracked screen",
"cracked skin",
"cracked wall",
"crane (animal)",
"crane (machine)",
"crane game",
"crash landing",
"crate",
"crater",
"crayon",
"crazy",
"crazy eyes",
"crazy smile",
"cream",
"cream on body",
"cream on face",
"cream puff",
"creature",
"creature and personification",
"creature on shoulder",
"creepy eyes",
"crepe",
"crescent",
"crescent earrings",
"crescent hair ornament",
"crescent hat ornament",
"crescent moon",
"crescent pin",
"crescent print",
"crescent tattoo",
"crescent-shaped pupils",
"crest",
"crew neck",
"crime prevention buzzer",
"criss-cross back-straps",
"criss-cross halter",
"crocodile",
"crocodilian",
"crocodilian tail",
"crocs",
"croissant",
"crop circle",
"crop top",
"crop top overhang",
"cropped arms",
"cropped hoodie",
"cropped jacket",
"cropped legs",
"cropped shirt",
"cropped shoulders",
"cropped sweater",
"cropped torso",
"crops",
"croquette",
"cross",
"cross background",
"cross bracelet",
"cross choker",
"cross earrings",
"cross hair ornament",
"cross hat ornament",
"cross moline",
"cross necklace",
"cross ornament",
"cross pasties",
"cross potent",
"cross print",
"cross tie",
"cross-body stretch",
"cross-eyed",
"cross-laced bikini",
"cross-laced clothes",
"cross-laced cutout",
"cross-laced dress",
"cross-laced footwear",
"cross-laced hoodie",
"cross-laced skirt",
"cross-laced sleeves",
"cross-laced slit",
"cross-laced top",
"cross-section",
"cross-shaped pupils",
"crossbow",
"crossbow bolt",
"crossdressing",
"crossed ankles",
"crossed arms",
"crossed bangs",
"crossed fingers",
"crossed legs",
"crosshair",
"crosshair pupils",
"crossover",
"crosswalk",
"crotch",
"crotch cutout",
"crotch rope",
"crotch rub",
"crotch seam",
"crotchless",
"crotchless panties",
"crow",
"crowbar",
"crowd",
"crowded",
"crown",
"crown braid",
"crt",
"crucifix",
"crumbs",
"crumpled paper",
"crushed can",
"crutch",
"crying",
"crying aqua (meme)",
"crying cat (meme)",
"crying emoji",
"crying with eyes open",
"cryptid",
"crystal",
"crystal ball",
"crystal eye",
"cube",
"cubicle",
"cuddling",
"cuffs",
"cuirass",
"cuirassier",
"cuisses",
"cum",
"cum in ass",
"cum in bowl",
"cum in clothes",
"cum in container",
"cum in mouth",
"cum in pussy",
"cum on armpits",
"cum on ass",
"cum on bed",
"cum on body",
"cum on breasts",
"cum on clothes",
"cum on crotch",
"cum on eyewear",
"cum on feet",
"cum on fingers",
"cum on floor",
"cum on gloves",
"cum on hair",
"cum on hands",
"cum on legs",
"cum on legwear",
"cum on male",
"cum on penis",
"cum on pussy",
"cum on self",
"cum on sheets",
"cum on skirt",
"cum on stomach",
"cum on tongue",
"cum on wall",
"cum overflow",
"cum pool",
"cum string",
"cum through clothes",
"cumdrip",
"cumulonimbus cloud",
"cunnilingus",
"cunnilingus gesture",
"cunnilingus through clothes",
"cunt punt",
"cup",
"cup ramen",
"cup size",
"cupboard",
"cupcake",
"cupless bra",
"cupping hands",
"curled fingers",
"curled horns",
"curling iron",
"curly hair",
"curry",
"cursive",
"cursor",
"curtain grab",
"curtains",
"curtsey",
"curved monitor",
"curvy",
"cushion",
"cutoffs",
"cuts",
"cutting board",
"cutting hair",
"cutting own hair",
"cyberpunk",
"cyborg",
"cycle",
"cyclops",
"cynthia (pokemon) (cosplay)",
"cyrillic",
"cz 805 bren",
"cz scorpion evo 3",
"d.va (overwatch) (cosplay)",
"d:",
"daewoo k1",
"daewoo k11",
"daewoo k2",
"dagger",
"daikoku parking area",
"dakimakura (medium)",
"dakimakura (object)",
"damaged",
"dancer",
"dancing",
"dangle earrings",
"dango",
"danmaku comments",
"dappled sunlight",
"dark",
"dark areolae",
"dark aura",
"dark background",
"dark blue hair",
"dark elf",
"dark halo",
"dark nipples",
"dark penis",
"dark persona",
"dark room",
"dark skin",
"dark-skinned female",
"dark-skinned male",
"darkness",
"dated",
"dating",
"dawn",
"day",
"deal with it (meme)",
"death",
"death flag",
"death note (object)",
"debris",
"decade comparison",
"decantering",
"decora",
"deep penetration",
"deep skin",
"deepthroat",
"deer",
"deer ears",
"deer girl",
"deer print",
"defeat",
"defensive wall",
"defloration",
"delinquent",
"delivery",
"demon",
"demon boy",
"demon girl",
"demon horns",
"demon tail",
"demon wings",
"denim",
"denim apron",
"denim jacket",
"denim shorts",
"dental chair",
"dentist",
"depth of field",
"desert",
"desert camouflage",
"desert tech mdr",
"desk",
"desk lamp",
"dessert",
"destroyed",
"destroyer",
"destruction",
"detached collar",
"detached sleeves",
"detached wings",
"determined",
"deutsche bahn",
"deviantart username",
"dew drop",
"diagonal bangs",
"diagonal stripes",
"diagonal-striped bow",
"diagonal-striped bowtie",
"diagonal-striped clothes",
"diagonal-striped necktie",
"diagram",
"dialogue options",
"diamond (gemstone)",
"diamond (shape)",
"diamond cutout",
"diamond earrings",
"diamond mouth",
"diaper",
"diary",
"different reflection",
"different shadow",
"diffraction spikes",
"digital camouflage",
"digital clock",
"digital dissolve",
"digitigrade",
"digivice",
"dilapidated",
"dildo",
"dildo gag",
"dildo reveal",
"dildo riding",
"dildo under mask",
"dimensional hole",
"dimples of venus",
"diner",
"dinner",
"dinosaur",
"dinosaur girl",
"dinosaur tail",
"dior",
"diploma",
"dirndl",
"dirt",
"dirt road",
"dirty",
"dirty clothes",
"dirty face",
"dirty hands",
"disembodied eye",
"disembodied head",
"disembodied limb",
"disembodied penis",
"disgust",
"dishes",
"display",
"disposable camera",
"disposable cup",
"dissolving",
"distortion",
"dive bomber",
"diving mask",
"diving suit",
"dna",
"doberman",
"dock",
"doctor",
"dog",
"dog ears",
"dog girl",
"dog penis",
"dog shadow puppet",
"dog tags",
"dog tail",
"dog-shaped pillow",
"doggystyle",
"doily",
"dojo",
"doll",
"doll joints",
"dollar sign",
"dollhouse view",
"dolphin",
"dolphin print",
"dolphin shorts",
"dome",
"dominatrix",
"don't worry i'm wearing",
"donation box",
"dondurma (ice cream)",
"dongtan dress",
"door",
"door handle",
"doorknob",
"doorway",
"doqute stuffed doll",
"doritos",
"dorsiflexion",
"dot mouth",
"dot nose",
"dou",
"double \\m/",
"double breast sucking",
"double bun",
"double fox shadow puppet",
"double handjob",
"double horizontal stripe",
"double middle finger",
"double penetration",
"double scoop",
"double thumbs up",
"double v",
"double-breasted",
"double-decker hamburger bun",
"double-parted bangs",
"doughnut",
"doujin cover",
"dove",
"down jacket",
"downblouse",
"doyagao",
"dp-27",
"dragging",
"dragon",
"dragon ball (object)",
"dragon boy",
"dragon girl",
"dragon horns",
"dragon on head",
"dragon on shoulder",
"dragon ornament",
"dragon print",
"dragon tail",
"dragon wings",
"dragonfly",
"dragoon",
"dragoon helmet",
"dragunov svd",
"drain (object)",
"drakeposting (meme)",
"dramatic dmitry (meme)",
"drawbridge",
"drawer",
"drawing",
"drawing (object)",
"drawing bow",
"drawing tablet",
"drawn ears",
"drawn whiskers",
"drawstring",
"dream catcher",
"dress",
"dress bow",
"dress lift",
"dress pants",
"dress pull",
"dress shirt",
"dress shoes",
"dress tug",
"dress uniform",
"dressing",
"dressing another",
"dressing room",
"drill",
"drill hair",
"drill hand",
"drill sidelocks",
"drink",
"drink can",
"drinking",
"drinking blood",
"drinking glass",
"drinking horn",
"drinking straw",
"drinking straw in mouth",
"dripping",
"driving",
"drooling",
"drop earrings",
"drop tank",
"dropper",
"dropping",
"drugged",
"drugs",
"drum",
"drum (container)",
"drum magazine",
"drumsticks",
"drunk",
"dry lips",
"dryad",
"drying",
"drying hair",
"dual persona",
"dual sights",
"dual wielding",
"dualshock",
"duck",
"duck earrings",
"duck hair ornament",
"duel",
"duffel bag",
"duffel coat",
"dullahan",
"dumbbell",
"dummy",
"dumpling",
"dungeon",
"dusk",
"dust",
"dust cloud",
"duster",
"dutch angle",
"dvd (object)",
"dvd case",
"dwarf",
"dyed bangs",
"dynamite",
"e.g.o (project moon)",
"eagle",
"ear biting",
"ear blush",
"ear bow",
"ear chain",
"ear covers",
"ear focus",
"ear piercing",
"ear protection",
"ear ribbon",
"ear tag",
"earbuds",
"earclip",
"earmuffs",
"earmuffs around neck",
"earphones",
"earphones removed",
"earpiece",
"earplugs",
"earrings",
"ears down",
"ears through headwear",
"ears visible through hair",
"earth (ornament)",
"earth (planet)",
"earth hair",
"earthworm",
"easel",
"east asian architecture",
"east german",
"eastern dragon",
"eating",
"eclipse",
"educational",
"eel hat",
"eevee ears",
"effects pedal",
"egasumi",
"egg",
"egg (food)",
"egg hair ornament",
"egg vibrator",
"egg yolk",
"egyptian",
"egyptian clothes",
"egyptian plover",
"eiffel tower",
"eighth note",
"eisenhower jacket",
"ejaculating while penetrated",
"ejaculation",
"ejaculation under clothes",
"elbow gloves",
"elbow on table",
"elbow pads",
"elbow rest",
"elbow sleeve",
"elbow spikes",
"elbows on knees",
"elbows on table",
"elcan scope",
"eldritch abomination",
"electric fan",
"electric guitar",
"electric kettle",
"electric plug",
"electric plug tail",
"electrical outlet",
"electricity",
"electrocution",
"elemental (creature)",
"elevator",
"elevator door",
"elf",
"elite ii (arknights)",
"ema",
"embarrassed",
"embers",
"emblem",
"embroidered",
"embroidery",
"emo fashion",
"emoji",
"emoticon",
"emotionless sex",
"emphasis lines",
"employee uniform",
"empty eyes",
"empty picture frame",
"emr camouflage",
"endoplasmic reticulum",
"energy",
"energy blade",
"energy drink",
"energy gun",
"energy sword",
"energy weapon",
"engine",
"engineering nonsense",
"english flag",
"english text",
"engraved",
"engrish text",
"enmaided",
"enpera",
"entrance",
"entrenching tool",
"envelope",
"enveloped",
"eotech",
"epaulettes",
"equation",
"equipment layout",
"eraser",
"erection",
"erection under clothes",
"erlenmeyer flask",
"error message",
"escalator",
"espresso (drink)",
"estonian flag",
"european architecture",
"evening",
"evening gown",
"evil grin",
"evil smile",
"excavator",
"excessive cum",
"excessive pubic hair",
"excessive pussy juice",
"excited",
"exercise",
"exercise ball",
"exhausted",
"exhibition drill",
"exhibitionism",
"exit sign",
"exoskeleton",
"expectations/reality",
"explosion",
"explosive",
"exposed muscle",
"exposed pocket",
"expression chart",
"expressionless",
"expressions",
"expressive hair",
"extra arms",
"extra digits",
"extra ears",
"extra eyes",
"extra hands",
"extra legs",
"extra mouth",
"eye contact",
"eye focus",
"eye glitter",
"eye in palm",
"eye print",
"eye reflection",
"eye socket",
"eyeball",
"eyeball bracelet",
"eyeball hair ornament",
"eyebrow cut",
"eyebrow piercing",
"eyebrows",
"eyebrows hidden by hair",
"eyedrops",
"eyelashes",
"eyelid piercing",
"eyeliner",
"eyepatch",
"eyepatch bikini",
"eyes in shadow",
"eyes visible through hair",
"eyeshadow",
"eyeshadow under eye",
"eyewear on head",
"eyewear on headwear",
"eyewear view",
"f-117 nighthawk",
"f-14 tomcat",
"f-16 fighting falcon",
"f-18 hornet",
"f-22 raptor",
"f-35 lightning ii",
"f6f hellcat",
"face between breasts",
"face down",
"face filter",
"face in pillow",
"face punch",
"face to breasts",
"face-to-face",
"facebook logo",
"facebook username",
"faceless",
"faceless female",
"faceless male",
"facepaint",
"faceplate",
"faceswap",
"facial",
"facial hair",
"facial mark",
"facial tattoo",
"facing ahead",
"facing another",
"facing away",
"facing back",
"facing down",
"facing to the side",
"facing viewer",
"failure",
"fairy",
"fairy wings",
"fake ad",
"fake animal ears",
"fake antlers",
"fake horns",
"fake nails",
"fake phone screenshot",
"fake screenshot",
"fake tail",
"fake video",
"fake wings",
"fallen angel",
"fallen down",
"falling",
"falling feathers",
"falling leaves",
"falling petals",
"false smile",
"famas",
"family",
"family tree",
"fanbox username",
"fang",
"fang out",
"fanged bangs",
"fangs",
"fangs out",
"fanning face",
"fanning self",
"fanny pack",
"fantasy",
"fare gate",
"farm",
"fashion",
"fast food",
"fat",
"fat man",
"fat mons",
"father and child",
"father and daughter",
"father and son",
"fatigues",
"faucet",
"faulds",
"faux figurine",
"faux text",
"faux traditional media",
"fb msbs grot",
"fbi",
"feast",
"feather boa",
"feather dress",
"feather hair ornament",
"feather trim",
"feathered wings",
"feathers",
"february",
"fed by viewer",
"fedora",
"feeding",
"feet",
"feet on chair",
"feet on table",
"feet only",
"feet out of frame",
"feet up",
"fellatio",
"fellatio gesture",
"female butler",
"female ejaculation",
"female goblin",
"female masturbation",
"female orc",
"female orgasm",
"female pervert",
"female pov",
"female pubic hair",
"female service cap",
"femdom",
"feminization",
"fence",
"fender precision bass",
"fender telecaster",
"fern",
"ferrari",
"ferrari f60",
"ferris wheel",
"fertilization",
"festival",
"fetal position",
"fetch",
"fewer digits",
"fez hat",
"fff threesome",
"ffm threesome",
"fgm-148 javelin",
"fidgeting",
"field",
"field cap",
"field ration",
"fiery hair",
"fiery wings",
"fig",
"fighter jet",
"fighting",
"fighting stance",
"figure",
"fiji water",
"file",
"file cabinet",
"film grain",
"fim-92 stinger",
"fine art",
"fine art parody",
"fine fabric emphasis",
"finger frame",
"finger frame duo",
"finger in another's mouth",
"finger in navel",
"finger in own mouth",
"finger on trigger",
"finger tattoo",
"finger to cheek",
"finger to mouth",
"finger to own chin",
"fingering",
"fingerless gloves",
"fingernails",
"fingers to cheeks",
"fingers to mouth",
"fingers together",
"fingersmile",
"finland",
"finnish army",
"finnish clothes",
"finnish flag",
"fins",
"fire",
"fire alarm",
"fire axe",
"fire hydrant",
"firefighter",
"firefighter jacket",
"fireflies",
"firelock",
"fireplace",
"fireworks",
"firing",
"first aid",
"first aid kit",
"fish",
"fish (food)",
"fish bone",
"fish hair ornament",
"fish print",
"fish tail",
"fish tank",
"fishbowl",
"fisheye",
"fishnet armwear",
"fishnet gloves",
"fishnet pantyhose",
"fishnet sleeves",
"fishnet thighhighs",
"fishnet top",
"fishnets",
"fist pump",
"fisting",
"fitting room",
"fkey",
"flaccid",
"flag",
"flag background",
"flagpole",
"flail",
"flakpanzer gepard",
"flamberge",
"flamel symbol",
"flaming weapon",
"flanged mace",
"flare",
"flare gun",
"flared muzzle",
"flashback",
"flashbang",
"flashing",
"flashlight",
"flask",
"flat cap",
"flat chastity cage",
"flat chest",
"flat chest joke",
"flat color",
"flat envy",
"flat fuck friday (meme)",
"flatlining",
"flats",
"fleeing",
"fleur-de-lis",
"flexible",
"flick",
"flight attendant",
"flintlock",
"flip phone",
"flip-flops",
"flip-up sight",
"flipped hair",
"flippers",
"flipping food",
"flirting",
"floating",
"floating book",
"floating cape",
"floating clothes",
"floating hair",
"floating island",
"floating neckwear",
"floating object",
"floating rock",
"floating scarf",
"floating skull",
"flock",
"flood",
"floor",
"floorplan",
"floppy disk",
"floppy ears",
"floral arch",
"floral background",
"floral print",
"flower",
"flower basket",
"flower box",
"flower field",
"flower knot",
"flower on liquid",
"flower pot",
"flower tattoo",
"flower wreath",
"fluffy",
"fluorescent lamp",
"flustered",
"flute",
"fly",
"fly agaric",
"flyer",
"flying",
"flying animal",
"flying button",
"flying buttress",
"flying fish",
"flying paper",
"flying saucer",
"flying sweatdrops",
"fm 24/29",
"fn f2000",
"fn fal",
"fn fnc",
"fn mag",
"fn scar",
"fn scar 16",
"fn scar 17",
"focused",
"fog",
"fold-over boots",
"folded",
"folded clothes",
"folded hair",
"folded ponytail",
"folder",
"folding chair",
"folding fan",
"folding stock",
"foliage",
"food",
"food art",
"food fight",
"food focus",
"food in mouth",
"food insertion",
"food on body",
"food on face",
"food on head",
"food print",
"food stand",
"food wrapper",
"food-themed earrings",
"food-themed hair ornament",
"foodification",
"foot bath",
"foot focus",
"foot on head",
"foot out of frame",
"foot up",
"foot wraps",
"footjob",
"footprints",
"footsies",
"footwear bow",
"footwear flower",
"footwear focus",
"for the better right? (meme)",
"forced",
"forced orgasm",
"forced smile",
"ford",
"ford crown victoria",
"foregrip",
"forehead",
"forehead flick",
"forehead jewel",
"forehead mark",
"forehead protector",
"foreshortening",
"foreskin",
"foreskin insertion",
"foreskin pull",
"forest",
"fork",
"forked tongue",
"forklift certified (meme)",
"formal",
"forniphilia",
"fortress",
"fossil",
"fountain",
"four-leaf clover",
"fourth wall",
"fox",
"fox boy",
"fox ears",
"fox girl",
"fox mask",
"fox shadow puppet",
"fox tail",
"framed",
"framed breasts",
"framed image",
"framed insect",
"france",
"frankenstein's monster (cosplay)",
"frappuccino",
"frayed clothes",
"freckles",
"freediving",
"freedom of russia legion",
"french army",
"french braid",
"french clothes",
"french flag",
"french fries",
"french kiss",
"french text",
"fried chicken",
"fried egg",
"fried egg on toast",
"fried rice",
"fried rice prank (meme)",
"friends",
"frilled apron",
"frilled ascot",
"frilled bikini",
"frilled bonnet",
"frilled bra",
"frilled choker",
"frilled collar",
"frilled dress",
"frilled hairband",
"frilled headwear",
"frilled kimono",
"frilled leotard",
"frilled neck lizard",
"frilled panties",
"frilled pillow",
"frilled sailor collar",
"frilled shirt",
"frilled shirt collar",
"frilled shorts",
"frilled skirt",
"frilled sleeves",
"frilled socks",
"frilled thighhighs",
"frilled umbrella",
"frills",
"fringe trim",
"frisbee",
"frisbee doge (meme)",
"fritos",
"frog",
"frog boy",
"frog print",
"frog-mouth helm",
"froggy chair",
"from above",
"from behind",
"from below",
"from ground",
"from outside",
"from side",
"front-hook bra",
"front-tie bikini top",
"front-tie top",
"frottage",
"frown",
"fruit",
"fruit cup",
"fruit tart",
"fruit tree",
"frustrated",
"frying pan",
"fsb",
"ft-17",
"fu hua (azure empyrea) (cosplay)",
"fuck-me shirt",
"fucked silly",
"full armor",
"full body",
"full moon",
"full nelson",
"full stomach",
"full-body tattoo",
"full-face blush",
"full-length mirror",
"full-package futanari",
"fumo (doll)",
"fundoshi",
"fur",
"fur cape",
"fur capelet",
"fur choker",
"fur coat",
"fur collar",
"fur hat",
"fur jacket",
"fur scarf",
"fur shawl",
"fur sweater",
"fur trim",
"fur-tipped tail",
"fur-trimmed boots",
"fur-trimmed cape",
"fur-trimmed capelet",
"fur-trimmed coat",
"fur-trimmed collar",
"fur-trimmed dress",
"fur-trimmed gloves",
"fur-trimmed headwear",
"fur-trimmed hood",
"fur-trimmed jacket",
"fur-trimmed kimono",
"fur-trimmed shirt",
"fur-trimmed skirt",
"fur-trimmed sleeves",
"fur-trimmed thighhighs",
"furigana",
"furious",
"furisode",
"furoshiki",
"furrowed brow",
"furry",
"furry female",
"furry male",
"furry with furry",
"furry with non-furry",
"fusion",
"futa with female",
"futa with futa",
"futa with male",
"futakuchi-onna",
"futanari",
"futanari masturbation",
"futon",
"futuristic weapon",
"fuuin no tsue",
"g-string",
"gadsden flag",
"gag",
"gagged",
"gaiters",
"gakuran",
"galactic empire",
"galactic republic",
"galaxy",
"galil ace",
"gambeson",
"game boy",
"game boy (original)",
"game cartridge",
"game console",
"game controller",
"game link cable",
"game screenshot background",
"gamecube controller",
"gamepad",
"gameplay mechanics",
"gaming chair",
"gangbang",
"gao",
"gaping",
"garage",
"garand thumb",
"garbage truck",
"garden",
"garrison cap",
"garter belt",
"garter straps",
"gas mask",
"gas mask canister",
"gas station",
"gashadokuro",
"gate",
"gathers",
"gatling gun",
"gau-8",
"gauntlets",
"gauze",
"gaz-2975 tigr",
"gears",
"gecko",
"geforce rtx 2070 super",
"geisha",
"geissele urg-i",
"gem",
"gemini (constellation)",
"gender dysphoria",
"gender transitioning",
"genderswap",
"genderswap (ftm)",
"genderswap (mtf)",
"genre connection",
"german army",
"german clothes",
"german empire",
"german flag",
"german text",
"germany",
"germany oneesan (meme)",
"gesture",
"geta",
"getabako",
"gewehr 88",
"ghana (chocolate)",
"ghost",
"giancarlo esposito's \"i was acting\" (meme)",
"giant",
"giant monster",
"giant skeleton",
"giant snake",
"giantess",
"gibson les paul",
"gift",
"gift bag",
"gift box",
"gift wrapping",
"gigachad (meme)",
"gigantic breasts",
"gigantic penis",
"giggling",
"gign",
"gills",
"ginkgo leaf",
"ginkgo tree",
"girl on top",
"girl sandwich",
"giving",
"gladiator sandals",
"gladius",
"glaive (polearm)",
"glands of montgomery",
"glansjob",
"glaring",
"glasgow smile",
"glass",
"glass door",
"glass floor",
"glass slipper",
"glass teacup",
"glass teapot",
"glasses",
"glasses case",
"glassware",
"glenngarry cap",
"glint",
"glitch",
"globe",
"glock",
"gloom (expression)",
"glory hole",
"glove biting",
"glove bow",
"gloved handjob",
"gloves",
"glowing",
"glowing armor",
"glowing butterfly",
"glowing earrings",
"glowing eye",
"glowing eyes",
"glowing hair",
"glowing hand",
"glowing hot",
"glowing mouth",
"glowing sword",
"glowing tattoo",
"glowing weapon",
"glowstick",
"glue stick",
"goat",
"goat boy",
"goat ears",
"goat girl",
"goat horns",
"goat tail",
"goatee",
"goblin",
"goggles",
"goggles on head",
"goggles on headwear",
"gold",
"gold armor",
"gold bikini",
"gold bracelet",
"gold choker",
"gold earrings",
"gold hairband",
"gold necklace",
"gold trim",
"golden gun",
"goldfish",
"goldfish print",
"golf",
"golf ball",
"golf club",
"golf course",
"golgi apparatus",
"good end",
"goose",
"gorget",
"gorka",
"gothic",
"gothic architecture",
"gothic lolita",
"gotoh hitori (cosplay)",
"gown",
"gp-25",
"grabbing",
"grabbing another's arm",
"grabbing another's ass",
"grabbing another's breast",
"grabbing another's chin",
"grabbing another's hair",
"grabbing from behind",
"grabbing own arm",
"grabbing own ass",
"grabbing own breast",
"grabbing own thigh",
"gradient",
"gradient background",
"gradient clothes",
"gradient dress",
"gradient eyes",
"gradient hair",
"gradient horns",
"gradient skin",
"gradient sky",
"graduation",
"graffiti",
"grandfather clock",
"grapes",
"graphics card",
"graphite (medium)",
"grass",
"grasslands",
"grate",
"grave",
"graves",
"graveyard",
"great helm",
"great pyramid of giza",
"greatsword",
"greaves",
"greco-roman architecture",
"greco-roman clothes",
"greece",
"greek macedonian flag",
"greek toe",
"green apron",
"green armor",
"green background",
"green bag",
"green belt",
"green bikini",
"green bow",
"green bowtie",
"green bra",
"green bracelet",
"green camisole",
"green capelet",
"green cardigan",
"green cloak",
"green coat",
"green curtains",
"green dress",
"green eyes",
"green eyeshadow",
"green flower",
"green footwear",
"green gemstone",
"green gloves",
"green hair",
"green hairband",
"green headwear",
"green hoodie",
"green horns",
"green jacket",
"green kimono",
"green legwear",
"green lips",
"green mask",
"green nails",
"green neckerchief",
"green necktie",
"green neckwear",
"green pajamas",
"green panties",
"green pants",
"green pantyhose",
"green ribbon",
"green robe",
"green sash",
"green scarf",
"green sclera",
"green scrunchie",
"green shawl",
"green shirt",
"green shorts",
"green skin",
"green skirt",
"green sky",
"green sleeves",
"green socks",
"green sweater",
"green tail",
"green tea",
"green theme",
"green tongue",
"green tunic",
"green vest",
"greenhouse",
"grenade",
"grenade launcher",
"grey apron",
"grey background",
"grey bag",
"grey belt",
"grey border",
"grey bow",
"grey bowtie",
"grey bra",
"grey cape",
"grey capelet",
"grey car",
"grey cardigan",
"grey choker",
"grey cloak",
"grey coat",
"grey dress",
"grey eyes",
"grey eyeshadow",
"grey feathers",
"grey footwear",
"grey fur",
"grey gloves",
"grey hair",
"grey hairband",
"grey headband",
"grey headwear",
"grey hoodie",
"grey horns",
"grey jacket",
"grey kimono",
"grey legwear",
"grey leotard",
"grey lips",
"grey mask",
"grey nails",
"grey neckerchief",
"grey necktie",
"grey pajamas",
"grey panties",
"grey pants",
"grey pantyhose",
"grey pupils",
"grey ribbon",
"grey rose",
"grey sailor collar",
"grey scales",
"grey scarf",
"grey sclera",
"grey shirt",
"grey shorts",
"grey skin",
"grey skirt",
"grey sky",
"grey socks",
"grey sports bra",
"grey suit",
"grey sweater",
"grey sweater vest",
"grey tank top",
"grey theme",
"grey thighhighs",
"grey vest",
"grey-framed eyewear",
"greyscale",
"greyscale with colored background",
"griffin & kryuger",
"griffin & kryuger military uniform",
"grimace shake (meme)",
"grimoire",
"grin",
"grinding",
"grip",
"gris swimsuit",
"groceries",
"grocery bag",
"groin",
"groin tendon",
"groping",
"ground vehicle",
"group picture",
"group sex",
"growth",
"grumpy",
"guard rail",
"gucci",
"guided breast grab",
"guided penetration",
"guiding hand",
"guitar",
"guitar case",
"guitar print",
"gulf war",
"gun",
"gun on back",
"gun sling",
"gun to head",
"gusset",
"gyaru",
"gyaru v",
"gyaruo",
"gym",
"gym shirt",
"gym shorts",
"gym storeroom",
"gym uniform",
"h&k g28",
"h&k g3",
"h&k hk416",
"h&k mp5",
"h&k mp5k",
"h&k mp5sd",
"h&k mp7",
"h&k vp9",
"habit",
"hachimaki",
"haikei (le gris no9)",
"haiku",
"hair behind ear",
"hair behind eyewear",
"hair bell",
"hair between breasts",
"hair between eyes",
"hair between horns",
"hair bobbles",
"hair bow",
"hair brush",
"hair bun",
"hair censor",
"hair dryer",
"hair dye",
"hair flaps",
"hair flip",
"hair flower",
"hair flowing over",
"hair focus",
"hair in food",
"hair in own mouth",
"hair intakes",
"hair lift",
"hair on horn",
"hair ornament",
"hair over breasts",
"hair over crotch",
"hair over eyes",
"hair over face",
"hair over one eye",
"hair over shoulder",
"hair pulled back",
"hair ribbon",
"hair rings",
"hair rollers",
"hair scrunchie",
"hair slicked back",
"hair spread out",
"hair stick",
"hair straightener",
"hair strand",
"hair tie",
"hair tie in mouth",
"hair tubes",
"hair tucking",
"hair twirling",
"hair up",
"hairband",
"hairclip",
"hairdressing",
"hairpin",
"hairpods",
"hairy",
"hakama",
"hakama short skirt",
"hakama shorts",
"hakama skirt",
"halberd",
"half gloves",
"half lotus position",
"half mask",
"half updo",
"half-closed eye",
"half-closed eyes",
"half-erect",
"half-harpy",
"half-split chopsticks",
"half-swording",
"half-timbered",
"half-track",
"halftone",
"halftone background",
"halloween",
"halloween bucket",
"halloween costume",
"hallway",
"halo",
"halo behind head",
"halter dress",
"halter shirt",
"halterneck",
"ham",
"hamburger steak",
"hammer",
"hammer and sickle",
"hamsa",
"hanamaru",
"hanbok",
"hand around waist",
"hand between legs",
"hand eye",
"hand fan",
"hand focus",
"hand gesture",
"hand grab",
"hand grip",
"hand in another's hair",
"hand in another's panties",
"hand in another's pocket",
"hand in own hair",
"hand in panties",
"hand in pocket",
"hand mirror",
"hand on animal",
"hand on another's arm",
"hand on another's back",
"hand on another's cheek",
"hand on another's chest",
"hand on another's chin",
"hand on another's face",
"hand on another's head",
"hand on another's knee",
"hand on another's leg",
"hand on another's neck",
"hand on another's shoulder",
"hand on another's thigh",
"hand on another's waist",
"hand on back",
"hand on belt",
"hand on eyewear",
"hand on floor",
"hand on forehead",
"hand on glass",
"hand on hand",
"hand on handle",
"hand on headphones",
"hand on headwear",
"hand on hilt",
"hand on lap",
"hand on own arm",
"hand on own ass",
"hand on own cheek",
"hand on own chest",
"hand on own chin",
"hand on own crotch",
"hand on own ear",
"hand on own elbow",
"hand on own face",
"hand on own foot",
"hand on own head",
"hand on own hip",
"hand on own knee",
"hand on own leg",
"hand on own shoulder",
"hand on own stomach",
"hand on own thigh",
"hand on railing",
"hand on table",
"hand on wall",
"hand on weapon",
"hand over face",
"hand over own mouth",
"hand rest",
"hand sonic",
"hand tattoo",
"hand to own mouth",
"hand under clothes",
"hand under shirt",
"hand up",
"hand wraps",
"handbag",
"handcuffs",
"handgun",
"handheld fan",
"handheld game console",
"handjob",
"handjob gesture",
"handle",
"handprint",
"handrail",
"hands",
"hands in another's armpits",
"hands in hair",
"hands in opposite sleeves",
"hands in pocket",
"hands in pockets",
"hands on another's arm",
"hands on another's cheeks",
"hands on another's face",
"hands on another's hips",
"hands on another's knees",
"hands on another's leg",
"hands on another's shoulders",
"hands on another's thighs",
"hands on ass",
"hands on feet",
"hands on floor",
"hands on ground",
"hands on headwear",
"hands on hilt",
"hands on lap",
"hands on own ass",
"hands on own cheeks",
"hands on own chest",
"hands on own chin",
"hands on own face",
"hands on own head",
"hands on own hips",
"hands on own knees",
"hands on own leg",
"hands on own legs",
"hands on own stomach",
"hands on shoulder",
"hands on table",
"hands up",
"handsfree ejaculation",
"handsfree paizuri",
"handshake",
"handsome squidward (meme)",
"hanfu",
"hangar",
"hanging",
"hanging breasts",
"hanging flower",
"hanging food",
"hanging lantern",
"hanging light",
"hanging plant",
"haori",
"happy",
"happy birthday",
"happy new year",
"happy sex",
"happy tears",
"happy valentine",
"harajuku fashion",
"hard drive",
"hard hat",
"hardboiled egg",
"hardpoint",
"harem",
"harness",
"harp",
"harpoon",
"harpoon gun",
"harpy",
"hat",
"hat bow",
"hat feather",
"hat flower",
"hat loss",
"hat ornament",
"hat over eyes",
"hat ribbon",
"hat with ears",
"hatch",
"hatching (texture)",
"hatsumoude",
"hauberk",
"haunting",
"have to pee",
"hay",
"hayapi (sinsin08051)",
"hazmat suit",
"head back",
"head backwards",
"head between breasts",
"head bump",
"head chain",
"head fins",
"head grab",
"head on another's shoulder",
"head on hand",
"head on pillow",
"head on table",
"head only",
"head out of frame",
"head rest",
"head scarf",
"head steam",
"head tilt",
"head wings",
"head wreath",
"head-mounted display",
"headband",
"headboard",
"headdress",
"headdress removed",
"headgear",
"headless",
"headlight",
"headpat",
"headphones",
"headphones around neck",
"headphones removed",
"headpiece",
"heads together",
"headset",
"headwear pull",
"headwear writing",
"health bar",
"heart",
"heart ahoge",
"heart background",
"heart balloon",
"heart brooch",
"heart button",
"heart censor",
"heart choker",
"heart earrings",
"heart facial mark",
"heart hair ornament",
"heart hands",
"heart hands duo",
"heart hands failure",
"heart in eye",
"heart in mouth",
"heart necklace",
"heart o-ring",
"heart of string",
"heart pasties",
"heart print",
"heart ribbon",
"heart tail",
"heart tattoo",
"heart-shaped bag",
"heart-shaped box",
"heart-shaped buckle",
"heart-shaped chocolate",
"heart-shaped eyes",
"heart-shaped gem",
"heart-shaped ornament",
"heart-shaped pillow",
"heart-shaped pupils",
"heartbeat",
"heater",
"heattech leotard",
"heaven",
"heavy breathing",
"heavy machine gun",
"heavy metal",
"heckler & koch",
"heel pop",
"heel up",
"heel-less legwear",
"height",
"height chart",
"height difference",
"height mark",
"helicopter",
"helm",
"helmet",
"hemokinesis",
"heraldry",
"herb bundle",
"hercules (constellation)",
"hermit crab",
"hetero",
"heterochromia",
"hexagram",
"hey friend listen (meme)",
"hibiscus",
"hickey",
"hiding",
"hiding behind another",
"high collar",
"high five",
"high heel boots",
"high heels",
"high ponytail",
"high tops",
"high-visibility jacket",
"high-visibility vest",
"high-waist dress",
"high-waist pants",
"high-waist shorts",
"high-waist skirt",
"highleg",
"highleg bikini",
"highleg leotard",
"highleg panties",
"highleg swimsuit",
"highway",
"hijab",
"hikimayu",
"hikyou takarasou",
"hill",
"hime cut",
"himejoshi",
"hip bones",
"hip flask",
"hip focus",
"hip vent",
"hiroshima",
"historical american flag",
"historical event",
"hitachi magic wand",
"hitodama",
"hitting",
"hms laforey (1913)",
"hms warspite (badge)",
"hms warspite (s103)",
"hobble",
"hoe",
"hogtie",
"hogwarts school uniform",
"holding",
"holding animal",
"holding another's arm",
"holding another's leg",
"holding another's wrist",
"holding arrow",
"holding axe",
"holding baby",
"holding bag",
"holding ball",
"holding baseball bat",
"holding basket",
"holding binoculars",
"holding bird",
"holding blanket",
"holding book",
"holding bottle",
"holding bouquet",
"holding bow (music)",
"holding bow (weapon)",
"holding bowl",
"holding box",
"holding boxcutter",
"holding bra",
"holding branch",
"holding briefcase",
"holding broom",
"holding brush",
"holding camera",
"holding can",
"holding candle",
"holding candy",
"holding cane",
"holding card",
"holding carrot",
"holding carton",
"holding cat",
"holding chain",
"holding chocolate",
"holding chopsticks",
"holding cigarette",
"holding cigarette pack",
"holding clipboard",
"holding clothes",
"holding clothes hanger",
"holding clothes iron",
"holding clover",
"holding club",
"holding coin",
"holding condom",
"holding controller",
"holding crossbow",
"holding cup",
"holding dagger",
"holding dog",
"holding doll",
"holding dress",
"holding drink",
"holding duster",
"holding egg",
"holding envelope",
"holding fan",
"holding feather",
"holding fireworks",
"holding fish",
"holding flag",
"holding flail",
"holding flower",
"holding food",
"holding footwear",
"holding fork",
"holding fruit",
"holding game controller",
"holding gift",
"holding glowstick",
"holding goggles",
"holding golf ball",
"holding golf club",
"holding grenade",
"holding gun",
"holding hair",
"holding hair dryer",
"holding hair tie",
"holding hammer",
"holding hand grip",
"holding handcuffs",
"holding handheld game console",
"holding hands",
"holding hands is lewd",
"holding hat",
"holding head",
"holding heart",
"holding helmet",
"holding hoe",
"holding hose",
"holding ice cream",
"holding instrument",
"holding jacket",
"holding jar",
"holding key",
"holding knife",
"holding ladle",
"holding lamp",
"holding lantern",
"holding leaf",
"holding leash",
"holding leg",
"holding legs",
"holding letter",
"holding lighter",
"holding lipstick tube",
"holding lollipop",
"holding luggage",
"holding mace",
"holding manga",
"holding map",
"holding marker",
"holding mask",
"holding menu",
"holding microphone",
"holding mirror",
"holding money",
"holding mop",
"holding mushroom",
"holding nail",
"holding notebook",
"holding notepad",
"holding own arm",
"holding own wrist",
"holding paddle",
"holding paintbrush",
"holding panties",
"holding paper",
"holding pen",
"holding pencil",
"holding person",
"holding petal",
"holding phone",
"holding photo",
"holding pillow",
"holding pitchfork",
"holding pizza",
"holding plate",
"holding plunger",
"holding pocket watch",
"holding pokemon",
"holding polearm",
"holding popsicle",
"holding quill",
"holding rabbit",
"holding racket",
"holding rattle",
"holding razor",
"holding reins",
"holding removed eyewear",
"holding ribbon",
"holding rocket launcher",
"holding sack",
"holding saucer",
"holding scalpel",
"holding scanner",
"holding scepter",
"holding scissors",
"holding scroll",
"holding scythe",
"holding sex toy",
"holding sheath",
"holding shield",
"holding shoes",
"holding shovel",
"holding sign",
"holding skateboard",
"holding sketchbook",
"holding skewer",
"holding skull",
"holding smoking pipe",
"holding spatula",
"holding spoon",
"holding staff",
"holding stick",
"holding strap",
"holding stuffed toy",
"holding stylus",
"holding suitcase",
"holding swimsuit",
"holding sword",
"holding syringe",
"holding teapot",
"holding tennis racket",
"holding test tube",
"holding torch",
"holding towel",
"holding toy",
"holding tray",
"holding umbrella",
"holding underwear",
"holding vase",
"holding vegetable",
"holding violin",
"holding wand",
"holding water",
"holding weapon",
"holding whip",
"hole",
"hole in face",
"holed coin",
"hollow body",
"hollow eyes",
"hollowed legwear",
"holly",
"holly hair ornament",
"holographic clothing",
"holster",
"holstered",
"holy roman empire",
"homeless",
"homework",
"honey",
"honeycomb (pattern)",
"honeycomb background",
"hood",
"hood down",
"hood up",
"hooded cape",
"hooded capelet",
"hooded cloak",
"hooded coat",
"hooded jacket",
"hooded robe",
"hooded sweater",
"hoodie",
"hoodie hiding shorts",
"hoodie lift",
"hook",
"hoop",
"hoop earrings",
"hoop piercing",
"hoop skirt",
"hooves",
"horizon",
"horizontal pupils",
"horn (instrument)",
"horn cover",
"horn grab",
"horn ornament",
"horn ring",
"horned helmet",
"horns",
"horrified",
"horror (theme)",
"horse",
"horse boy",
"horse ears",
"horse girl",
"horse penis",
"horse print",
"horse tail",
"horseback riding",
"horseshoe",
"horseshoe crab",
"hose",
"hoshimachi suisei (cosplay)",
"hospital",
"hospital bed",
"hospital gown",
"hot",
"hotpot",
"houndstooth",
"hourglass",
"house",
"housewife",
"how to",
"howa type 20",
"howa type 89",
"howitzer",
"hug",
"hug from behind",
"huge ahoge",
"huge ass",
"huge breasts",
"huge dildo",
"huge horns",
"huge nipples",
"huge penis",
"huge testicles",
"huge weapon",
"hugging book",
"hugging doll",
"hugging object",
"hugging own legs",
"hugging own tail",
"hugging tail",
"hula hoop",
"human furniture",
"human head",
"human tower",
"humanization",
"humanoid robot",
"humiliation",
"humpback whale",
"hunched over",
"husband and wife",
"hut",
"hybrid sight",
"hydrangea",
"hypnosis",
"hypnotizing viewer",
"i am a surgeon (meme)",
"i have no tits (shirt)",
"i showed you my dick please respond (meme)",
"iahfy",
"ice",
"ice cream",
"ice cream cone",
"ice cream cone spill",
"ice cream float",
"ice cube",
"ice skates",
"ice skating",
"iced coffee",
"iced latte with breast milk (meme)",
"ichimegasa",
"icon (computing)",
"id card",
"identity censor",
"idol",
"if they mated",
"ikea shark",
"ikura (food)",
"image macro (meme)",
"imagining",
"imitating",
"immersed",
"imminent anal",
"imminent cunnilingus",
"imminent death",
"imminent fellatio",
"imminent fingering",
"imminent kiss",
"imminent netorare",
"imminent penetration",
"imminent rape",
"imminent vaginal",
"immobilization",
"impact (font)",
"impaled",
"imperial german flag",
"imperial japanese navy",
"imperium of man",
"implied after sex",
"implied ass grab",
"implied cannibalism",
"implied cheating (relationship)",
"implied fellatio",
"implied futanari",
"implied handjob",
"implied incest",
"implied kiss",
"implied murder",
"implied orgasm",
"implied penetration",
"implied prostitution",
"implied sex",
"implied yuri",
"impossible architecture",
"impossible clothes",
"impossible dress",
"impossible shirt",
"impregnation",
"impressionism",
"improvised gag",
"in box",
"in container",
"in crane game",
"in heat",
"in mouth",
"in the face",
"in the walls (meme)",
"in water",
"in-ear earphones",
"ina's back (meme)",
"incense",
"incense burner",
"incest",
"incoming attack",
"incoming food",
"incoming gift",
"incoming hug",
"incoming letter",
"inconvenient ass",
"inconvenient breasts",
"index finger raised",
"index fingers together",
"indian",
"indian style",
"indirect kiss",
"indonesia",
"indonesian army",
"indoors",
"industrial",
"industrial piercing",
"industrial pipe",
"infirmary",
"initial",
"injury",
"ink",
"ink bottle",
"inline skates",
"inn",
"inner ego",
"innertube",
"innie navel",
"insect hair ornament",
"insect wings",
"insecticide",
"insemination",
"inset",
"inset border",
"inside-out",
"insignia",
"insomnia",
"instagram logo",
"instagram username",
"instant loss",
"instant soba",
"instrument",
"instrument case",
"instrument on back",
"intercom",
"interior",
"interlocked fingers",
"internal cumshot",
"internet",
"interracial",
"interrogation",
"interspecies",
"intertwined tails",
"interview",
"intravenous drip",
"introduction",
"inugami-ke no ichizoku pose",
"inverted colors",
"inverted cross",
"inverted nipples",
"inverted pentagram",
"invisible",
"invisible chair",
"invisible floor",
"ipad",
"iphone",
"iphone 12",
"iphone 13",
"ips cells",
"ireland",
"iridescent",
"irish army",
"iron blood (emblem)",
"iron cross",
"ironing",
"ironing board",
"ironwork",
"irrumatio",
"irs",
"isekai truck",
"island",
"isometric",
"isopod",
"it's morbin' time (meme)",
"itabag",
"italian army",
"italian flag",
"italy",
"iv stand",
"iwi tavor",
"j-20",
"j-31",
"j.k.",
"jack daniel's",
"jack-o' challenge",
"jack-o'-lantern",
"jack-o'-lantern hair ornament",
"jackal ears",
"jacket",
"jacket around waist",
"jacket on shoulders",
"jacket over shoulder",
"jacket partially removed",
"jacket pull",
"jacques de molay (foreigner) (fate) (cosplay)",
"jacques de molay (foreigner) (third ascension) (fate) (cosplay)",
"jagariko",
"jaggy lines",
"jam",
"january",
"japan",
"japan ground self-defense force",
"japan self-defense force",
"japanese armor",
"japanese clothes",
"jar",
"javelin (spear)",
"jealous",
"jeans",
"jeep (company)",
"jeep wrangler",
"jellyfish",
"jersey",
"jersey maid",
"jet",
"jewel butt plug",
"jewelry",
"jiangshi",
"jimiko",
"jingasa",
"jingle bell",
"jinyiwei",
"jirai kei",
"jitome",
"jocelin carmes",
"joints",
"jojo pose",
"joker (dc) (cosplay)",
"jolly roger",
"joseon dynasty",
"josou seme",
"journey in the auspicious snow (girls' frontline)",
"joy-con",
"judge",
"juice",
"juice box",
"juliana (pokemon) (cosplay)",
"juliet sleeves",
"jumping",
"jumpsuit",
"jumpsuit around waist",
"june",
"jungle gym",
"just as planned (meme)",
"just shoes",
"just the tip",
"ka-52",
"kabedon",
"kabedon on viewer",
"kabuto (helmet)",
"kac sr-15",
"kaga (aircraft carrier)",
"kaisendon",
"kaitan",
"kalashnikov rifle",
"kamehameha (dragon ball)",
"kaneda shoutarou's bike",
"kanji",
"kanzashi",
"karaoke",
"karin (blue archive) (cosplay)",
"katana",
"katsudon (food)",
"katsushika hokusai (1760)",
"kebab",
"keffiyeh",
"kelp",
"kemonomimi mode",
"kendo",
"kendo mask",
"kepi",
"kerchief",
"ketchup",
"ketchup bottle",
"kettenkrad",
"kettle",
"kettle helm",
"key",
"key necklace",
"keyboard (computer)",
"keyboard (instrument)",
"keychain",
"keyhole",
"keyring",
"khakis",
"kicking",
"kikouken",
"kimono",
"kimono around waist",
"kimono pull",
"kindergarten teacher",
"kindergarten uniform",
"kiseru",
"kiss",
"kiss day",
"kissing animal",
"kissing cheek",
"kissing forehead",
"kissing neck",
"kissing penis",
"kitagawa marin (cosplay)",
"kitchen",
"kitchen knife",
"kite shield",
"kitsune",
"kneading",
"knee boots",
"knee pads",
"knee to chest",
"knee up",
"kneehighs",
"kneeling",
"kneepits",
"knees",
"knees apart feet together",
"knees to chest",
"knees together feet apart",
"knees up",
"knife",
"knife block",
"knife in mouth",
"knight",
"knights templar",
"knit hat",
"knitting",
"knitting needle",
"knocking",
"knolling",
"knotted penis",
"kobold",
"kogal",
"koi",
"koito yuu (cosplay)",
"kongou (battleship)",
"kooribata",
"korean armor",
"korean clothes",
"korean fire noodles",
"korean text",
"korsehut",
"kotatsu",
"kote",
"kotwica (symbol)",
"koyuki (kotatsu358)",
"kraken",
"kremlin",
"kriegsmarine",
"kubrick stare",
"kurokote",
"kusazuri",
"kyuubi",
"kyuudou",
"la chancla",
"lab coat",
"laboratory",
"labret piercing",
"lace",
"lace bra",
"lace choker",
"lace gloves",
"lace panties",
"lace shirt",
"lace trim",
"lace-trimmed apron",
"lace-trimmed bra",
"lace-trimmed collar",
"lace-trimmed dress",
"lace-trimmed garter belt",
"lace-trimmed hairband",
"lace-trimmed legwear",
"lace-trimmed panties",
"lace-trimmed skirt",
"lace-trimmed sleeves",
"lace-trimmed thighhighs",
"lace-up boots",
"lactation",
"lactation through clothes",
"ladder",
"ladle",
"ladybug",
"ladybug girl",
"lake",
"lamb",
"lamellar armor",
"lamia",
"lamp",
"lamppost",
"lampshade",
"lance",
"landing",
"landing gear",
"landscape",
"landship",
"landsknecht",
"lane line",
"lantern",
"lantern festival",
"lanyard",
"lap pillow",
"lap pov",
"lapels",
"laptop",
"large areolae",
"large bow",
"large breasts",
"large ears",
"large hat",
"large insertion",
"large penis",
"large tail",
"large testicles",
"laser",
"laser pointer projection",
"laser sight",
"latex",
"latex bodysuit",
"latex gloves",
"latex legwear",
"latex thighhighs",
"latin cross",
"latin text",
"latte art",
"laughing",
"laundromat",
"laundry basket",
"laurel crown",
"lava",
"lava cake",
"lavender (flower)",
"lay's (potato chips)",
"layered armor",
"layered bikini",
"layered clothes",
"layered dress",
"layered kimono",
"layered shirt",
"layered skirt",
"layered sleeves",
"lazy",
"lead pipe",
"leaf",
"leaf hair ornament",
"leaf hat ornament",
"leaf on head",
"leaf print",
"leaning",
"leaning against vehicle",
"leaning back",
"leaning forward",
"leaning on object",
"leaning on person",
"leaning on table",
"leaning on weapon",
"leaning to the side",
"leash",
"leather",
"leather armor",
"leather belt",
"leather boots",
"leather gloves",
"leather jacket",
"leather pants",
"leather skirt",
"leather strap",
"leather vest",
"lee-enfield",
"left-hand drive",
"left-handed",
"left-to-right manga",
"leg armor",
"leg belt",
"leg between thighs",
"leg cast",
"leg grab",
"leg hair",
"leg hold",
"leg holster",
"leg lift",
"leg lock",
"leg ribbon",
"leg tattoo",
"leg up",
"leg warmers",
"leg wrap",
"leggings",
"leggings pull",
"legionnaire",
"lego",
"lego minifig",
"legs",
"legs apart",
"legs folded",
"legs on table",
"legs together",
"legs up",
"legwear cutout",
"legwear garter",
"lemon",
"lemon earrings",
"lemon print",
"lemon slice",
"lenny face",
"lens",
"lens eye",
"lens flare",
"leopard 2",
"leopard gecko",
"leopard print",
"leotard",
"leotard aside",
"leotard peek",
"leotard under clothes",
"lesbian flag",
"let him cook (meme)",
"letter",
"letterboxed",
"letterman jacket",
"lettuce",
"leucochloridium paradoxum",
"lever action",
"levitation",
"lgbt pride",
"library",
"license plate",
"licking",
"licking another's cheek",
"licking another's face",
"licking armpit",
"licking blade",
"licking blood",
"licking breast",
"licking ear",
"licking finger",
"licking lips",
"licking nipple",
"licking panties",
"licking penis",
"licking testicle",
"licking weapon",
"lifeboat",
"lifebuoy",
"lifted by another",
"lifted by self",
"lifted by tail",
"lifting person",
"lifting vehicle",
"light",
"light areolae",
"light blue background",
"light blue hair",
"light blush",
"light brown background",
"light brown hair",
"light frown",
"light machine gun",
"light particles",
"light purple hair",
"light rays",
"light smile",
"light switch",
"lighter",
"lighthouse",
"lighting cigarette",
"lightning",
"lightning bolt symbol",
"lights",
"ligne claire",
"like and retweet",
"lilac",
"lily (flower)",
"lily of the valley",
"lily pad",
"lily print",
"limited palette",
"linea alba",
"linear hatching",
"lineup",
"lingerie",
"linked collar",
"linked piercing",
"lion ears",
"lion print",
"lion tail",
"lionel messi (cosplay)",
"lip piercing",
"lipgloss",
"lips",
"lipstick",
"lipstick mark",
"lipstick mark on ass",
"lipstick mark on pussy",
"lipstick ring",
"lipstick tube",
"liquid",
"liquid hair",
"liquid silver",
"liquid weapon",
"list",
"listening to music",
"little red riding hood (grimm) (cosplay)",
"live hood",
"livestream",
"living armor",
"living clothes",
"living hair",
"living plush",
"living room",
"living shadow",
"lizard",
"lizard tail",
"lizardman",
"lk (lunar lander)",
"llama",
"load bearing equipment",
"load bearing vest",
"loaded interior",
"loading screen",
"loaf of bread",
"loafers",
"lobster",
"lock",
"lock earrings",
"locked arms",
"locker",
"locker room",
"lockheed have blue",
"locking",
"locomotive",
"log",
"logo",
"logo parody",
"loincloth",
"lolita fashion",
"lolita hairband",
"lollipop",
"london",
"lonely",
"long arms",
"long bangs",
"long beard",
"long braid",
"long coat",
"long dress",
"long eyebrows",
"long eyelashes",
"long fingernails",
"long fingers",
"long hair",
"long hair between eyes",
"long hoodie",
"long labia",
"long legs",
"long neck",
"long pointy ears",
"long scarf",
"long shirt",
"long skirt",
"long sleeves",
"long sword",
"long tail",
"long toenails",
"long toes",
"long tongue",
"longcat (meme)",
"looking afar",
"looking ahead",
"looking at animal",
"looking at another",
"looking at breasts",
"looking at food",
"looking at mirror",
"looking at object",
"looking at penis",
"looking at phone",
"looking at screen",
"looking at viewer",
"looking away",
"looking back",
"looking down",
"looking outside",
"looking over eyewear",
"looking to the side",
"looking up",
"loose bowtie",
"loose clothes",
"loose hair strand",
"loose necktie",
"loose shirt",
"loose socks",
"los angeles county sheriff's department",
"los angeles police department",
"lotion",
"lotion bottle",
"lotus leaf",
"louis vuitton (brand)",
"lounge chair",
"loungewear",
"love ball",
"love hotel",
"love letter",
"lovebird",
"low ponytail",
"low tied hair",
"low twin braids",
"low twintails",
"low wings",
"low-tied long hair",
"lowe (tank)",
"lower body",
"lower teeth only",
"lowered eyelids",
"lowleg",
"lowleg bikini",
"lowleg panties",
"lr-300",
"lube",
"lucerne hammer",
"luftwaffe",
"luger p08",
"luggage",
"lunar surface",
"lunch",
"lunchbox",
"lying",
"lying on person",
"lynus",
"lyrics",
"m legs",
"m1 abrams",
"m1 carbine",
"m1 garand",
"m1 helmet",
"m142 himars",
"m16",
"m16a1",
"m16a2",
"m16a4",
"m1903 springfield",
"m1911",
"m1918 bar",
"m203",
"m4 carbine",
"m4 sherman",
"m4 sopmod ii",
"m43 field cap",
"m5 stuart",
"m8 greyhound",
"m87 black hole",
"maboroshi no ginzuishou",
"macaron",
"macaron background",
"mace",
"machine",
"machine gun",
"machinery",
"maebari",
"mafia",
"magatama",
"magatama necklace",
"magazine (object)",
"magazine (weapon)",
"mage staff",
"magic",
"magic circle",
"magical boy",
"magical girl",
"magnet",
"magpul fmg-9",
"maid",
"maid apron",
"maid bikini",
"maid cafe",
"maid day",
"maid headdress",
"maintenance",
"makeup",
"male focus",
"male hand",
"male masturbation",
"male playboy bunny",
"male pubic hair",
"male underwear",
"mallet",
"malyuk",
"mamerakkkkko",
"manboobs",
"mandarin collar",
"mandarin duck",
"mandarin orange",
"mandragora",
"mandrake",
"manga (object)",
"manhole cover",
"manly",
"mannequin",
"mannlicher m1886",
"mantelpiece",
"map",
"map background",
"maple leaf",
"maracas",
"marble (stone)",
"march",
"margherita pizza",
"mari (blue archive) (cosplay)",
"mariachi",
"marker",
"marker (dead space)",
"market",
"marriage certificate (object)",
"marriage proposal",
"mars symbol",
"marshall amplification",
"marshmallow",
"martini-henry",
"maruchan midori no tanuki tensoba",
"mary janes",
"mas-36",
"mascara",
"mask",
"mask around neck",
"mask bikini",
"mask lift",
"mask on head",
"mask pull",
"masked",
"mast",
"mastercard",
"masturbation",
"matchbox",
"matches",
"matching outfits",
"matchlock",
"materia",
"math",
"mating press",
"mature female",
"mature male",
"maus (tank)",
"mauser 98",
"mauser c96",
"may",
"maz-537",
"mazda rx-7",
"mazda rx-7 fc",
"meadow",
"meal",
"measurements",
"measuring",
"meat",
"mecha",
"mecha musume",
"mechanic",
"mechanical arms",
"mechanical ears",
"mechanical eye",
"mechanical hands",
"mechanical horns",
"mechanical legs",
"mechanical parts",
"mechanical pencil",
"mechanical spine",
"mechanical tail",
"mechanical wings",
"mechanix wear",
"mechanization",
"medal",
"median furrow",
"medic",
"medical eyepatch",
"medical scrubs",
"medicine",
"medicine cabinet",
"medieval",
"meditation",
"medium bangs",
"medium breasts",
"medium dress",
"medium hair",
"medium skirt",
"medium tank",
"megastructure",
"melodica",
"melon bread",
"melon soda",
"melting",
"melting halo",
"meme",
"meme attire",
"memorial on desk",
"menacing (jojo)",
"menpoo",
"menu",
"menu board",
"merchandise",
"mercury (spacecraft)",
"mermaid",
"merry christmas",
"meruccubus (merunyaa) (cosplay)",
"mess kit",
"messenger bag",
"messy",
"messy hair",
"messy room",
"mesugaki",
"metal",
"metal collar",
"metal detector",
"metal pipe falling (meme)",
"meteor",
"mexican clothes",
"mexican revolution",
"mexico",
"mexico ufo alien bodies hearing (meme)",
"mg08/15",
"mg08/18",
"mg42",
"mi-24",
"micro bikini",
"micro bra",
"micro panties",
"micro shorts",
"microdress",
"microphone",
"microphone stand",
"microskirt",
"microsoft excel",
"microsoft outlook",
"microsoft paint (software)",
"microsoft teams",
"microwave",
"midair",
"middle finger",
"midriff",
"midriff peek",
"mig-21",
"mig-29",
"mig-31",
"miko",
"milestone celebration",
"military",
"military coat",
"military hat",
"military jacket",
"military operator",
"military police",
"military rank insignia",
"military truck",
"military uniform",
"military vehicle",
"milk",
"milk bottle",
"milk carton",
"milk churn",
"milking handjob",
"milking machine",
"milkor mgl",
"milkshake",
"milky way",
"mimic",
"mimic chest",
"mimikaki",
"mind control",
"mine (weapon)",
"minecraft pickaxe",
"mini balloon",
"mini crown",
"mini dragon",
"mini hat",
"mini person",
"mini top hat",
"mini wings",
"minigirl",
"minigun",
"miniskirt",
"minnie mouse ears",
"miqo'te",
"mirror",
"mismatched earrings",
"mismatched eyebrows",
"mismatched gloves",
"mismatched legwear",
"mismatched pubic hair",
"mismatched sclera",
"mismatched socks",
"miso soup",
"missile",
"missile pod",
"missionary",
"misunderstanding",
"mitarashi dango",
"mitochondria",
"mitosis",
"mitsubishi motors",
"mitsubishi pajero",
"mitsudomoe (shape)",
"mitsudoue",
"mittens",
"mixed maids",
"mixed media",
"mixed-language text",
"mixed-sex bathing",
"mk 18 carbine",
"mk 22 pistol",
"mk2 grenade",
"mmf threesome",
"mmm threesome",
"moaning",
"moat",
"mob cap",
"mobile suit",
"mochi",
"mochi trail",
"model ship",
"modeling",
"moe moe kyun!",
"moe2021",
"moe2022",
"mohawk",
"mojo",
"moka pot",
"mole",
"mole above eye",
"mole above mouth",
"mole on areola",
"mole on arm",
"mole on armpit",
"mole on ass",
"mole on body",
"mole on breast",
"mole on cheek",
"mole on collarbone",
"mole on crotch",
"mole on forehead",
"mole on leg",
"mole on neck",
"mole on pussy",
"mole on stomach",
"mole on thigh",
"mole under each eye",
"mole under eye",
"mole under mouth",
"molestation",
"money",
"mongolian clothes",
"monitor",
"monk",
"monochrome",
"monochrome background",
"monocle",
"monoglove",
"monokubo",
"monolith (object)",
"monster",
"monster boy",
"monster energy",
"monster girl",
"monsterification",
"mont blanc (food)",
"moon",
"moon rabbit",
"moon stick",
"moonlight",
"mop",
"moped",
"morion",
"morning",
"morning glory",
"morning glory print",
"mortar (bowl)",
"mortar (weapon)",
"mortarboard",
"mosaic censoring",
"moscow",
"mosin-nagant",
"mosquito",
"mosquito girl",
"moss",
"mossberg 590",
"moth",
"moth antennae",
"moth girl",
"mother and child",
"mother and daughter",
"mother and son",
"motion blur",
"motion lines",
"motor vehicle",
"motorcycle",
"motorcycle helmet",
"mount fuji",
"mountain",
"mountainous horizon",
"mourning",
"mouse",
"mouse (computer)",
"mouse ears",
"mouse girl",
"mouse tail",
"mousepad (object)",
"mousse (food)",
"mouth drool",
"mouth focus",
"mouth hold",
"mouth mask",
"mouth mirror",
"mouth pull",
"mouth veil",
"movie poster",
"movie poster (object)",
"movie theater",
"mp28",
"mp40",
"mp443",
"mpi-kms-72",
"muffin top",
"mug",
"multi-strapped bikini",
"multi-strapped bikini bottom",
"multi-strapped panties",
"multicolored background",
"multicolored bow",
"multicolored bra",
"multicolored clothes",
"multicolored dress",
"multicolored eyes",
"multicolored footwear",
"multicolored gloves",
"multicolored hair",
"multicolored headwear",
"multicolored hoodie",
"multicolored horns",
"multicolored jacket",
"multicolored legwear",
"multicolored nails",
"multicolored panties",
"multicolored scarf",
"multicolored shirt",
"multicolored skin",
"multicolored tail",
"multicolored thighhighs",
"multilingual",
"multiple belts",
"multiple boys",
"multiple bracelets",
"multiple cats",
"multiple condoms",
"multiple crossover",
"multiple dogs",
"multiple drawing challenge",
"multiple earrings",
"multiple girls",
"multiple hair bows",
"multiple hairpins",
"multiple heads",
"multiple horns",
"multiple legs",
"multiple moles",
"multiple monitors",
"multiple moons",
"multiple others",
"multiple penises",
"multiple persona",
"multiple piercings",
"multiple riders",
"multiple rings",
"multiple scars",
"multiple swords",
"multiple tails",
"multiple views",
"multiple wings",
"mummy costume",
"mumyou-sakanagare",
"muneate",
"muscle cuirass",
"muscular",
"muscular female",
"muscular male",
"museum",
"mushroom",
"mushroom on head",
"music",
"musical note",
"musical note print",
"musket",
"musketeer",
"mustache",
"mutation",
"muted color",
"muzzle",
"muzzle device",
"muzzle flash",
"my little pogchamp (meme)",
"n1 (rocket)",
"nagasaki",
"nagato (battleship)",
"naginata",
"nail",
"nail art",
"nail bat",
"nail clippers",
"nail polish",
"nail polish bottle",
"naked apron",
"naked bandage",
"naked belt",
"naked chocolate",
"naked cloak",
"naked coat",
"naked hoodie",
"naked kimono",
"naked raincoat",
"naked ribbon",
"naked robe",
"naked sheet",
"naked shirt",
"naked sweater",
"naked tabard",
"naked towel",
"name connection",
"name tag",
"nanami touko (cosplay)",
"nape",
"napkin",
"naranja academy school uniform",
"narrow waist",
"narrowed eyes",
"nasa",
"nasa logo",
"nationale volksarmee",
"native american",
"native american clothes",
"nattou",
"nature",
"naughty face",
"naval uniform",
"navel",
"navel cutout",
"navel focus",
"navel piercing",
"navy",
"navy seal copypasta",
"nazar (amulet)",
"nazi",
"nazi flag",
"nebula",
"neck",
"neck bell",
"neck fur",
"neck ribbon",
"neck ring",
"neck ruff",
"neck tassel",
"neck tattoo",
"neckerchief",
"necklace",
"necktie",
"necktie between breasts",
"necktie grab",
"necktie in mouth",
"neckwear grab",
"neco spirit",
"needle",
"negligee",
"nejime",
"nemes",
"nengajou",
"neon lights",
"neon palette",
"neon trim",
"nerd emoji",
"nerv",
"nervous",
"nervous smile",
"nervous sweating",
"net",
"netherlands",
"netorare",
"netorase",
"new year",
"new york city police department",
"news",
"newspaper",
"nib pen (object)",
"night",
"night sky",
"night vision device",
"nightgown",
"nightstand",
"nigirizushi",
"nihonga",
"nihongami",
"nike",
"nikon (company)",
"ninja",
"nintendo 3ds",
"nintendo ds",
"nintendo switch",
"nipple bar",
"nipple bells",
"nipple chain",
"nipple clamps",
"nipple cutout",
"nipple jewelry",
"nipple piercing",
"nipple rings",
"nipple slip",
"nipple stimulation",
"nipple tag",
"nipple tweak",
"nipples",
"nipples pressed together",
"nishizawa",
"nissan",
"nissan 180sx",
"nissan 300zx",
"nissan 300zx (z32)",
"nissan fairlady z",
"nissan s13 silvia",
"nissan silvia",
"nissan skyline",
"nissan skyline gt-r",
"nissan skyline r32",
"nissan skyline r34",
"nissin cup noodle",
"nlaw",
"no ai logo",
"no anus",
"no bra",
"no eyes",
"no feet",
"no headwear",
"no heterochromia",
"no horny (meme)",
"no humans",
"no legwear",
"no mouth",
"no nose",
"no panties",
"no pants",
"no parking sign",
"no pupils",
"no pussy",
"no sclera",
"no shirt",
"no shoes",
"no smoking",
"no socks",
"no stopping sign",
"no symbol",
"no-show socks",
"noh mask",
"non-binary flag",
"non-humanoid robot",
"nontraditional miko",
"nontraditional playboy bunny",
"noodles",
"nori (seaweed)",
"north korean flag",
"northrop tacit blue",
"nose",
"nose art",
"nose blush",
"nose bubble",
"nose grab",
"nose mask",
"nose piercing",
"nose ring",
"nosebleed",
"noses touching",
"nostrils",
"notched ear",
"notched lapels",
"note",
"notebook",
"notepad",
"nothing personnel kid (meme)",
"notice lines",
"notorious b.i.g. (cosplay)",
"notre dame de paris",
"novel cover",
"now draw her giving birth (meme)",
"nozzle",
"nsv",
"nude",
"nude cover",
"nudist",
"number tattoo",
"numbered",
"nun",
"nurse",
"nurse cap",
"nursing handjob",
"nuzzle",
"nva uniform",
"nyan",
"nyantcha (style)",
"o-ring",
"o-ring belt",
"o-ring bikini",
"o-ring choker",
"o-ring dress",
"o-ring thigh strap",
"o-ring top",
"o3o",
"o_o",
"obese",
"obi",
"obiage",
"obidome",
"obijime",
"object head",
"object in pocket",
"object insertion",
"object on head",
"objectification",
"obliques",
"obrez",
"obs studio",
"occult",
"ocean",
"octopus",
"oekaki",
"oerlikon 20mm gun",
"off shoulder",
"off-shoulder dress",
"off-shoulder jacket",
"off-shoulder shirt",
"off-shoulder sweater",
"office",
"office chair",
"office lady",
"official alternate costume",
"official art inset",
"ofuda",
"ofuda between fingers",
"ogre",
"oh-58 kiowa",
"ohisashiburi (style)",
"oi parking area",
"oil",
"oil painting (medium)",
"oil-paper umbrella",
"ojou-sama pose",
"ok sign",
"ok sign over eye",
"okamoto condoms",
"okobo",
"old",
"old man",
"old woman",
"omelet",
"omikuji",
"omurice",
"on back",
"on bed",
"on bench",
"on chair",
"on couch",
"on desk",
"on floor",
"on grass",
"on ground",
"on head",
"on lap",
"on moon",
"on motorcycle",
"on one knee",
"on person",
"on pillow",
"on railing",
"on rock",
"on roof",
"on scooter",
"on shoulder",
"on side",
"on stairs",
"on stomach",
"on table",
"on top of pole",
"on vehicle",
"on water",
"one breast out",
"one eye closed",
"one eye covered",
"one finger selfie challenge (meme)",
"one side up",
"one-eyed",
"one-hour drawing challenge",
"one-piece swimsuit",
"one-piece swimsuit pull",
"one-piece tan",
"one-piece thong",
"onee-loli",
"onee-shota",
"onesie",
"oni",
"oni horns",
"oni mask",
"onigiri",
"onion",
"onmyouji",
"onsen",
"onsen symbol",
"opaque glasses",
"open bag",
"open book",
"open box",
"open bra",
"open can",
"open cardigan",
"open clothes",
"open coat",
"open collar",
"open door",
"open drawer",
"open dress",
"open fly",
"open hand",
"open hands",
"open hatch",
"open hoodie",
"open jacket",
"open kimono",
"open mouth",
"open pajamas",
"open robe",
"open shirt",
"open shorts",
"open sign",
"open vest",
"open window",
"open-chest sweater",
"opened by self",
"opening door",
"opera cake",
"opossum",
"oppai challenge",
"oppai loli",
"optical illusion",
"optical sight",
"oral",
"oral invitation",
"orange (fruit)",
"orange apron",
"orange background",
"orange bag",
"orange bikini",
"orange bow",
"orange bowtie",
"orange cat",
"orange choker",
"orange dress",
"orange eyes",
"orange flower",
"orange footwear",
"orange gloves",
"orange hair",
"orange headwear",
"orange hoodie",
"orange jacket",
"orange moon",
"orange nails",
"orange necktie",
"orange nipples",
"orange panties",
"orange pants",
"orange penis",
"orange print",
"orange pupils",
"orange ribbon",
"orange robe",
"orange scarf",
"orange sclera",
"orange scrunchie",
"orange shirt",
"orange shorts",
"orange skin",
"orange skirt",
"orange sky",
"orange slice",
"orange sports bra",
"orange sweater",
"orange tail",
"orange theme",
"orange thighhighs",
"orange tongue",
"orange tree",
"orange vest",
"orange-shaped earrings",
"orange-tinted eyewear",
"orb",
"orc",
"orgasm",
"orgy",
"origami",
"orion (constellation)",
"ork (warhammer)",
"ornament",
"ornament focus",
"ornate ring",
"orrery",
"otaku",
"otaku room",
"other focus",
"other with female",
"otoko no ko",
"ots-38 stechkin",
"otto von bismarck (cosplay)",
"ottoman empire",
"out of frame",
"out-of-frame censoring",
"outdoors",
"outline",
"outside border",
"outstretched arm",
"outstretched arms",
"outstretched hand",
"outstretched leg",
"outstretched legs",
"oven",
"oven mitts",
"over shoulder",
"over-kneehighs",
"over-rim eyewear",
"overall shorts",
"overalls",
"overcast",
"overexposure",
"overgrown",
"overhead line",
"overpass",
"oversized animal",
"oversized clothes",
"oversized food",
"oversized forearms",
"oversized limbs",
"oversized object",
"oversized shirt",
"oviraptor",
"ovum",
"owl girl",
"own a musket for home defense (meme)",
"own hands clasped",
"own hands together",
"oxygen tank",
"pacifier",
"package",
"padded armor",
"padded jacket",
"padded pants",
"padded vest",
"paddle",
"padlock",
"page number",
"pain",
"paint",
"paint on clothes",
"paint roller",
"paint splatter",
"paint splatter on face",
"paint tube",
"paintbrush",
"paintbrush hair ornament",
"painterly",
"painting (action)",
"painting (medium)",
"painting (object)",
"paisley",
"paizuri",
"paizuri on lap",
"paizuri under clothes",
"pajamas",
"paladin",
"palantir",
"pale color",
"pale skin",
"palette (object)",
"palette knife",
"palm leaf",
"palm tree",
"palms together",
"pancake",
"panda print",
"pandakorya",
"paneled background",
"panicking",
"pant suit",
"pantaloons",
"panther (tank)",
"panties",
"panties around one ankle",
"panties around one leg",
"panties aside",
"panties day",
"panties under pantyhose",
"pantograph",
"pants",
"pants around one leg",
"pants pull",
"pants rolled up",
"pants tucked in",
"pants under dress",
"pants under shorts",
"panty lift",
"panty peek",
"panty pull",
"panty straps",
"pantyhose",
"pantyhose pull",
"pantyhose under shorts",
"pantyhose under trousers",
"pantylines",
"pantyshot",
"panzer i",
"panzer ii",
"panzer iii",
"panzer iv",
"panzerfaust",
"panzerfaust 3",
"paper",
"paper airplane",
"paper bag",
"paper crane",
"paper fan",
"paper lantern",
"paper stack",
"parachute",
"parasite",
"parasol",
"paratrooper",
"parent and child",
"parfait",
"paris",
"park",
"park bench",
"parka",
"parking lot",
"parody",
"parrot",
"parted bangs",
"parted hair",
"parted lips",
"partially blind",
"partially colored",
"partially fingerless gloves",
"partially opaque glasses",
"partially submerged",
"partially unbuttoned",
"partially undressed",
"partially unzipped",
"partially visible vulva",
"partisan",
"party hat",
"party whistle",
"pasgt helmet",
"pasta",
"pastel colors",
"pasties",
"pastry",
"pastry box",
"patch",
"path",
"patreon logo",
"patreon username",
"patriotism",
"patterned",
"patterned background",
"patterned clothing",
"patting back",
"pauldrons",
"pause button",
"pavise",
"paw gloves",
"paw pose",
"paw print",
"paw print background",
"paw print soles",
"paw shoes",
"pawpads",
"pc engine",
"pc-98 (computer)",
"pc-98 (style)",
"peaked cap",
"peanut",
"pearl (gemstone)",
"pearl bracelet",
"pearl earrings",
"pearl necklace",
"pearl thong",
"peas",
"pectorals",
"pedal board",
"pedestrian bridge",
"pedicure",
"pee",
"pee in container",
"peeing",
"peeking",
"peeking out",
"peeking through fingers",
"peephole",
"pelt",
"pelvic curtain",
"pelvic curtain aside",
"pelvic curtain lift",
"pen",
"pen (medium)",
"pen to mouth",
"pencil",
"pencil as mustache",
"pencil case",
"pencil dress",
"pencil sharpener",
"pencil skirt",
"pendant",
"pendant choker",
"pendulum",
"penguin",
"penis",
"penis awe",
"penis biting",
"penis grab",
"penis in pantyhose",
"penis on face",
"penis on tongue",
"penis out",
"penis over eyes",
"penis peek",
"penis size difference",
"penis tentacle",
"penis under another's clothes",
"penis under clothing",
"penises touching",
"penny loafers",
"pennywise in the sewer (meme)",
"pensive",
"pentagram",
"pentagram necklace",
"people",
"people's liberation army",
"pepper (spice)",
"pepper shaker",
"peril",
"perineum",
"perky breasts",
"persimmon",
"personification",
"perspective",
"pervert",
"pestle",
"pet",
"pet bowl",
"petals",
"peterprime",
"petite",
"petticoat",
"petting",
"phallic symbol",
"phimosis",
"phoenix",
"phone",
"phone booth",
"phone with ears",
"photo (medium)",
"photo (object)",
"photo album",
"photo background",
"photo inset",
"photo stand-in",
"photorealistic",
"physics",
"piano",
"piano bench",
"pickelhaube",
"pickle",
"pickup truck",
"picnic",
"picnic basket",
"pico de orizaba",
"picture frame",
"pie",
"piercing",
"pig ears",
"pig print",
"pigeon",
"pigeon-toed",
"piggy bank",
"piggyback",
"pike (weapon)",
"piledriver (sex)",
"pill",
"pillar",
"pillarboxed",
"pillow",
"pillow grab",
"pillow hug",
"pilot",
"pilot helmet",
"pilot suit",
"pilot uniform",
"pilum",
"pimple",
"pinafore dress",
"pinching",
"pinching sleeves",
"pindad ss2",
"pine tree",
"pink apron",
"pink armband",
"pink background",
"pink bag",
"pink belt",
"pink bikini",
"pink blood",
"pink border",
"pink bow",
"pink bowtie",
"pink bra",
"pink camisole",
"pink cardigan",
"pink choker",
"pink clouds",
"pink coat",
"pink collar",
"pink dress",
"pink eyes",
"pink flower",
"pink footwear",
"pink fur",
"pink hair",
"pink hairband",
"pink hakama",
"pink headwear",
"pink hoodie",
"pink jacket",
"pink kimono",
"pink legwear",
"pink lips",
"pink lipstick tube",
"pink mask",
"pink nails",
"pink neckerchief",
"pink necktie",
"pink pajamas",
"pink panties",
"pink pants",
"pink ribbon",
"pink rose",
"pink sailor collar",
"pink scarf",
"pink scrunchie",
"pink shirt",
"pink shorts",
"pink skin",
"pink skirt",
"pink sky",
"pink socks",
"pink suit",
"pink sweater",
"pink sweater vest",
"pink tank top",
"pink theme",
"pink thighhighs",
"pink umbrella",
"pink vest",
"pink-framed eyewear",
"pink-tinted eyewear",
"pinky out",
"pinky swear",
"pinned",
"pinstripe bow",
"pinstripe jacket",
"pinstripe pattern",
"pinstripe suit",
"pinup (style)",
"pipe bomb",
"pipe wrench",
"pirate",
"pirate hat",
"pirate ship",
"piss drawer",
"piston",
"pitcher (container)",
"pitchfork",
"pith helmet",
"pixel art",
"pixelated",
"pixie cut",
"pixiv id",
"pixiv logo",
"pixiv username",
"pizza",
"pizza box",
"pizza slice",
"pkm",
"plague doctor",
"plague doctor mask",
"plaid",
"plaid ascot",
"plaid background",
"plaid bikini",
"plaid bow",
"plaid bowtie",
"plaid dress",
"plaid jacket",
"plaid kimono",
"plaid necktie",
"plaid pants",
"plaid pillow",
"plaid ribbon",
"plaid scarf",
"plaid shirt",
"plaid shorts",
"plaid skirt",
"plaid vest",
"plain",
"planet",
"plank",
"planking",
"plant",
"plant girl",
"plantar flexion",
"planted",
"planted arrow",
"planted axe",
"planted knife",
"planted shield",
"planted spear",
"planted sword",
"planted umbrella",
"planting",
"plap",
"plasma cutter",
"plasma sword",
"plastic bag",
"plastic bottle",
"plastic wrap",
"plate",
"plate armor",
"plate carrier",
"plate stack",
"platform boots",
"platform footwear",
"platinum blonde hair",
"play button",
"playboy bunny",
"playground",
"playing bass",
"playing card",
"playing card print",
"playing card theme",
"playing games",
"playing instrument",
"playing sports",
"playing with own hair",
"playstation controller",
"playstation portable",
"pleading face emoji",
"pleated dress",
"pleated shirt",
"pleated skirt",
"pleated sleeves",
"plug",
"plug (piercing)",
"plugsuit",
"plume",
"plumeria",
"plump",
"plunger",
"plunging neckline",
"pm md 63/65",
"pocket",
"pocket square",
"pocket watch",
"pocky",
"pocky day",
"pocky in mouth",
"pocky kiss",
"podium",
"poem",
"pointer",
"pointing",
"pointing at another",
"pointing at self",
"pointing at viewer",
"pointing down",
"pointing forward",
"pointing up",
"pointless censoring",
"pointless condom",
"pointy breasts",
"pointy ears",
"pointy nose",
"poke ball",
"poke ball (basic)",
"poke ball symbol",
"poke ball theme",
"pokemon (creature)",
"poker chip",
"poking",
"poland",
"polar bear",
"polaroid",
"pole",
"pole dancing",
"polearm",
"polehammer",
"poleyn",
"police",
"police badge",
"police car",
"police hat",
"police uniform",
"policeman",
"policewoman",
"polish army",
"polish flag",
"polish text",
"politics",
"polka dot",
"polka dot background",
"polka dot bow",
"polka dot dress",
"polka dot legwear",
"polka dot pajamas",
"polka dot pants",
"polka dot scrunchie",
"polka dot shirt",
"polo shirt",
"pom pom (cheerleading)",
"pom pom (clothes)",
"pom pom beanie",
"pom pom hair ornament",
"pommel",
"poncho",
"pond",
"pondering my orb (meme)",
"ponytail",
"pool",
"pool ladder",
"pool of blood",
"poolside",
"popcorn",
"popped button",
"popsicle",
"popsicle stick",
"porch",
"pork",
"pornography",
"porsche",
"porsche 911",
"porsche 964",
"portal (object)",
"portrait",
"portrait (object)",
"portuguese text",
"pose",
"possessed",
"possum ears",
"possum girl",
"possum tail",
"post and rail fence",
"post-apocalypse",
"postcard",
"poster (medium)",
"poster (object)",
"potato chips",
"potion",
"potted plant",
"pouch",
"pouncing",
"pouring",
"pout",
"pouty lips",
"pov",
"pov across bed",
"pov across table",
"pov bullying",
"pov cheek warming (meme)",
"pov crotch",
"pov dating",
"pov doorway",
"pov hands",
"pov legs",
"pov peephole",
"pov shadow",
"power armor",
"power bank",
"power cord",
"power lines",
"power suit",
"powering up",
"pp-19-01",
"ppsh-41",
"praetor suit",
"prattkeeping (meme)",
"praying",
"precum",
"precum drip",
"precum string",
"precum through clothes",
"predicament bondage",
"pregnant",
"prehensile tongue",
"presenting",
"presenting foot",
"presenting removed panties",
"price",
"price tag",
"priest",
"priestess",
"princess",
"princess carry",
"pringles can",
"print (medium)",
"print bikini",
"print bra",
"print coat",
"print dress",
"print gloves",
"print hair",
"print headwear",
"print hoodie",
"print jacket",
"print kimono",
"print necktie",
"print robe",
"print sheath",
"print shirt",
"print skirt",
"print socks",
"print thighhighs",
"print vest",
"printer",
"prison",
"prison cell",
"prison clothes",
"procrastination",
"product placement",
"profanity",
"profile",
"profile picture",
"programming (topic)",
"progression",
"projected inset",
"projectile cum",
"prone bone",
"propaganda",
"propeller",
"prostate",
"prostate milking",
"prosthesis",
"prosthetic arm",
"prosthetic hand",
"prosthetic leg",
"prostitution",
"protecting",
"pteruges",
"ptsd",
"pubic hair",
"pubic hair peek",
"pubic stubble",
"pubic tattoo",
"public indecency",
"public masturbation",
"public nudity",
"public service announcement",
"puckered anus",
"puckered lips",
"puddle",
"puff of air",
"puffy cheeks",
"puffy chest",
"puffy lips",
"puffy long sleeves",
"puffy nipples",
"puffy short sleeves",
"puffy sleeves",
"pulled by another",
"pulled by self",
"pulling",
"pulling back",
"pump action",
"pumpkin",
"pumps",
"pun",
"punching",
"punishment",
"punk",
"puppet",
"puppet strings",
"purity seal",
"purple armband",
"purple background",
"purple bikini",
"purple blood",
"purple bodysuit",
"purple bow",
"purple bowtie",
"purple bra",
"purple butterfly",
"purple cardigan",
"purple choker",
"purple collar",
"purple dress",
"purple eyeliner",
"purple eyes",
"purple eyeshadow",
"purple flower",
"purple footwear",
"purple garter belt",
"purple gemstone",
"purple gloves",
"purple hair",
"purple hakama",
"purple headwear",
"purple hoodie",
"purple horns",
"purple jacket",
"purple kimono",
"purple legwear",
"purple leotard",
"purple lips",
"purple liquid",
"purple nails",
"purple neckerchief",
"purple necktie",
"purple one-piece swimsuit",
"purple outline",
"purple pajamas",
"purple panties",
"purple pants",
"purple pantyhose",
"purple pubic hair",
"purple pupils",
"purple ribbon",
"purple robe",
"purple rose",
"purple sash",
"purple scales",
"purple scarf",
"purple scrunchie",
"purple shirt",
"purple shorts",
"purple skin",
"purple skirt",
"purple sky",
"purple socks",
"purple sweater",
"purple tail",
"purple theme",
"purple thighhighs",
"purple tongue",
"purple vest",
"purple-framed eyewear",
"purring",
"pursed lips",
"push-button",
"pussy",
"pussy juice",
"pussy juice drip through clothes",
"pussy juice on fingers",
"pussy juice puddle",
"pussy juice stain",
"pussy juice trail",
"pussy peek",
"pussy piercing",
"puttee",
"putting on gloves",
"putting on shoes",
"puzzle piece hair ornament",
"pyramid (structure)",
"pyramid head (cosplay)",
"qbz-191",
"qinghua (porcelain)",
"qixiong ruqun",
"qr code",
"quad tails",
"quadruplets",
"quarter note",
"queen",
"queen (chess)",
"queen of spades symbol",
"quest",
"queue",
"quick ball",
"quill",
"quintuplets",
"quiver",
"rabbit",
"rabbit boy",
"rabbit ears",
"rabbit girl",
"rabbit hair ornament",
"rabbit hood",
"rabbit mask",
"rabbit pajamas",
"rabbit pose",
"rabbit print",
"rabbit tail",
"raccoon ears",
"raccoon girl",
"race queen",
"race vehicle",
"racecar",
"racetrack",
"racism",
"racket",
"radar",
"radiation symbol",
"radio",
"radio antenna",
"raft",
"raglan sleeves",
"rags",
"railing",
"railroad crossing",
"railroad signal",
"railroad tracks",
"rain",
"rainbow",
"rainbow gradient",
"rainbow hair",
"rainbow text",
"raincoat",
"raised eyebrow",
"raised eyebrows",
"raised fist",
"rally car",
"ram (computer)",
"ramen",
"randoseru",
"ranguage",
"rape",
"rapier",
"rattle",
"ravenclaw",
"ray-ban",
"razor",
"razor blade",
"reaching",
"reaching towards viewer",
"reactive armor",
"reading",
"ready to draw",
"real life insert",
"real world location",
"realistic",
"rear naked choke",
"rear-view mirror",
"rearing",
"rebar",
"receipt",
"reclining",
"recoil",
"record",
"recorder",
"recording",
"rectangular eyewear",
"rectangular mouth",
"rectangular pupils",
"recurring image",
"recursion",
"recycling",
"recycling symbol",
"red apple",
"red apron",
"red armband",
"red armor",
"red ascot",
"red background",
"red bag",
"red bikini",
"red bodysuit",
"red border",
"red bow",
"red bowtie",
"red bra",
"red camisole",
"red cape",
"red capelet",
"red cardigan",
"red carpet",
"red choker",
"red circle",
"red cloak",
"red coat",
"red collar",
"red cross",
"red curtains",
"red dress",
"red eyes",
"red eyeshadow",
"red flag",
"red flower",
"red footwear",
"red fur",
"red garter belt",
"red gemstone",
"red gloves",
"red hair",
"red hairband",
"red hakama",
"red halo",
"red headband",
"red headwear",
"red hood",
"red hoodie",
"red horns",
"red jacket",
"red kimono",
"red legwear",
"red leotard",
"red lips",
"red moon",
"red nails",
"red neckerchief",
"red necktie",
"red oni",
"red outline",
"red pajamas",
"red panda",
"red panda ears",
"red panda girl",
"red panda tail",
"red panties",
"red pants",
"red pupils",
"red ribbon",
"red robe",
"red rope",
"red rose",
"red sailor collar",
"red sash",
"red scales",
"red scarf",
"red sclera",
"red scrunchie",
"red shirt",
"red shorts",
"red skin",
"red skirt",
"red sky",
"red sleeves",
"red socks",
"red sports bra",
"red square",
"red star",
"red stripes",
"red sweater",
"red tail",
"red tassel",
"red theme",
"red thighhighs",
"red track suit",
"red trim",
"red tulip",
"red tunic",
"red umbrella",
"red vest",
"red wine",
"red wings",
"red-crowned crane",
"red-framed eyewear",
"red-tinted eyewear",
"redesign",
"reeds",
"reference inset",
"reference sheet",
"reflection",
"reflective floor",
"reflective surface",
"reflective table",
"reflective water",
"reflex sight",
"refraction",
"refrigerator",
"refrigerator interior",
"refrigerator magnet",
"refueling",
"reichsadler",
"reichstag",
"reins",
"rejected kiss",
"religion",
"reloading",
"remembering",
"remington model 700",
"remote control",
"remote control vibrator",
"removing bra",
"removing eyewear",
"removing jacket",
"removing legwear",
"removing shoes",
"removing sock",
"reptile",
"reptile girl",
"reptilian",
"republic of china army",
"republic of korea army",
"rerebrace",
"respirator",
"rest in peace (phrase)",
"restaurant",
"restrained",
"restraints",
"retort stand",
"retro artstyle",
"revealing clothes",
"reverse cowgirl position",
"reverse grip",
"reverse outfit",
"reverse palettes",
"reverse suspended congress",
"reverse trap",
"reverse upright straddle",
"revolver",
"rewind button",
"rgd-33",
"rgd-5",
"ribbed dress",
"ribbed legwear",
"ribbed panties",
"ribbed shirt",
"ribbed sleeves",
"ribbed socks",
"ribbed sweater",
"ribbed swimsuit",
"ribbed thighhighs",
"ribbon",
"ribbon bar",
"ribbon braid",
"ribbon choker",
"ribbon earrings",
"ribbon in mouth",
"ribbon of saint george",
"ribbon spool",
"ribbon trim",
"ribbon-trimmed legwear",
"ribbon-trimmed sleeves",
"ribbon-trimmed thighhighs",
"ribs",
"rice",
"rice bowl",
"rice cooker",
"rice hat",
"rice paddy",
"riding",
"riding bicycle",
"riding boots",
"riding crop",
"rifle",
"rifle cartridge",
"rifleman's creed",
"right-over-left kimono",
"right-to-left comic",
"rimless eyewear",
"ring",
"ring 411",
"ring bell",
"ring box",
"ring pull",
"ringed eyes",
"ringlets",
"riot shield",
"ripping",
"ripples",
"river",
"rivets",
"rk62",
"road",
"road sign",
"roasted sweet potato",
"robbery",
"robe",
"robot",
"robot animal",
"robot ears",
"robot fish",
"robot girl",
"robot joints",
"rock",
"rock paper scissors",
"rocket",
"rocket launcher",
"rocking chair",
"roe",
"rogatywka",
"rogue",
"role reversal",
"rolleiflex",
"roller coaster",
"roller skates",
"rolling eyes",
"rolling suitcase",
"romaji text",
"roman armor",
"roman clothes",
"roman empire",
"roman numeral",
"romania",
"romanian flag",
"rome (city)",
"ronald mcdonald (cosplay)",
"rondel",
"rooftop",
"room",
"rooster",
"roots",
"rope",
"rosary",
"rose",
"rose bush",
"rose print",
"rosemary (herb)",
"rotor",
"rough sex",
"round eyewear",
"round image",
"round table",
"round teeth",
"round window",
"round-bottom flask",
"roundel",
"route 66",
"royal navy",
"rpd",
"rpg (weapon)",
"rpg-7",
"rpk-16",
"rubber band",
"rubber boots",
"rubber duck",
"rubber duck hair ornament",
"rubber gloves",
"rubbing eyes",
"rubble",
"rudder footwear",
"ruffling hair",
"rug",
"ruins",
"ruler",
"runes",
"running",
"runny makeup",
"runny nose",
"runway",
"rural",
"russia",
"russian air force",
"russian anti-war flag",
"russian army",
"russian empire",
"russian flag",
"russian text",
"russo-ukrainian war",
"rust",
"rv",
"s&w m29",
"s&w m39",
"sabaton",
"saber (weapon)",
"sacabambaspis",
"sack",
"sad",
"sad keanu (meme)",
"sad smile",
"saddle",
"safety glasses",
"safety pin",
"safety pin piercing",
"safety razor",
"sagging breasts",
"sailboat",
"sailor",
"sailor collar",
"sailor dress",
"sailor hat",
"sailor moon (cosplay)",
"sailor senshi uniform",
"sailor shirt",
"saint",
"saizeriya",
"sajkaca",
"sakazuki",
"sake",
"sake bottle",
"sakura no tomoru hi e",
"sakuragaoka high school uniform",
"sakuramon",
"salad",
"salad bowl",
"saliva",
"saliva drip",
"saliva on breasts",
"saliva trail",
"sallet",
"salmon",
"salmon (fish)",
"salt",
"salt shaker",
"salute",
"sam browne belt",
"same-hada",
"same-sex bathing",
"sample watermark",
"samurai",
"sanbou",
"sand",
"sand castle",
"sand sculpture",
"sandals",
"sandbag",
"sandwich",
"sandwiched",
"sangvis ferri",
"sanpaku",
"santa bikini",
"santa costume",
"santa dress",
"santa gloves",
"santa hat",
"santa leotard",
"sapporo beer",
"sarashi",
"sash",
"sashimi",
"satchel",
"satellite",
"satellite dish",
"satin",
"satin bra",
"satin panties",
"satire",
"sattou (style)",
"sauce",
"saucer",
"sauna",
"sausage",
"sausage slice",
"savannah",
"saw",
"saya (scabbard)",
"sbd dauntless",
"scabbard",
"scale armor",
"scales",
"scalpel",
"scanlines",
"scar",
"scar across eye",
"scar on arm",
"scar on cheek",
"scar on chest",
"scar on face",
"scar on forehead",
"scar on hand",
"scar on leg",
"scar on neck",
"scar on nose",
"scar on stomach",
"scare",
"scared",
"scarf",
"scarf grab",
"scarf over mouth",
"scary maze game",
"scene reference",
"scenery",
"scepter",
"school",
"school bag",
"school chair",
"school desk",
"school hat",
"school nurse",
"school swimsuit",
"school uniform",
"science",
"science fiction",
"scientist",
"scissors",
"scolding",
"sconce",
"scooter",
"scope",
"scoreboard",
"scorpion",
"scout trooper",
"scowl",
"scratched",
"scratches",
"screaming",
"screen",
"screen light",
"screencap inset",
"screentones",
"screw",
"screw hair ornament",
"screwdriver",
"scroll",
"scrunchie",
"scuba",
"scuba gear",
"scuba tank",
"sculpting",
"sculpture",
"scylla",
"scythe",
"sea monster",
"sea spray",
"seagull",
"seal (animal)",
"seal script",
"seal team six",
"seamed legwear",
"seams",
"seaplane",
"search bar",
"searchlight",
"seashell",
"seaweed",
"security checkpoint",
"security guard",
"seductive smile",
"see-through",
"see-through bra",
"see-through dress",
"see-through legwear",
"see-through panties",
"see-through shawl",
"see-through shirt",
"see-through silhouette",
"see-through skirt",
"see-through skirt layer",
"see-through sleeves",
"see-through swimsuit",
"sega mega drive",
"seggs (meme)",
"segment display",
"segmented comic",
"seigaiha",
"seiza",
"self bondage",
"self breast sucking",
"self exposure",
"self-harm scar",
"selfie",
"semi truck",
"semi-circular eyewear",
"semi-rimless eyewear",
"sennheiser",
"sepia",
"sepia background",
"sequential",
"serafuku",
"seraph",
"serbia",
"serious",
"server",
"serving",
"sesame seeds",
"sesshouseki",
"seven-segment display",
"sewer grate",
"sewing machine",
"sex",
"sex ed",
"sex from behind",
"sex machine",
"sex toy",
"sex toy pull",
"sexting",
"sexual coaching",
"sexually suggestive",
"shackles",
"shade",
"shaded face",
"shading eyes",
"shadow",
"shadow company",
"shadow puppet",
"shaking",
"shako cap",
"shallow water",
"shampoo bottle",
"shanghai",
"shared artificial vagina",
"shared bathing",
"shared blanket",
"shared drink",
"shared earphones",
"shared umbrella",
"sharing",
"sharing food",
"sharingan",
"shark",
"shark bag",
"shark fin",
"shark girl",
"shark tail",
"sharp fingernails",
"sharp teeth",
"sharp toenails",
"shaved head",
"shaving",
"shaving another",
"shaving cream",
"shaving crotch",
"shawl",
"she versus he thought bubble (meme)",
"shearing",
"shears",
"sheath",
"sheathed",
"sheathing",
"shedding",
"shedding fur",
"sheep",
"sheep ears",
"sheep girl",
"sheep horns",
"sheep tail",
"sheet grab",
"sheet music",
"shelf",
"shell",
"shell casing",
"shell earrings",
"shelving book",
"shepherd",
"sheriff",
"shiba inu",
"shibari",
"shibari over clothes",
"shibuya (tokyo)",
"shibuya 109",
"shibuya scramble crossing",
"shide",
"shield",
"shiitake",
"shikoro",
"shimakaze (kancolle) (cosplay)",
"shimenawa",
"shin guards",
"shinai",
"shiny",
"shiny clothes",
"shiny footwear",
"shiny legwear",
"shiny lips",
"shiny pantyhose",
"shiny pokemon",
"shiny skin",
"ship",
"shiromuku",
"shirt",
"shirt bow",
"shirt grab",
"shirt in mouth",
"shirt lift",
"shirt on shoulders",
"shirt overhang",
"shirt partially tucked in",
"shirt pull",
"shirt slip",
"shirt tucked in",
"shirt tug",
"shoal",
"shoe dangle",
"shoe flower",
"shoe locker pov",
"shoe loss",
"shoe soles",
"shoelaces",
"shoes",
"shooing",
"shooting range",
"shooting star",
"shop",
"shopping",
"shopping bag",
"shopping basket",
"shopping cart",
"shore",
"short bangs",
"short dress",
"short eyebrows",
"short hair",
"short hair with long locks",
"short kimono",
"short necktie",
"short over long sleeves",
"short ponytail",
"short shorts",
"short sidetail",
"short sleeves",
"short sword",
"short twintails",
"shorts",
"shorts around one leg",
"shorts aside",
"shorts pull",
"shorts under shorts",
"shorts under skirt",
"shortstack",
"shot down",
"shot glass",
"shotgun",
"shotgun shell",
"shouji",
"shoulder angel",
"shoulder armor",
"shoulder bag",
"shoulder belt",
"shoulder blades",
"shoulder blush",
"shoulder boards",
"shoulder cannon",
"shoulder cutout",
"shoulder devil",
"shoulder pads",
"shoulder patch",
"shoulder plates",
"shoulder spikes",
"shoulder strap",
"shoulder tattoo",
"shout lines",
"shouting",
"shovel",
"shower (place)",
"shower curtain",
"shower head",
"showering",
"showgirl skirt",
"shrimp",
"shrimp fried this rice (meme)",
"shrimp tempura",
"shrine",
"shrug (clothing)",
"shrugging",
"shuriken hair ornament",
"shushing",
"shy",
"siblings",
"sick",
"side ahoge",
"side braid",
"side braids",
"side cape",
"side ponytail",
"side slit",
"side-by-side",
"side-seamed gloves",
"side-seamed legwear",
"side-tie bikini bottom",
"side-tie panties",
"side-tie peek",
"side-view mirror",
"sideboob",
"sideburns",
"sidecar",
"sidelighting",
"sidelocks",
"sidewalk",
"sideways",
"sideways glance",
"sideways hat",
"sideways mouth",
"sig 556",
"sig mcx",
"sig p220/p226",
"sig sauer",
"sig sauer p320",
"sig ssg3000",
"sight magnifier",
"sign",
"signal bar",
"signal flag",
"signal lamp",
"signature",
"sigrunen",
"silent comic",
"silhouette",
"silhouette target",
"silk",
"silver footwear",
"silver hair",
"simple background",
"simple bird",
"simulated fellatio",
"simulated paizuri",
"singing",
"single bare leg",
"single bare shoulder",
"single boot",
"single braid",
"single bridal gauntlet",
"single couter",
"single earphone removed",
"single earring",
"single elbow glove",
"single elbow pad",
"single empty eye",
"single epaulette",
"single fingerless glove",
"single flame",
"single garter strap",
"single gauntlet",
"single glove",
"single hair bun",
"single hair intake",
"single half glove",
"single hand",
"single horizontal stripe",
"single horn",
"single knee pad",
"single kneehigh",
"single leg pantyhose",
"single mechanical arm",
"single mechanical hand",
"single mitten",
"single pauldron",
"single shoe",
"single side bun",
"single sidelock",
"single sleeve",
"single sock",
"single stripe",
"single thighhigh",
"single-lens reflex camera",
"sink",
"sir arthur (makaimura) (cosplay)",
"sisters",
"site of grace",
"sitting",
"sitting backwards",
"sitting in window",
"sitting on bench",
"sitting on box",
"sitting on car",
"sitting on desk",
"sitting on face",
"sitting on lap",
"sitting on object",
"sitting on person",
"sitting on pillow",
"sitting on rock",
"sitting on shoulder",
"sitting on stairs",
"sitting on table",
"sitting sideways",
"size difference",
"skate park",
"skateboard",
"skateboarding",
"skates",
"skating",
"skeleton",
"sketch",
"sketchbook",
"sketching",
"skewer",
"skid mark",
"skin fang",
"skin fangs",
"skin tight",
"skin-covered horns",
"skindentation",
"skinny",
"skirt",
"skirt grab",
"skirt hold",
"skirt lift",
"skirt pull",
"skirt set",
"skirt suit",
"skirt tug",
"skirt under dress",
"skort",
"sks",
"skull",
"skull and crossbones",
"skull and crossed swords",
"skull earrings",
"skull hair ornament",
"skull hat ornament",
"skull mask",
"skull necklace",
"skull ornament",
"skull print",
"skull ring",
"skullcap",
"sky",
"skylight",
"skylight (architecture)",
"skyline",
"skyscraper",
"slam dunk (basketball)",
"slap mark",
"slaps roof of car (meme)",
"slave",
"sleep mask",
"sleeping",
"sleeping animal",
"sleeping on desk",
"sleeping on person",
"sleeping upright",
"sleepwear",
"sleepy",
"sleeve bow",
"sleeve cuffs",
"sleeve garter",
"sleeve grab",
"sleeve pull",
"sleeve ribbon",
"sleeve rolled up",
"sleeveless",
"sleeveless dress",
"sleeveless jacket",
"sleeveless kimono",
"sleeveless shirt",
"sleeveless sweater",
"sleeveless turtleneck",
"sleeveless turtleneck leotard",
"sleeves past elbows",
"sleeves past fingers",
"sleeves past wrists",
"sleeves pushed up",
"sleeves rolled up",
"sleigh",
"sliced cheese",
"sliding doors",
"slim legs",
"slime (creature)",
"slime (substance)",
"slime girl",
"slimification",
"slingshot swimsuit",
"slippers",
"slit pupils",
"slouch hat",
"slouching",
"slovenly",
"slug girl",
"slums",
"small breasts",
"small head",
"small penis",
"small testicles",
"smartphone",
"smartphone case",
"smartwatch",
"smeared lipstick",
"smell",
"smelling",
"smelling clothes",
"smelling hair",
"smelling underwear",
"smersh",
"smile",
"smiley face",
"smirk",
"smoke",
"smoke grenade",
"smoke trail",
"smokestack",
"smoking",
"smoking gun",
"smoking pipe",
"smother",
"smug",
"smug trap (meme)",
"snack",
"snail girl",
"snail hands",
"snail shell",
"snake",
"snake hair",
"snake mouth",
"snake tattoo",
"snap-fit buckle",
"snapchat",
"sneakers",
"sneaking",
"sneaking suit",
"sneed's feed and seed (meme)",
"sniper rifle",
"sniper team",
"sniping",
"snoot challenge",
"snorkel",
"snot",
"snout",
"snow",
"snow globe",
"snow rabbit",
"snow shovel",
"snowball",
"snowball fight",
"snowflake earrings",
"snowflake hair ornament",
"snowflake ornament",
"snowflake print",
"snowflakes",
"snowing",
"snowman",
"snowman print",
"soaking feet",
"soap bubbles",
"soap censor",
"sobbing",
"soccer",
"soccer ball",
"soccer uniform",
"sock pull",
"socks",
"socks over pantyhose",
"soda",
"soda bottle",
"soda can",
"sode",
"softboiled egg",
"solar system",
"soldering iron",
"soldier",
"soles",
"solid circle eyes",
"solid circle pupils",
"solid eyes",
"solid oval eyes",
"solid state drive",
"solo",
"solo focus",
"solraka",
"sombrero",
"song name",
"sos",
"sound effects",
"soup",
"soup ladle",
"soviet",
"soviet army",
"soviet flag",
"space",
"space cat (meme)",
"space helmet",
"space marine",
"space print",
"space shuttle",
"space station",
"spacecraft",
"spacecraft interior",
"spacesuit",
"spade (shape)",
"spade tattoo",
"spaghetti",
"spaghetti strap",
"spanish inquisition",
"spanish text",
"spanked",
"spanking",
"sparkle",
"sparkle background",
"sparkler",
"sparkling eyes",
"sparks",
"sparrow",
"spartan (halo)",
"spatula",
"speaker",
"spear",
"special air service",
"speech bubble",
"speed limit sign",
"speed lines",
"sperm cell",
"spetsnaz",
"sphinx",
"spider",
"spider girl",
"spider print",
"spider web",
"spider web print",
"spidersona",
"spiked armlet",
"spiked armor",
"spiked belt",
"spiked boots",
"spiked bracelet",
"spiked choker",
"spiked collar",
"spiked dildo",
"spiked ear piercing",
"spiked footwear",
"spiked gauntlets",
"spiked hair",
"spiked hairband",
"spiked hood",
"spiked knuckles",
"spiked legwear",
"spiked mace",
"spiked pauldrons",
"spikes",
"spill",
"spilling",
"spine",
"spinning",
"spinning top",
"spinosaurus",
"spiral",
"spiral staircase",
"spire",
"spirit",
"spitroast",
"splashing",
"splatter",
"split",
"split mouth",
"split screen",
"split theme",
"split-color hair",
"spoken anger vein",
"spoken blush",
"spoken character",
"spoken ellipsis",
"spoken emoji",
"spoken exclamation mark",
"spoken flower",
"spoken flying sweatdrops",
"spoken heart",
"spoken interrobang",
"spoken mars symbol",
"spoken musical note",
"spoken object",
"spoken question mark",
"spoken squiggle",
"spoken sweatdrop",
"spoken symbol",
"sponsor",
"spoon",
"spooning",
"sports bikini",
"sports bra",
"sports bra lift",
"sports bra pull",
"sports car",
"sports festival",
"sports utility vehicle",
"sportswear",
"spot color",
"spotify",
"spotlight",
"spotting scope",
"sprain",
"spray bottle",
"spray can",
"spray paint",
"spraying",
"spread anus",
"spread arms",
"spread ass",
"spread fingers",
"spread legs",
"spread navel",
"spread pussy",
"spread pussy under clothes",
"spread toes",
"spreader bar",
"spring (season)",
"spring onion",
"spurs",
"square",
"square 4koma",
"squatting",
"squatting cowgirl position",
"squeezing",
"squeezing testicles",
"squiggle",
"squinting",
"sr-25",
"ss insignia",
"ss uniform",
"st basil's cathedral",
"st. andrew's cross",
"staccato 2011",
"stacking",
"stadium",
"staff",
"stag beetle",
"stage lights",
"stahlhelm",
"stained clothes",
"stained glass",
"stained panties",
"stairs",
"stairwell",
"stalking",
"stance",
"stand (jojo)",
"standard bearer",
"standing",
"standing cunnilingus",
"standing on box",
"standing on one leg",
"standing on three legs",
"standing sex",
"standing split",
"star (sky)",
"star (symbol)",
"star balloon",
"star censor",
"star earrings",
"star facial mark",
"star hair ornament",
"star halo",
"star hat ornament",
"star in eye",
"star ornament",
"star tattoo",
"star-shaped pupils",
"staring",
"starry sky",
"starry sky print",
"state of puebla",
"state of veracruz",
"station",
"station attendant",
"stationery",
"stats",
"statue",
"steak",
"steal her look (meme)",
"stealth fingering",
"stealth masturbation",
"stealth paizuri",
"stealth sex",
"steam",
"steam censor",
"steaming body",
"steampunk",
"steel beam",
"steelseries",
"steepled fingers",
"steering wheel",
"stencil (object)",
"step-brother and step-sister",
"step-siblings",
"stepped on",
"stepping stones",
"stereo",
"sterling smg",
"stethoscope",
"stew",
"stg44",
"stick",
"stick figure",
"sticker",
"sticker on face",
"sticks",
"sticky",
"sticky note",
"stiletto heels",
"still life",
"stinger",
"stirrup legwear",
"stirrups (riding)",
"stitches",
"stocks",
"stole",
"stomach",
"stomach bulge",
"stomach day",
"stomach tattoo",
"stone",
"stone floor",
"stone stairs",
"stone walkway",
"stone wall",
"stool",
"stop sign",
"stopwatch",
"storefront",
"storm",
"storm cloud",
"storm drain",
"stove",
"straddling",
"straight hair",
"straight-on",
"straitjacket",
"strangling",
"strap",
"strap between breasts",
"strap gap",
"strap lift",
"strap pull",
"strap slip",
"strap-on",
"strapless",
"strapless bikini",
"strapless bottom",
"strapless bra",
"strapless dress",
"strapless leotard",
"strapless shirt",
"strappy heels",
"straw hat",
"strawberry",
"strawberry cake",
"strawberry earrings",
"strawberry hair ornament",
"strawberry milk",
"strawberry pocky",
"strawberry shortcake",
"strawberry slice",
"stray pubic hair",
"streaked hair",
"stream",
"streaming tears",
"street",
"stress",
"stretching",
"strichtarn",
"string",
"string bikini",
"string bra",
"string choker",
"string lights",
"string of fate",
"string of flags",
"string panties",
"string phone",
"striped",
"striped ascot",
"striped background",
"striped bikini",
"striped bow",
"striped bowtie",
"striped clothes",
"striped dress",
"striped hair",
"striped headwear",
"striped hoodie",
"striped horns",
"striped jacket",
"striped kimono",
"striped necktie",
"striped pajamas",
"striped panties",
"striped pants",
"striped pantyhose",
"striped ribbon",
"striped scarf",
"striped shirt",
"striped shorts",
"striped skin",
"striped skirt",
"striped sleeves",
"striped socks",
"striped sweater",
"striped tail",
"striped tank top",
"striped thighhighs",
"stripper",
"stripper pole",
"stroking own chin",
"strong",
"strong zero",
"stubble",
"stuck",
"stud earrings",
"studded belt",
"studded bracelet",
"studded choker",
"studded collar",
"studded trim",
"studying",
"stuffed animal",
"stuffed bird",
"stuffed cat",
"stuffed cow",
"stuffed crocodile",
"stuffed dog",
"stuffed dragon",
"stuffed mouse",
"stuffed mushroom",
"stuffed panda",
"stuffed penguin",
"stuffed pig",
"stuffed rabbit",
"stuffed shark",
"stuffed squirrel",
"stuffed toy",
"stuffed whale",
"stun gun",
"style parody",
"stylus",
"su-27",
"su-57",
"submachine gun",
"submarine",
"submerged",
"subtitled",
"subway",
"subway station",
"sucking both nipples",
"sucking on multiple breasts",
"suction cups",
"suggestive fluid",
"sugoi dekai",
"suit",
"suit jacket",
"suitcase",
"sukeban",
"sukiyaki",
"sumida (tokyo)",
"summer",
"summer festival",
"summer uniform",
"summoning",
"sun",
"sun hair ornament",
"sun hat",
"sun shower",
"sun symbol",
"sun tattoo",
"sunbathing",
"sunbeam",
"sunburst",
"sundae",
"sundress",
"sunflower",
"sunflower field",
"sunglasses",
"sunken cheeks",
"sunlight",
"sunrise",
"sunset",
"super mushroom",
"superhero",
"supermarket",
"suppressor",
"supreme (brand)",
"surcoat",
"surfboard",
"surgical mask",
"surprise kiss",
"surprised",
"surreal",
"surrounded",
"surrounded by penises",
"sushi",
"suspended congress",
"suspender shorts",
"suspender skirt",
"suspenders",
"suspenders pull",
"sv-98",
"svt-38",
"swaddled",
"swallow (bird)",
"swan",
"swastika",
"swat",
"sweat",
"sweatband",
"sweatdrop",
"sweater",
"sweater around waist",
"sweater dress",
"sweater lift",
"sweater pull",
"sweater tucked in",
"sweater vest",
"sweater vest lift",
"sweatpants",
"sweaty clothes",
"sweden",
"swedish flag",
"swedish text",
"swedish uniform",
"sweet potato",
"sweets",
"swept bangs",
"swim ring",
"swimming",
"swimsuit",
"swimsuit around one leg",
"swimsuit aside",
"swimsuit cover-up",
"swimsuit hanger",
"swimsuit lift",
"swimsuit tug",
"swing",
"swing set",
"swinging",
"swinging legs",
"swirl lollipop",
"swiss roll",
"switchblade",
"swivel chair",
"sword",
"sword behind back",
"sword on back",
"sword over shoulder",
"sword writing",
"swordbreaker (weapon)",
"symbol in eye",
"symbol-shaped eyes",
"symbol-shaped pupils",
"symbolism",
"symmetrical docking",
"synthesizer",
"syria",
"syrian civil war",
"syringe",
"t-34",
"t-64",
"t-64bv",
"t-72",
"t-pose",
"t-shirt",
"t91 assault rifle",
"tabard",
"tabby cat",
"tabi",
"table",
"tablecloth",
"tablet pc",
"tachi (weapon)",
"tachi-e",
"tack (riding)",
"tactical clothes",
"tactical maid",
"tactile paving",
"tag",
"tail",
"tail bell",
"tail between legs",
"tail biting",
"tail bow",
"tail brushing",
"tail grab",
"tail insertion",
"tail lights",
"tail ornament",
"tail piercing",
"tail raised",
"tail removed",
"tail ribbon",
"tail ring",
"tail wagging",
"tail wrap",
"tailcoat",
"tailjob",
"taiwan",
"taiyaki",
"take it home",
"take your pick",
"taking notes",
"taking picture",
"taking shelter",
"tako-san wiener",
"takoyaki",
"talisman",
"talking",
"talking animal",
"talking on phone",
"tall",
"tall female",
"tall grass",
"tall lady shopping in japanese store (meme)",
"tally",
"talons",
"tamagoyaki",
"tan",
"tank",
"tank helmet",
"tank top",
"tank turret",
"tankard",
"tanline peek",
"tanlines",
"tanuki",
"tape",
"tape dispenser",
"tape gag",
"tape on nipples",
"taped note",
"tapir",
"tareme",
"target practice",
"tart (food)",
"tassel",
"tassel choker",
"tassel earrings",
"tassel hair ornament",
"tasting",
"tasuki",
"tatami",
"tattoo",
"taur",
"taut clothes",
"taut dress",
"taut pants",
"taut shirt",
"taut skirt",
"taut swimsuit",
"tavern",
"tea",
"teacher",
"teacher and student",
"teacup",
"teapot",
"teardrop",
"tearing clothes",
"tearing up",
"tears",
"teasing",
"tech support",
"teddy (lingerie)",
"teddy bear",
"teddy bear hair ornament",
"teeth",
"teleport",
"teleporter",
"telescope",
"television",
"telnyashka",
"telogreika",
"temari ball",
"template",
"temple",
"tempura",
"tenga",
"tennis",
"tennis ball",
"tennis racket",
"tennis skirt",
"tennis uniform",
"tent",
"tentacle hair",
"tentacle sex",
"tentacles",
"tentacles under clothes",
"tented shirt",
"teruterubouzu",
"test",
"test score (paper)",
"test tube",
"testicle grab",
"testicle sucking",
"testicles",
"testicles touching",
"tetrapod",
"text background",
"text focus",
"text messaging",
"text print",
"textbook",
"thai student uniform",
"thank you",
"thatched roof",
"the cooler daniel (meme)",
"the north face",
"the pose",
"the west has fallen (meme)",
"theater seating",
"theft",
"thermal paste",
"thermos",
"thick eyebrows",
"thick eyelashes",
"thick lips",
"thick thighs",
"thigh belt",
"thigh boots",
"thigh gap",
"thigh grab",
"thigh holster",
"thigh pouch",
"thigh ribbon",
"thigh sex",
"thigh strap",
"thighband pantyhose",
"thighhighs",
"thighhighs over pantyhose",
"thighhighs under boots",
"thighlet",
"thighs",
"thinking",
"thinkpad",
"third eye",
"this egg got me acting unwise (meme)",
"thompson submachine gun",
"thong",
"thong aside",
"thong bikini",
"thong leotard",
"thorns",
"thought bubble",
"thread",
"three sizes",
"threesome",
"throat microphone",
"throne",
"through clothes",
"through door",
"through medium",
"through screen",
"throwing",
"thumb ring",
"thumbs up",
"thumbtack",
"thurible",
"tiara",
"tiara removed",
"ticket",
"tickling",
"tickling stomach",
"tie clip",
"tie fighter",
"tied hair",
"tied shirt",
"tied to chair",
"tied up (nonsexual)",
"tiered skirt",
"tiered tray",
"tiger",
"tiger costume",
"tiger cub",
"tiger ears",
"tiger i",
"tiger print",
"tiger tail",
"tight clothes",
"tight pants",
"tight shirt",
"tights day",
"tile ceiling",
"tile floor",
"tile roof",
"tile wall",
"tiles",
"tilted headwear",
"tiltrotor",
"tim hortons",
"timbougami",
"time lapse",
"time paradox",
"timestamp",
"tinted eyewear",
"tiptoe kiss",
"tiptoes",
"tire",
"tire swing",
"tissue",
"tissue box",
"title",
"title parody",
"tnt",
"tnt block (minecraft)",
"to be continued",
"toast",
"toddler",
"toe cleavage",
"toe ring",
"toe scrunch",
"toe seam",
"toeless footwear",
"toeless legwear",
"toenail polish",
"toenails",
"toes",
"toga",
"toggles",
"toilet",
"toilet brush",
"toilet paper",
"toilet paper tube",
"toilet stall",
"toilet use",
"tokkuri",
"tokusatsu",
"tokyo (city)",
"tokyo skytree",
"tomato",
"tomboy",
"tomoe (symbol)",
"toned",
"toned male",
"tongue",
"tongue out",
"tongue piercing",
"tonguejob",
"tony hawk's existential nightmare (meme)",
"too bad! it was just me! (meme)",
"too literal",
"too long",
"too many",
"too many books",
"too many butterflies",
"too many condoms",
"too many sex toys",
"too much food",
"toolbox",
"toon (style)",
"tooth",
"tooth gap",
"tooth necklace",
"toothbrush",
"toothpaste",
"top hat",
"top-down bottom-up",
"topknot",
"topless",
"topless male",
"torch",
"torii",
"torn",
"torn buruma",
"torn cape",
"torn cloak",
"torn clothes",
"torn dress",
"torn hat",
"torn jeans",
"torn legwear",
"torn pants",
"torn pantyhose",
"torn scarf",
"torn shirt",
"torn shorts",
"torn sleeves",
"torn swimsuit",
"torn thighhighs",
"torn wings",
"tornado",
"torogao",
"torpedo",
"torso flash",
"torso grab",
"torture",
"tossing",
"tote bag",
"totenkopf",
"touching another's back",
"tourbox",
"towel",
"towel around neck",
"tower",
"town",
"toy",
"toy airplane",
"toy car",
"toy gun",
"toy tank",
"toyota",
"toyota supra",
"toyota supra mk iii",
"track and field",
"track jacket",
"track marks",
"track pants",
"track suit",
"track uniform",
"traction beam",
"tractor",
"trade offer (meme)",
"traditional chinese text",
"traditional clothes",
"traditional dress",
"traditional media",
"traditional nun",
"traffic",
"traffic barrier",
"traffic baton",
"traffic cone",
"traffic light",
"trail",
"trailer",
"train",
"train (clothing)",
"train interior",
"train station",
"train station platform",
"trans rights",
"transformation",
"transgender flag",
"translation note",
"translucent",
"translucent skin",
"transmission tower",
"transparent",
"transparent background",
"transparent curtains",
"transparent footwear",
"transparent raincoat",
"transparent umbrella",
"trash",
"trash bag",
"trash can",
"travel attendant",
"tray",
"treasure chest",
"tree",
"tree shade",
"tree stump",
"trellis",
"trembling",
"trench",
"trench coat",
"trial",
"triangle",
"triangle earrings",
"triangle mouth",
"triangle print",
"triangular headpiece",
"tribal",
"tribal tattoo",
"tricorne",
"tricycle",
"trident print",
"trigger discipline",
"trigram",
"triple penetration",
"triplets",
"tripod",
"tripping",
"triptych (art)",
"troll face",
"trophy",
"tropical drink",
"trowel",
"truck",
"truth",
"trying on clothes",
"tryzub",
"tsubasa tsubasa (style)",
"tsumami kanzashi",
"tsundere",
"tsurime",
"tube",
"tube top",
"tucked penis",
"tulip",
"tumbleweed",
"tunic",
"tunnel",
"turkey (country)",
"turkish clothes",
"turkish flag",
"turkish text",
"turn pale",
"turnaround",
"turning head",
"turret",
"turtle",
"turtle costume",
"turtleneck",
"turtleneck dress",
"turtleneck sweater",
"tusks",
"tutu",
"tuxedo",
"tweaking own nipple",
"tweezers",
"twig",
"twilight",
"twin braids",
"twin drills",
"twin-lens reflex camera",
"twins",
"twintails",
"twintails day",
"twisted breasts",
"twisted hair",
"twisted torso",
"twitching",
"twitter logo",
"twitter strip game (meme)",
"twitter username",
"twitter verified checkmark",
"two side up",
"two soyjaks pointing (meme)",
"two tails",
"two-footed footjob",
"two-handed",
"two-handed handjob",
"two-handed sword",
"two-sided cape",
"two-sided fabric",
"two-sided headwear",
"two-sided hoodie",
"two-tone background",
"two-tone bikini",
"two-tone bow",
"two-tone dress",
"two-tone eyes",
"two-tone footwear",
"two-tone gloves",
"two-tone hair",
"two-tone headwear",
"two-tone hoodie",
"two-tone jacket",
"two-tone legwear",
"two-tone necktie",
"two-tone panties",
"two-tone scarf",
"two-tone shirt",
"two-tone skin",
"two-tone skirt",
"two-tone socks",
"two-tone sports bra",
"two-tone sweater",
"two-tone swimsuit",
"tying",
"tying apron",
"tying footwear",
"tying hair",
"typing",
"typo",
"u_u",
"uc card",
"uchikake",
"uchiwa",
"udon",
"ufo",
"ugly man",
"uh-60 blackhawk",
"ukiyo-e",
"ukraine",
"ukrainian flag",
"ukrainian text",
"ultramarines",
"umbrella",
"umbrella octopus",
"umbrella stand",
"unamused",
"unbuttoned",
"unbuttoned jacket",
"unbuttoned shirt",
"uncensored",
"unconscious",
"unconventional maid",
"unconventional media",
"undead",
"under armour",
"under covers",
"under kotatsu",
"under table",
"under tree",
"under-rim eyewear",
"underbarrel grenade launcher",
"underboob",
"underboob cutout",
"underbust",
"underbutt",
"undercut",
"underground",
"underlighting",
"undershirt",
"undersized animal",
"undersized clothes",
"undertaker standing behind aj styles (meme)",
"underwater",
"underwear",
"underwear only",
"undone bra",
"undone necktie",
"undressing",
"undressing another",
"uneven eyes",
"uneven legwear",
"uneven sleeves",
"unfastened",
"uniform",
"union jack",
"unit patch",
"united kingdom",
"united nations",
"united states",
"united states army",
"united states marine corps",
"united states navy",
"universe",
"unkempt",
"unmoving pattern",
"unsheathed",
"unsheathing",
"untied bikini",
"untucked shirt",
"untying hair",
"unwanted creampie",
"unworn apron",
"unworn armor",
"unworn backpack",
"unworn bag",
"unworn belt",
"unworn bikini",
"unworn bikini top",
"unworn boots",
"unworn bra",
"unworn clothes",
"unworn dress",
"unworn earring",
"unworn eyewear",
"unworn goggles",
"unworn hair ornament",
"unworn hairclip",
"unworn hat",
"unworn headwear",
"unworn helmet",
"unworn jacket",
"unworn jewelry",
"unworn kimono",
"unworn legwear",
"unworn male underwear",
"unworn mask",
"unworn necktie",
"unworn panties",
"unworn pantyhose",
"unworn sandals",
"unworn scarf",
"unworn scrunchie",
"unworn shirt",
"unworn shoes",
"unworn shorts",
"unworn skirt",
"unworn socks",
"unworn sweater",
"unworn swimsuit",
"unzipped",
"uohhhhhhhhh! (meme)",
"updo",
"upper body",
"upper teeth only",
"upright straddle",
"upside-down",
"upskirt",
"upturned eyes",
"urban",
"urban camouflage",
"urban legend",
"urethra",
"uriko (baseball)",
"usb",
"used condom",
"used condom on penis",
"used tissue",
"usekh collar",
"user interface",
"ushanka",
"uss brooklyn (cl-40)",
"uss enterprise (cv-6)",
"uss essex (cv-9)",
"uss independence (cvl-22)",
"uss south dakota (bb-57)",
"uss washington (bb-56)",
"utensil in mouth",
"uterus",
"utility belt",
"utility pole",
"utility vest",
"uva academy school uniform",
"uwabaki",
"uwagi",
"uwu",
"v",
"v arms",
"v over eye",
"v-22 osprey",
"v-neck",
"v-shaped eyebrows",
"v-shaped eyes",
"vacation",
"vaginal",
"vaginal object insertion",
"valentine",
"vambraces",
"vampire",
"vampire costume",
"van",
"vanishing point",
"vanripper (style)",
"vans",
"vaporeon (cosplay)",
"varangian guard",
"varia suit",
"variations",
"vase",
"vaulting horse",
"vdv",
"vegetable",
"vegetation",
"vehicle and personification",
"vehicle focus",
"veil",
"veins",
"veiny arms",
"veiny hands",
"veiny penis",
"vending cart",
"vending machine",
"venice",
"venom snake (cosplay)",
"venus symbol",
"veranda",
"vertical foregrip",
"vertical stripes",
"vertical-striped clothes",
"vertical-striped dress",
"vertical-striped kimono",
"vertical-striped pants",
"vertical-striped pantyhose",
"vertical-striped scarf",
"vertical-striped shirt",
"vertical-striped skirt",
"very dark skin",
"very long fingernails",
"very long hair",
"very long sleeves",
"very long tail",
"very short hair",
"very sweaty",
"very wide shot",
"vest",
"vhs artifacts",
"vial",
"vibrator",
"vibrator cord",
"vibrator in thighhighs",
"vibrator under clothes",
"vibrator under panties",
"victorian",
"victorian maid",
"victory",
"video call",
"video camera",
"video game",
"videocassette",
"vietnam",
"vietnam war",
"vietnamese dress",
"vietnamese high school uniform",
"view between legs",
"viewer holding leash",
"viewfinder",
"vignetting",
"viking",
"village",
"vines",
"violin",
"virgin destroyer sweater",
"virgin killer outfit",
"virgin killer sweater",
"virgin mary (cosplay)",
"virgin vs chad (meme)",
"virtual reality",
"virtual youtuber",
"virus",
"visor",
"visor (armor)",
"visor cap",
"visor lift",
"visual basic (programming)",
"vkontakte username",
"vodka",
"voice recorder",
"volcano",
"volleyball",
"volleyball (object)",
"volleyball uniform",
"vorpal sword (day of wrath)",
"vss vintorez",
"vz. 58",
"w",
"w arms",
"wa lolita",
"wading",
"waffen-ss",
"wagashi",
"wagon",
"waist apron",
"waist cape",
"waistcoat",
"waistpack",
"wait a minute... this isn't tennis! this is anal sex!! (meme)",
"waiting",
"waitress",
"wake",
"waking up",
"walk-in",
"walkie-talkie",
"walking",
"walking away",
"walking bike",
"walking on liquid",
"walkman",
"wall",
"wall clock",
"wall lamp",
"wall of text",
"wallet chain",
"wallpaper (object)",
"wand",
"war",
"war flag",
"war hammer",
"wardrobe malfunction",
"warehouse",
"wariza",
"warning sign",
"warped",
"warrior",
"warship",
"washbowl",
"washing",
"washing machine",
"wataboushi",
"watch",
"watching",
"watching television",
"water",
"water bottle",
"water dragon",
"water drop",
"water lily flower",
"water pipe",
"watercolor (medium)",
"watercolor effect",
"watercraft",
"waterfall",
"watermark",
"watermelon",
"watermelon bar",
"watermelon print",
"watson amelia (cosplay)",
"watson cross",
"waves",
"waving",
"wavy eyes",
"wavy hair",
"wavy mouth",
"weapon",
"weapon behind back",
"weapon case",
"weapon family",
"weapon focus",
"weapon name",
"weapon on back",
"weapon over shoulder",
"weapon rack",
"weasel",
"weathergirl",
"web address",
"webley revolver",
"wedding",
"wedding dress",
"wedding ring",
"wedgie",
"wehrmacht",
"weibo logo",
"weibo username",
"weighing breasts",
"weighing scale",
"weight conscious",
"weightlifting",
"weights",
"welsh corgi",
"werewolf",
"western comics (style)",
"western dragon",
"wet",
"wet clothes",
"wet dream",
"wet dress",
"wet face",
"wet floor",
"wet ground",
"wet hair",
"wet panties",
"wet pantyhose",
"wet shirt",
"wet skirt",
"wet swimsuit",
"wetland",
"wetsuit",
"whale",
"whale tail (clothing)",
"what",
"wheat",
"wheat field",
"wheelchair",
"wheellock",
"when you see it",
"where's the cat? (meme)",
"whip",
"whip marks",
"whipped cream",
"whisk",
"whisker markings",
"whiskers",
"whiskey",
"whispering",
"whistle",
"whistle around neck",
"whistling",
"white apron",
"white armband",
"white ascot",
"white background",
"white bag",
"white belt",
"white bikini",
"white bird",
"white bodysuit",
"white border",
"white bow",
"white bowtie",
"white bra",
"white butterfly",
"white camisole",
"white cane",
"white cape",
"white capelet",
"white cardigan",
"white cat",
"white choker",
"white cloak",
"white coat",
"white collar",
"white dress",
"white ensign",
"white eyes",
"white feathers",
"white flower",
"white footwear",
"white fur",
"white garter belt",
"white gloves",
"white hair",
"white hairband",
"white headband",
"white headdress",
"white headwear",
"white hoodie",
"white horns",
"white jacket",
"white kimono",
"white leggings",
"white legwear",
"white leotard",
"white lily",
"white mask",
"white mittens",
"white nails",
"white neckerchief",
"white necktie",
"white nightgown",
"white one-piece swimsuit",
"white outline",
"white overalls",
"white pajamas",
"white panties",
"white pants",
"white pantyhose",
"white pupils",
"white rabbit (animal)",
"white ribbon",
"white robe",
"white rose",
"white sailor collar",
"white sash",
"white scales",
"white scarf",
"white scrunchie",
"white serafuku",
"white shawl",
"white shirt",
"white shorts",
"white skin",
"white skirt",
"white sky",
"white sleeves",
"white snake",
"white sneakers",
"white socks",
"white sports bra",
"white stripes",
"white suit",
"white sweater",
"white sweater vest",
"white tail",
"white tank top",
"white theme",
"white thighhighs",
"white towel",
"white trim",
"white tulip",
"white tunic",
"white umbrella",
"white vest",
"white wings",
"white wrist cuffs",
"white wristband",
"whiteboard",
"wide brim",
"wide hips",
"wide oval eyes",
"wide shot",
"wide sleeves",
"wide spread legs",
"wide-eyed",
"wife and wife",
"wiffle gag",
"wifi symbol",
"wii remote",
"willow",
"wimple",
"wince",
"winch",
"winchester model 1866",
"winchester model 1887",
"winchester model 1897",
"wind",
"wind chime",
"wind lift",
"wind turbine",
"winding",
"windmill",
"window",
"window (computing)",
"window blinds",
"window shade",
"window shutter",
"windowsill",
"wine",
"wine bottle",
"wine glass",
"wing collar",
"wing hair ornament",
"wing ornament",
"wing tattoo",
"winged arms",
"winged footwear",
"winged helmet",
"wings",
"winter",
"winter clothes",
"winter coat",
"winter gloves",
"winter uniform",
"wiping face",
"wiping forehead",
"wiping sweat",
"wiping tears",
"wire",
"wireless earphones",
"wisteria",
"witch",
"witch hat",
"with a car you can go anywhere you want (meme)",
"wizard",
"wok",
"wolf",
"wolf boy",
"wolf cut",
"wolf ears",
"wolf girl",
"wolf hat",
"wolf pelt",
"wolf tail",
"woman shouting knives (meme)",
"woman yelling at cat (meme)",
"women want me fish fear me (meme)",
"women's wallet",
"wood",
"wood art",
"wooden bench",
"wooden box",
"wooden bucket",
"wooden chair",
"wooden fence",
"wooden floor",
"wooden horse",
"wooden lantern",
"wooden railing",
"wooden shield",
"wooden staff",
"wooden sword",
"wooden table",
"wooden wall",
"woodland camouflage",
"wool",
"woollen cap",
"working",
"workshop",
"world map",
"world war i",
"world war ii",
"worm",
"worried",
"wreath",
"wreckage",
"wrench",
"wringing clothes",
"wringing skirt",
"wrinkled fabric",
"wrinkled skin",
"wrist cuffs",
"wrist flower",
"wrist ribbon",
"wrist scrunchie",
"wrist wrap",
"wristband",
"wristwatch",
"writing",
"wrong foot",
"wrong hand",
"x",
"x hair ornament",
"x-2 shinshin",
"x-cross (bdsm)",
"x-ray",
"x-shaped pupils",
"xd",
"xm2010",
"y-wing",
"yakiniku",
"yamamura sadako (cosplay)",
"yami kawaii",
"yandere",
"yandere trance",
"yaoi",
"yawning",
"year of the ox",
"year of the pig",
"year of the rabbit",
"year of the tiger",
"yearbook",
"yellow apron",
"yellow armband",
"yellow ascot",
"yellow background",
"yellow bikini",
"yellow bow",
"yellow bowtie",
"yellow butterfly",
"yellow camisole",
"yellow cardigan",
"yellow choker",
"yellow dress",
"yellow eyes",
"yellow flower",
"yellow footwear",
"yellow gloves",
"yellow hairband",
"yellow halo",
"yellow headwear",
"yellow hoodie",
"yellow jacket",
"yellow kimono",
"yellow legwear",
"yellow lips",
"yellow nails",
"yellow neckerchief",
"yellow necktie",
"yellow outline",
"yellow pajamas",
"yellow panties",
"yellow pants",
"yellow pantyhose",
"yellow pupils",
"yellow raincoat",
"yellow ribbon",
"yellow rose",
"yellow sash",
"yellow scarf",
"yellow sclera",
"yellow scrunchie",
"yellow shawl",
"yellow shirt",
"yellow skin",
"yellow skirt",
"yellow sky",
"yellow socks",
"yellow sweater",
"yellow sweater vest",
"yellow tail",
"yellow tank top",
"yellow theme",
"yellow thighhighs",
"yellow tongue",
"yellow vest",
"yellow-framed eyewear",
"yellow-tinted eyewear",
"yes",
"yes-no pillow",
"yf-23",
"yin yang",
"yin yang earrings",
"yin yang orb",
"yin yang print",
"yoga",
"yoga mat",
"yoga pants",
"yokozuwari",
"yor briar (cosplay)",
"yoshi egg",
"you died",
"you gonna get raped",
"you're doing it wrong",
"young michael scott shaking ed truck's hand (meme)",
"yugake",
"yukata",
"yuki onna",
"yume kawaii",
"yume no tsue",
"yumi (bow)",
"yuri",
"z (russian symbol)",
"zebra print",
"zeon",
"zero gravity",
"zettai ryouiki",
"zipper",
"zipper footwear",
"zipper pull tab",
"zipping",
"zodiac",
"zombie",
"zombie pose",
"zoolander stare (meme)",
"zoom layer",
"zouri",
"zweihander",
"zzz",
"|_"
] |
dima806/cat_breed_image_detection
|
Detects cat breed (from the list of 48 common breeds) with about 77% accuracy based on image.
See https://www.kaggle.com/code/dima806/cat-breed-image-detection-vit for more details.
```
Classification report:
precision recall f1-score support
Abyssinian 0.9771 0.9628 0.9699 2258
American Bobtail 0.3837 0.4956 0.4325 2258
American Curl 0.9434 0.9376 0.9405 2258
American Shorthair 0.2603 0.0558 0.0919 2258
Applehead Siamese 0.9825 0.9920 0.9872 2258
Balinese 0.9191 0.9606 0.9394 2258
Bengal 0.7666 0.7724 0.7695 2258
Birman 0.8913 0.9371 0.9136 2258
Bombay 0.6471 0.8893 0.7491 2258
British Shorthair 0.7879 0.8720 0.8278 2258
Burmese 0.9124 0.9544 0.9329 2258
Calico 0.6144 0.6492 0.6314 2258
Cornish Rex 0.9960 0.9849 0.9904 2258
Devon Rex 0.9856 1.0000 0.9927 2258
Dilute Calico 0.6094 0.5540 0.5804 2258
Dilute Tortoiseshell 0.5747 0.7037 0.6327 2258
Domestic Long Hair 0.4103 0.3392 0.3714 2258
Domestic Medium Hair 0.2805 0.1120 0.1601 2258
Domestic Short Hair 0.9490 0.9810 0.9647 2258
Egyptian Mau 0.8900 0.9243 0.9068 2258
Exotic Shorthair 0.8720 0.8233 0.8469 2258
Extra-Toes Cat - Hemingway Polydactyl 0.3899 0.2055 0.2691 2258
Havana 0.9811 0.9889 0.9850 2258
Himalayan 0.8022 0.7201 0.7589 2258
Japanese Bobtail 0.9799 0.9725 0.9762 2258
Maine Coon 0.5321 0.6833 0.5983 2258
Manx 0.3942 0.1147 0.1777 2258
Munchkin 0.9325 0.9544 0.9433 2258
Nebelung 0.9639 0.9938 0.9786 2258
Norwegian Forest 0.8280 0.8463 0.8371 2258
Oriental Short Hair 0.8772 0.8096 0.8420 2258
Persian 0.8302 0.8012 0.8154 2258
Ragamuffin 0.9641 0.9872 0.9755 2258
Ragdoll 0.6866 0.6501 0.6679 2258
Russian Blue 0.8215 0.9256 0.8705 2258
Scottish Fold 0.9728 0.9181 0.9446 2258
Siamese 0.7649 0.7551 0.7600 2258
Siberian 0.9636 0.9619 0.9628 2258
Snowshoe 0.8389 0.8140 0.8263 2258
Sphynx 0.9942 0.9903 0.9922 2258
Tabby 0.3533 0.3441 0.3487 2258
Tiger 0.4071 0.6076 0.4876 2258
Tonkinese 0.9613 0.9464 0.9538 2258
Torbie 0.5544 0.6568 0.6013 2258
Tortoiseshell 0.6667 0.8122 0.7323 2258
Turkish Angora 0.7328 0.8308 0.7787 2258
Turkish Van 0.7658 0.9225 0.8369 2258
Tuxedo 0.6491 0.8357 0.7307 2258
accuracy 0.7698 108384
macro avg 0.7554 0.7698 0.7559 108384
weighted avg 0.7554 0.7698 0.7559 108384
```
|
[
"abyssinian",
"american bobtail",
"american curl",
"american shorthair",
"applehead siamese",
"balinese",
"bengal",
"birman",
"bombay",
"british shorthair",
"burmese",
"calico",
"cornish rex",
"devon rex",
"dilute calico",
"dilute tortoiseshell",
"domestic long hair",
"domestic medium hair",
"domestic short hair",
"egyptian mau",
"exotic shorthair",
"extra-toes cat - hemingway polydactyl",
"havana",
"himalayan",
"japanese bobtail",
"maine coon",
"manx",
"munchkin",
"nebelung",
"norwegian forest",
"oriental short hair",
"persian",
"ragamuffin",
"ragdoll",
"russian blue",
"scottish fold",
"siamese",
"siberian",
"snowshoe",
"sphynx",
"tabby",
"tiger",
"tonkinese",
"torbie",
"tortoiseshell",
"turkish angora",
"turkish van",
"tuxedo"
] |
pillIdentifierAI/pillIdentifier
|
This model is part of a school project.
Utilizing the google/vit-base-patch16-224 vision transformer for image classification,
this pre-trained model is further tuned utilizing images of pills and tablets.
As pills and tablets have three main features, color, shape, and imprint,
the model aims to identify images of pill and tablets by automatically extracting features.
The dataset utilized is from the U.S. Department of Health's Computational Photography Project for Pill Identification (C3PI).
DISCLAIMER: The accuracy of this model is currently low (<20%). Further training is currently ongoing to improve the accuracy.
Version 2: This version tries to train the pretrained model with only 20 of the most common pills.
Unfortunately, the accuract of the model is still currently low (<30%).
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11",
"label_12",
"label_13",
"label_14",
"label_15",
"label_16",
"label_17",
"label_18",
"label_19",
"label_20",
"label_21",
"label_22",
"label_23",
"label_24",
"label_25",
"label_26",
"label_27",
"label_28",
"label_29",
"label_30",
"label_31",
"label_32",
"label_33",
"label_34",
"label_35",
"label_36",
"label_37",
"label_38",
"label_39",
"label_40",
"label_41",
"label_42",
"label_43",
"label_44",
"label_45",
"label_46",
"label_47",
"label_48",
"label_49",
"label_50",
"label_51",
"label_52",
"label_53",
"label_54",
"label_55",
"label_56",
"label_57",
"label_58",
"label_59",
"label_60",
"label_61",
"label_62",
"label_63",
"label_64",
"label_65",
"label_66",
"label_67",
"label_68",
"label_69",
"label_70",
"label_71",
"label_72",
"label_73",
"label_74",
"label_75",
"label_76",
"label_77",
"label_78",
"label_79",
"label_80",
"label_81",
"label_82",
"label_83",
"label_84",
"label_85",
"label_86",
"label_87",
"label_88",
"label_89",
"label_90",
"label_91",
"label_92",
"label_93",
"label_94",
"label_95",
"label_96",
"label_97",
"label_98",
"label_99",
"label_100",
"label_101",
"label_102",
"label_103",
"label_104",
"label_105",
"label_106",
"label_107",
"label_108",
"label_109",
"label_110",
"label_111",
"label_112",
"label_113",
"label_114",
"label_115",
"label_116",
"label_117",
"label_118",
"label_119",
"label_120",
"label_121",
"label_122",
"label_123",
"label_124",
"label_125",
"label_126",
"label_127",
"label_128",
"label_129",
"label_130",
"label_131",
"label_132",
"label_133",
"label_134",
"label_135",
"label_136",
"label_137",
"label_138",
"label_139",
"label_140",
"label_141",
"label_142",
"label_143",
"label_144",
"label_145",
"label_146",
"label_147",
"label_148",
"label_149",
"label_150",
"label_151",
"label_152",
"label_153",
"label_154",
"label_155",
"label_156",
"label_157",
"label_158",
"label_159",
"label_160",
"label_161",
"label_162",
"label_163",
"label_164",
"label_165",
"label_166",
"label_167",
"label_168",
"label_169",
"label_170",
"label_171",
"label_172",
"label_173",
"label_174",
"label_175",
"label_176",
"label_177",
"label_178",
"label_179",
"label_180",
"label_181",
"label_182",
"label_183",
"label_184",
"label_185",
"label_186",
"label_187",
"label_188",
"label_189",
"label_190",
"label_191",
"label_192",
"label_193",
"label_194",
"label_195",
"label_196",
"label_197",
"label_198",
"label_199",
"label_200",
"label_201",
"label_202",
"label_203",
"label_204",
"label_205",
"label_206",
"label_207",
"label_208",
"label_209",
"label_210",
"label_211",
"label_212",
"label_213",
"label_214",
"label_215",
"label_216",
"label_217",
"label_218",
"label_219",
"label_220",
"label_221",
"label_222",
"label_223",
"label_224",
"label_225",
"label_226",
"label_227",
"label_228",
"label_229",
"label_230",
"label_231",
"label_232",
"label_233",
"label_234",
"label_235",
"label_236",
"label_237",
"label_238",
"label_239",
"label_240",
"label_241",
"label_242",
"label_243",
"label_244",
"label_245",
"label_246",
"label_247",
"label_248",
"label_249",
"label_250",
"label_251",
"label_252",
"label_253",
"label_254",
"label_255",
"label_256",
"label_257",
"label_258",
"label_259",
"label_260",
"label_261",
"label_262",
"label_263",
"label_264",
"label_265",
"label_266",
"label_267",
"label_268",
"label_269",
"label_270",
"label_271",
"label_272",
"label_273",
"label_274",
"label_275",
"label_276",
"label_277",
"label_278",
"label_279",
"label_280",
"label_281",
"label_282",
"label_283",
"label_284",
"label_285",
"label_286",
"label_287",
"label_288",
"label_289",
"label_290",
"label_291",
"label_292",
"label_293",
"label_294",
"label_295",
"label_296",
"label_297",
"label_298",
"label_299",
"label_300",
"label_301",
"label_302",
"label_303",
"label_304",
"label_305",
"label_306",
"label_307",
"label_308",
"label_309",
"label_310",
"label_311",
"label_312",
"label_313",
"label_314",
"label_315",
"label_316",
"label_317",
"label_318",
"label_319",
"label_320",
"label_321",
"label_322",
"label_323",
"label_324",
"label_325",
"label_326",
"label_327",
"label_328",
"label_329",
"label_330",
"label_331",
"label_332",
"label_333",
"label_334",
"label_335",
"label_336",
"label_337",
"label_338",
"label_339",
"label_340",
"label_341",
"label_342",
"label_343",
"label_344",
"label_345",
"label_346",
"label_347",
"label_348",
"label_349",
"label_350",
"label_351",
"label_352",
"label_353",
"label_354",
"label_355",
"label_356",
"label_357",
"label_358",
"label_359",
"label_360",
"label_361",
"label_362",
"label_363",
"label_364",
"label_365",
"label_366",
"label_367",
"label_368",
"label_369",
"label_370",
"label_371",
"label_372",
"label_373",
"label_374",
"label_375",
"label_376",
"label_377",
"label_378",
"label_379",
"label_380",
"label_381",
"label_382",
"label_383",
"label_384",
"label_385",
"label_386",
"label_387",
"label_388",
"label_389",
"label_390",
"label_391",
"label_392",
"label_393",
"label_394",
"label_395",
"label_396",
"label_397",
"label_398",
"label_399",
"label_400",
"label_401",
"label_402",
"label_403",
"label_404",
"label_405",
"label_406",
"label_407",
"label_408",
"label_409",
"label_410",
"label_411",
"label_412",
"label_413",
"label_414",
"label_415",
"label_416",
"label_417",
"label_418",
"label_419",
"label_420",
"label_421",
"label_422",
"label_423",
"label_424",
"label_425",
"label_426",
"label_427",
"label_428",
"label_429",
"label_430",
"label_431",
"label_432",
"label_433",
"label_434",
"label_435",
"label_436",
"label_437",
"label_438",
"label_439",
"label_440",
"label_441",
"label_442",
"label_443",
"label_444",
"label_445",
"label_446",
"label_447",
"label_448",
"label_449",
"label_450",
"label_451",
"label_452",
"label_453",
"label_454",
"label_455",
"label_456",
"label_457",
"label_458",
"label_459",
"label_460",
"label_461",
"label_462",
"label_463",
"label_464",
"label_465",
"label_466",
"label_467",
"label_468",
"label_469",
"label_470",
"label_471",
"label_472",
"label_473",
"label_474",
"label_475",
"label_476",
"label_477",
"label_478",
"label_479",
"label_480",
"label_481",
"label_482",
"label_483",
"label_484",
"label_485",
"label_486",
"label_487",
"label_488",
"label_489",
"label_490",
"label_491",
"label_492",
"label_493",
"label_494",
"label_495",
"label_496",
"label_497",
"label_498",
"label_499",
"label_500",
"label_501",
"label_502",
"label_503",
"label_504",
"label_505",
"label_506",
"label_507",
"label_508",
"label_509",
"label_510",
"label_511",
"label_512",
"label_513",
"label_514",
"label_515",
"label_516",
"label_517",
"label_518",
"label_519",
"label_520",
"label_521",
"label_522",
"label_523",
"label_524",
"label_525",
"label_526",
"label_527",
"label_528",
"label_529",
"label_530",
"label_531",
"label_532",
"label_533",
"label_534",
"label_535",
"label_536",
"label_537",
"label_538",
"label_539",
"label_540",
"label_541",
"label_542",
"label_543",
"label_544",
"label_545",
"label_546",
"label_547",
"label_548",
"label_549",
"label_550",
"label_551",
"label_552",
"label_553",
"label_554",
"label_555",
"label_556",
"label_557",
"label_558",
"label_559",
"label_560",
"label_561",
"label_562",
"label_563",
"label_564",
"label_565",
"label_566",
"label_567",
"label_568",
"label_569",
"label_570",
"label_571",
"label_572",
"label_573",
"label_574",
"label_575",
"label_576",
"label_577",
"label_578",
"label_579",
"label_580",
"label_581",
"label_582",
"label_583",
"label_584",
"label_585",
"label_586",
"label_587",
"label_588",
"label_589",
"label_590",
"label_591",
"label_592",
"label_593",
"label_594",
"label_595",
"label_596",
"label_597",
"label_598",
"label_599",
"label_600",
"label_601",
"label_602",
"label_603",
"label_604",
"label_605",
"label_606",
"label_607",
"label_608",
"label_609",
"label_610",
"label_611",
"label_612",
"label_613",
"label_614",
"label_615",
"label_616",
"label_617",
"label_618",
"label_619",
"label_620",
"label_621",
"label_622",
"label_623",
"label_624",
"label_625",
"label_626",
"label_627",
"label_628",
"label_629",
"label_630",
"label_631",
"label_632",
"label_633",
"label_634",
"label_635",
"label_636",
"label_637",
"label_638",
"label_639",
"label_640",
"label_641",
"label_642",
"label_643",
"label_644",
"label_645",
"label_646",
"label_647",
"label_648",
"label_649",
"label_650",
"label_651",
"label_652",
"label_653",
"label_654",
"label_655",
"label_656",
"label_657",
"label_658",
"label_659",
"label_660",
"label_661",
"label_662",
"label_663",
"label_664",
"label_665",
"label_666",
"label_667",
"label_668",
"label_669",
"label_670",
"label_671",
"label_672",
"label_673",
"label_674",
"label_675",
"label_676",
"label_677",
"label_678",
"label_679",
"label_680",
"label_681",
"label_682",
"label_683",
"label_684",
"label_685",
"label_686",
"label_687",
"label_688",
"label_689",
"label_690",
"label_691",
"label_692",
"label_693",
"label_694",
"label_695",
"label_696",
"label_697",
"label_698",
"label_699",
"label_700",
"label_701",
"label_702",
"label_703",
"label_704",
"label_705",
"label_706",
"label_707",
"label_708",
"label_709",
"label_710",
"label_711",
"label_712",
"label_713",
"label_714",
"label_715",
"label_716",
"label_717",
"label_718",
"label_719",
"label_720",
"label_721",
"label_722",
"label_723",
"label_724",
"label_725",
"label_726",
"label_727",
"label_728",
"label_729",
"label_730",
"label_731",
"label_732",
"label_733",
"label_734",
"label_735",
"label_736",
"label_737",
"label_738",
"label_739",
"label_740",
"label_741",
"label_742",
"label_743",
"label_744",
"label_745",
"label_746",
"label_747",
"label_748",
"label_749",
"label_750",
"label_751",
"label_752",
"label_753",
"label_754",
"label_755",
"label_756",
"label_757",
"label_758",
"label_759",
"label_760",
"label_761",
"label_762",
"label_763",
"label_764",
"label_765",
"label_766",
"label_767",
"label_768",
"label_769",
"label_770",
"label_771",
"label_772",
"label_773",
"label_774",
"label_775",
"label_776",
"label_777",
"label_778",
"label_779",
"label_780",
"label_781",
"label_782",
"label_783",
"label_784",
"label_785",
"label_786",
"label_787",
"label_788",
"label_789",
"label_790",
"label_791",
"label_792",
"label_793",
"label_794",
"label_795",
"label_796",
"label_797",
"label_798",
"label_799",
"label_800",
"label_801",
"label_802",
"label_803",
"label_804",
"label_805",
"label_806",
"label_807",
"label_808",
"label_809",
"label_810",
"label_811",
"label_812",
"label_813",
"label_814",
"label_815",
"label_816",
"label_817",
"label_818",
"label_819",
"label_820",
"label_821",
"label_822",
"label_823",
"label_824",
"label_825",
"label_826",
"label_827",
"label_828",
"label_829",
"label_830",
"label_831",
"label_832",
"label_833",
"label_834",
"label_835",
"label_836",
"label_837",
"label_838",
"label_839",
"label_840",
"label_841",
"label_842",
"label_843",
"label_844",
"label_845",
"label_846",
"label_847",
"label_848",
"label_849",
"label_850",
"label_851",
"label_852",
"label_853",
"label_854",
"label_855",
"label_856",
"label_857",
"label_858",
"label_859",
"label_860",
"label_861",
"label_862",
"label_863",
"label_864",
"label_865",
"label_866",
"label_867",
"label_868",
"label_869",
"label_870",
"label_871",
"label_872",
"label_873",
"label_874",
"label_875",
"label_876",
"label_877",
"label_878",
"label_879",
"label_880",
"label_881",
"label_882",
"label_883",
"label_884",
"label_885",
"label_886",
"label_887",
"label_888",
"label_889",
"label_890",
"label_891",
"label_892",
"label_893",
"label_894",
"label_895",
"label_896",
"label_897",
"label_898",
"label_899",
"label_900",
"label_901",
"label_902",
"label_903",
"label_904",
"label_905",
"label_906",
"label_907",
"label_908",
"label_909",
"label_910",
"label_911",
"label_912",
"label_913",
"label_914",
"label_915",
"label_916",
"label_917",
"label_918",
"label_919",
"label_920",
"label_921",
"label_922",
"label_923",
"label_924",
"label_925",
"label_926",
"label_927",
"label_928",
"label_929",
"label_930",
"label_931",
"label_932",
"label_933",
"label_934",
"label_935",
"label_936",
"label_937",
"label_938",
"label_939",
"label_940",
"label_941",
"label_942",
"label_943",
"label_944",
"label_945",
"label_946",
"label_947",
"label_948",
"label_949",
"label_950",
"label_951",
"label_952",
"label_953",
"label_954",
"label_955",
"label_956",
"label_957",
"label_958",
"label_959",
"label_960",
"label_961",
"label_962",
"label_963",
"label_964",
"label_965",
"label_966",
"label_967",
"label_968",
"label_969",
"label_970",
"label_971",
"label_972",
"label_973",
"label_974",
"label_975",
"label_976",
"label_977",
"label_978",
"label_979",
"label_980",
"label_981",
"label_982",
"label_983",
"label_984",
"label_985",
"label_986",
"label_987",
"label_988",
"label_989",
"label_990",
"label_991",
"label_992",
"label_993",
"label_994",
"label_995",
"label_996",
"label_997",
"label_998",
"label_999",
"label_1000",
"label_1001",
"label_1002",
"label_1003",
"label_1004",
"label_1005",
"label_1006",
"label_1007",
"label_1008",
"label_1009",
"label_1010",
"label_1011",
"label_1012",
"label_1013",
"label_1014",
"label_1015",
"label_1016",
"label_1017",
"label_1018",
"label_1019",
"label_1020",
"label_1021",
"label_1022",
"label_1023",
"label_1024",
"label_1025",
"label_1026",
"label_1027",
"label_1028",
"label_1029",
"label_1030",
"label_1031",
"label_1032",
"label_1033",
"label_1034",
"label_1035",
"label_1036",
"label_1037",
"label_1038",
"label_1039",
"label_1040",
"label_1041",
"label_1042",
"label_1043",
"label_1044",
"label_1045",
"label_1046",
"label_1047",
"label_1048",
"label_1049",
"label_1050",
"label_1051",
"label_1052",
"label_1053",
"label_1054",
"label_1055",
"label_1056",
"label_1057",
"label_1058",
"label_1059",
"label_1060",
"label_1061",
"label_1062",
"label_1063",
"label_1064",
"label_1065",
"label_1066",
"label_1067",
"label_1068",
"label_1069",
"label_1070",
"label_1071",
"label_1072",
"label_1073",
"label_1074",
"label_1075",
"label_1076",
"label_1077",
"label_1078",
"label_1079",
"label_1080",
"label_1081",
"label_1082",
"label_1083",
"label_1084",
"label_1085",
"label_1086",
"label_1087",
"label_1088",
"label_1089",
"label_1090",
"label_1091",
"label_1092",
"label_1093",
"label_1094",
"label_1095",
"label_1096",
"label_1097",
"label_1098",
"label_1099",
"label_1100",
"label_1101",
"label_1102",
"label_1103",
"label_1104",
"label_1105",
"label_1106",
"label_1107",
"label_1108",
"label_1109",
"label_1110",
"label_1111",
"label_1112",
"label_1113",
"label_1114",
"label_1115",
"label_1116",
"label_1117",
"label_1118",
"label_1119",
"label_1120",
"label_1121",
"label_1122",
"label_1123",
"label_1124",
"label_1125",
"label_1126",
"label_1127",
"label_1128",
"label_1129",
"label_1130",
"label_1131",
"label_1132",
"label_1133",
"label_1134",
"label_1135",
"label_1136",
"label_1137",
"label_1138",
"label_1139",
"label_1140",
"label_1141",
"label_1142",
"label_1143",
"label_1144",
"label_1145",
"label_1146",
"label_1147",
"label_1148",
"label_1149",
"label_1150",
"label_1151",
"label_1152",
"label_1153",
"label_1154",
"label_1155",
"label_1156",
"label_1157",
"label_1158",
"label_1159",
"label_1160",
"label_1161",
"label_1162",
"label_1163",
"label_1164",
"label_1165",
"label_1166",
"label_1167",
"label_1168",
"label_1169",
"label_1170",
"label_1171",
"label_1172",
"label_1173",
"label_1174",
"label_1175",
"label_1176",
"label_1177",
"label_1178",
"label_1179",
"label_1180",
"label_1181",
"label_1182",
"label_1183",
"label_1184",
"label_1185",
"label_1186",
"label_1187",
"label_1188",
"label_1189",
"label_1190",
"label_1191",
"label_1192",
"label_1193",
"label_1194",
"label_1195",
"label_1196",
"label_1197",
"label_1198",
"label_1199",
"label_1200",
"label_1201",
"label_1202",
"label_1203",
"label_1204",
"label_1205",
"label_1206",
"label_1207",
"label_1208",
"label_1209",
"label_1210",
"label_1211",
"label_1212",
"label_1213",
"label_1214",
"label_1215",
"label_1216",
"label_1217",
"label_1218",
"label_1219",
"label_1220",
"label_1221",
"label_1222",
"label_1223",
"label_1224",
"label_1225",
"label_1226",
"label_1227",
"label_1228",
"label_1229",
"label_1230",
"label_1231",
"label_1232",
"label_1233",
"label_1234",
"label_1235",
"label_1236",
"label_1237",
"label_1238",
"label_1239",
"label_1240",
"label_1241",
"label_1242",
"label_1243",
"label_1244",
"label_1245",
"label_1246",
"label_1247",
"label_1248",
"label_1249",
"label_1250",
"label_1251",
"label_1252",
"label_1253",
"label_1254",
"label_1255",
"label_1256",
"label_1257",
"label_1258",
"label_1259",
"label_1260",
"label_1261",
"label_1262",
"label_1263",
"label_1264",
"label_1265",
"label_1266",
"label_1267",
"label_1268",
"label_1269",
"label_1270",
"label_1271",
"label_1272",
"label_1273",
"label_1274",
"label_1275",
"label_1276",
"label_1277",
"label_1278",
"label_1279",
"label_1280",
"label_1281",
"label_1282",
"label_1283",
"label_1284",
"label_1285",
"label_1286",
"label_1287",
"label_1288",
"label_1289",
"label_1290",
"label_1291",
"label_1292",
"label_1293",
"label_1294",
"label_1295",
"label_1296",
"label_1297",
"label_1298",
"label_1299",
"label_1300",
"label_1301",
"label_1302",
"label_1303",
"label_1304",
"label_1305",
"label_1306",
"label_1307",
"label_1308",
"label_1309",
"label_1310",
"label_1311",
"label_1312",
"label_1313",
"label_1314",
"label_1315",
"label_1316",
"label_1317",
"label_1318",
"label_1319",
"label_1320",
"label_1321",
"label_1322",
"label_1323",
"label_1324",
"label_1325",
"label_1326",
"label_1327",
"label_1328",
"label_1329",
"label_1330",
"label_1331",
"label_1332",
"label_1333",
"label_1334",
"label_1335",
"label_1336",
"label_1337",
"label_1338",
"label_1339",
"label_1340",
"label_1341",
"label_1342",
"label_1343",
"label_1344",
"label_1345",
"label_1346",
"label_1347",
"label_1348",
"label_1349",
"label_1350",
"label_1351",
"label_1352",
"label_1353",
"label_1354",
"label_1355",
"label_1356",
"label_1357",
"label_1358",
"label_1359",
"label_1360",
"label_1361",
"label_1362",
"label_1363",
"label_1364",
"label_1365",
"label_1366",
"label_1367",
"label_1368",
"label_1369",
"label_1370",
"label_1371",
"label_1372",
"label_1373",
"label_1374",
"label_1375",
"label_1376",
"label_1377",
"label_1378",
"label_1379",
"label_1380",
"label_1381",
"label_1382",
"label_1383",
"label_1384",
"label_1385",
"label_1386",
"label_1387",
"label_1388",
"label_1389",
"label_1390",
"label_1391",
"label_1392",
"label_1393",
"label_1394",
"label_1395",
"label_1396",
"label_1397",
"label_1398",
"label_1399",
"label_1400",
"label_1401",
"label_1402",
"label_1403",
"label_1404",
"label_1405",
"label_1406",
"label_1407",
"label_1408",
"label_1409",
"label_1410",
"label_1411",
"label_1412",
"label_1413",
"label_1414",
"label_1415",
"label_1416",
"label_1417",
"label_1418",
"label_1419",
"label_1420",
"label_1421",
"label_1422",
"label_1423",
"label_1424",
"label_1425",
"label_1426",
"label_1427",
"label_1428",
"label_1429",
"label_1430",
"label_1431",
"label_1432",
"label_1433",
"label_1434",
"label_1435",
"label_1436",
"label_1437",
"label_1438",
"label_1439",
"label_1440",
"label_1441",
"label_1442",
"label_1443",
"label_1444",
"label_1445",
"label_1446",
"label_1447",
"label_1448",
"label_1449",
"label_1450",
"label_1451",
"label_1452",
"label_1453",
"label_1454",
"label_1455",
"label_1456",
"label_1457",
"label_1458",
"label_1459",
"label_1460",
"label_1461",
"label_1462",
"label_1463",
"label_1464",
"label_1465",
"label_1466",
"label_1467",
"label_1468",
"label_1469",
"label_1470",
"label_1471",
"label_1472",
"label_1473",
"label_1474",
"label_1475",
"label_1476",
"label_1477",
"label_1478",
"label_1479",
"label_1480",
"label_1481",
"label_1482",
"label_1483",
"label_1484",
"label_1485",
"label_1486",
"label_1487",
"label_1488",
"label_1489",
"label_1490",
"label_1491",
"label_1492",
"label_1493",
"label_1494",
"label_1495",
"label_1496",
"label_1497",
"label_1498",
"label_1499",
"label_1500",
"label_1501",
"label_1502",
"label_1503",
"label_1504",
"label_1505",
"label_1506",
"label_1507",
"label_1508",
"label_1509",
"label_1510",
"label_1511",
"label_1512",
"label_1513",
"label_1514",
"label_1515",
"label_1516",
"label_1517",
"label_1518",
"label_1519",
"label_1520",
"label_1521",
"label_1522",
"label_1523",
"label_1524",
"label_1525",
"label_1526",
"label_1527",
"label_1528",
"label_1529",
"label_1530",
"label_1531",
"label_1532",
"label_1533",
"label_1534",
"label_1535",
"label_1536",
"label_1537",
"label_1538",
"label_1539",
"label_1540",
"label_1541",
"label_1542",
"label_1543",
"label_1544",
"label_1545",
"label_1546",
"label_1547",
"label_1548",
"label_1549",
"label_1550",
"label_1551",
"label_1552",
"label_1553",
"label_1554",
"label_1555",
"label_1556",
"label_1557",
"label_1558",
"label_1559",
"label_1560",
"label_1561",
"label_1562",
"label_1563",
"label_1564",
"label_1565",
"label_1566",
"label_1567",
"label_1568",
"label_1569",
"label_1570",
"label_1571",
"label_1572",
"label_1573",
"label_1574",
"label_1575",
"label_1576",
"label_1577",
"label_1578",
"label_1579",
"label_1580",
"label_1581",
"label_1582",
"label_1583",
"label_1584",
"label_1585",
"label_1586",
"label_1587",
"label_1588",
"label_1589",
"label_1590",
"label_1591",
"label_1592",
"label_1593",
"label_1594",
"label_1595",
"label_1596",
"label_1597",
"label_1598",
"label_1599",
"label_1600",
"label_1601",
"label_1602",
"label_1603",
"label_1604",
"label_1605",
"label_1606",
"label_1607",
"label_1608",
"label_1609",
"label_1610",
"label_1611",
"label_1612",
"label_1613",
"label_1614",
"label_1615",
"label_1616",
"label_1617",
"label_1618",
"label_1619",
"label_1620",
"label_1621",
"label_1622",
"label_1623",
"label_1624",
"label_1625",
"label_1626",
"label_1627",
"label_1628",
"label_1629",
"label_1630",
"label_1631",
"label_1632",
"label_1633",
"label_1634",
"label_1635",
"label_1636",
"label_1637",
"label_1638",
"label_1639",
"label_1640",
"label_1641",
"label_1642",
"label_1643",
"label_1644",
"label_1645",
"label_1646",
"label_1647",
"label_1648",
"label_1649",
"label_1650",
"label_1651",
"label_1652",
"label_1653",
"label_1654",
"label_1655",
"label_1656",
"label_1657",
"label_1658",
"label_1659",
"label_1660",
"label_1661",
"label_1662",
"label_1663",
"label_1664",
"label_1665",
"label_1666",
"label_1667",
"label_1668",
"label_1669",
"label_1670",
"label_1671",
"label_1672",
"label_1673",
"label_1674",
"label_1675",
"label_1676",
"label_1677",
"label_1678",
"label_1679",
"label_1680",
"label_1681",
"label_1682",
"label_1683",
"label_1684",
"label_1685",
"label_1686",
"label_1687",
"label_1688",
"label_1689",
"label_1690",
"label_1691",
"label_1692",
"label_1693",
"label_1694",
"label_1695",
"label_1696",
"label_1697",
"label_1698",
"label_1699",
"label_1700",
"label_1701",
"label_1702",
"label_1703",
"label_1704",
"label_1705",
"label_1706",
"label_1707",
"label_1708",
"label_1709",
"label_1710",
"label_1711",
"label_1712",
"label_1713",
"label_1714",
"label_1715",
"label_1716",
"label_1717",
"label_1718",
"label_1719",
"label_1720",
"label_1721",
"label_1722",
"label_1723",
"label_1724",
"label_1725",
"label_1726",
"label_1727",
"label_1728",
"label_1729",
"label_1730",
"label_1731",
"label_1732",
"label_1733",
"label_1734",
"label_1735",
"label_1736",
"label_1737",
"label_1738",
"label_1739",
"label_1740",
"label_1741",
"label_1742",
"label_1743",
"label_1744",
"label_1745",
"label_1746",
"label_1747",
"label_1748",
"label_1749",
"label_1750",
"label_1751",
"label_1752",
"label_1753",
"label_1754",
"label_1755",
"label_1756",
"label_1757",
"label_1758",
"label_1759",
"label_1760",
"label_1761",
"label_1762",
"label_1763",
"label_1764",
"label_1765",
"label_1766",
"label_1767",
"label_1768",
"label_1769",
"label_1770",
"label_1771",
"label_1772",
"label_1773",
"label_1774",
"label_1775",
"label_1776",
"label_1777",
"label_1778",
"label_1779",
"label_1780",
"label_1781",
"label_1782",
"label_1783",
"label_1784",
"label_1785",
"label_1786",
"label_1787",
"label_1788",
"label_1789",
"label_1790",
"label_1791",
"label_1792",
"label_1793",
"label_1794",
"label_1795",
"label_1796",
"label_1797",
"label_1798",
"label_1799",
"label_1800",
"label_1801",
"label_1802",
"label_1803",
"label_1804",
"label_1805",
"label_1806",
"label_1807",
"label_1808",
"label_1809",
"label_1810",
"label_1811",
"label_1812",
"label_1813",
"label_1814",
"label_1815",
"label_1816",
"label_1817",
"label_1818",
"label_1819",
"label_1820",
"label_1821",
"label_1822",
"label_1823",
"label_1824",
"label_1825",
"label_1826",
"label_1827",
"label_1828",
"label_1829",
"label_1830",
"label_1831",
"label_1832",
"label_1833",
"label_1834",
"label_1835",
"label_1836",
"label_1837",
"label_1838",
"label_1839",
"label_1840",
"label_1841",
"label_1842",
"label_1843",
"label_1844",
"label_1845",
"label_1846",
"label_1847",
"label_1848",
"label_1849",
"label_1850",
"label_1851",
"label_1852",
"label_1853",
"label_1854",
"label_1855",
"label_1856",
"label_1857",
"label_1858",
"label_1859",
"label_1860",
"label_1861",
"label_1862",
"label_1863",
"label_1864",
"label_1865",
"label_1866",
"label_1867",
"label_1868",
"label_1869",
"label_1870",
"label_1871",
"label_1872",
"label_1873",
"label_1874",
"label_1875",
"label_1876",
"label_1877",
"label_1878",
"label_1879",
"label_1880",
"label_1881",
"label_1882",
"label_1883",
"label_1884",
"label_1885",
"label_1886",
"label_1887",
"label_1888",
"label_1889",
"label_1890",
"label_1891",
"label_1892",
"label_1893",
"label_1894",
"label_1895",
"label_1896",
"label_1897",
"label_1898",
"label_1899",
"label_1900",
"label_1901",
"label_1902",
"label_1903",
"label_1904",
"label_1905",
"label_1906",
"label_1907",
"label_1908",
"label_1909",
"label_1910",
"label_1911",
"label_1912",
"label_1913",
"label_1914",
"label_1915",
"label_1916",
"label_1917",
"label_1918",
"label_1919",
"label_1920",
"label_1921",
"label_1922",
"label_1923",
"label_1924",
"label_1925",
"label_1926",
"label_1927",
"label_1928",
"label_1929",
"label_1930",
"label_1931",
"label_1932",
"label_1933",
"label_1934",
"label_1935",
"label_1936",
"label_1937",
"label_1938",
"label_1939",
"label_1940",
"label_1941",
"label_1942",
"label_1943",
"label_1944",
"label_1945",
"label_1946",
"label_1947",
"label_1948",
"label_1949",
"label_1950",
"label_1951",
"label_1952",
"label_1953",
"label_1954",
"label_1955",
"label_1956",
"label_1957",
"label_1958",
"label_1959",
"label_1960",
"label_1961",
"label_1962",
"label_1963",
"label_1964",
"label_1965",
"label_1966",
"label_1967",
"label_1968",
"label_1969",
"label_1970",
"label_1971",
"label_1972",
"label_1973",
"label_1974",
"label_1975",
"label_1976",
"label_1977",
"label_1978",
"label_1979",
"label_1980",
"label_1981",
"label_1982",
"label_1983",
"label_1984",
"label_1985",
"label_1986",
"label_1987",
"label_1988",
"label_1989",
"label_1990",
"label_1991",
"label_1992",
"label_1993",
"label_1994",
"label_1995",
"label_1996",
"label_1997",
"label_1998",
"label_1999",
"label_2000",
"label_2001",
"label_2002",
"label_2003",
"label_2004",
"label_2005",
"label_2006",
"label_2007",
"label_2008",
"label_2009",
"label_2010",
"label_2011",
"label_2012",
"label_2013",
"label_2014",
"label_2015",
"label_2016",
"label_2017",
"label_2018",
"label_2019",
"label_2020",
"label_2021",
"label_2022",
"label_2023",
"label_2024",
"label_2025",
"label_2026",
"label_2027",
"label_2028",
"label_2029",
"label_2030",
"label_2031",
"label_2032",
"label_2033",
"label_2034",
"label_2035",
"label_2036",
"label_2037",
"label_2038",
"label_2039",
"label_2040",
"label_2041",
"label_2042",
"label_2043",
"label_2044",
"label_2045",
"label_2046",
"label_2047",
"label_2048",
"label_2049",
"label_2050",
"label_2051",
"label_2052",
"label_2053",
"label_2054",
"label_2055",
"label_2056",
"label_2057",
"label_2058",
"label_2059",
"label_2060",
"label_2061",
"label_2062",
"label_2063",
"label_2064",
"label_2065",
"label_2066",
"label_2067",
"label_2068",
"label_2069",
"label_2070",
"label_2071",
"label_2072",
"label_2073",
"label_2074",
"label_2075",
"label_2076",
"label_2077",
"label_2078",
"label_2079",
"label_2080",
"label_2081",
"label_2082",
"label_2083",
"label_2084",
"label_2085",
"label_2086",
"label_2087",
"label_2088",
"label_2089",
"label_2090",
"label_2091",
"label_2092",
"label_2093",
"label_2094",
"label_2095",
"label_2096",
"label_2097",
"label_2098",
"label_2099",
"label_2100",
"label_2101",
"label_2102",
"label_2103",
"label_2104",
"label_2105",
"label_2106",
"label_2107",
"label_2108",
"label_2109",
"label_2110",
"label_2111"
] |
tectonatech/swin-tiny-patch4-window7-224-fine_tune
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-fine_tune
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5958
- Accuracy: 0.8782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.175 | 0.96 | 16 | 4.7967 | 0.1345 |
| 4.1158 | 1.97 | 33 | 2.9977 | 0.3824 |
| 2.0676 | 2.99 | 50 | 1.5415 | 0.6807 |
| 1.4395 | 4.0 | 67 | 0.9951 | 0.8151 |
| 0.9396 | 4.96 | 83 | 0.8235 | 0.8277 |
| 0.7456 | 5.97 | 100 | 0.7195 | 0.8361 |
| 0.666 | 6.99 | 117 | 0.6406 | 0.8613 |
| 0.5893 | 8.0 | 134 | 0.6045 | 0.8739 |
| 0.4704 | 8.96 | 150 | 0.6016 | 0.8655 |
| 0.4475 | 9.97 | 167 | 0.5958 | 0.8782 |
| 0.3937 | 10.99 | 184 | 0.5856 | 0.8782 |
| 0.3327 | 12.0 | 201 | 0.5761 | 0.8782 |
| 0.3277 | 12.96 | 217 | 0.5758 | 0.8782 |
| 0.2928 | 13.97 | 234 | 0.5754 | 0.8739 |
| 0.2545 | 14.99 | 251 | 0.5711 | 0.8739 |
| 0.2657 | 16.0 | 268 | 0.5851 | 0.8739 |
| 0.2457 | 16.96 | 284 | 0.5805 | 0.8655 |
| 0.2359 | 17.97 | 301 | 0.5762 | 0.8697 |
| 0.2849 | 18.99 | 318 | 0.5792 | 0.8739 |
| 0.223 | 19.1 | 320 | 0.5792 | 0.8739 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"1",
"100",
"101",
"102",
"103",
"104",
"105",
"106",
"107",
"109",
"11",
"111",
"112",
"113",
"114",
"116",
"117",
"118",
"119",
"120",
"121",
"122",
"123",
"124",
"126",
"127",
"128",
"129",
"130",
"131",
"132",
"133",
"134",
"135",
"136",
"137",
"138",
"139",
"14",
"140",
"141",
"142",
"143",
"144",
"145",
"146",
"147",
"149",
"150",
"151",
"152",
"153",
"154",
"155",
"156",
"157",
"158",
"159",
"160",
"161",
"162",
"163",
"164",
"165",
"166",
"167",
"168",
"169",
"17",
"170",
"171",
"172",
"173",
"174",
"175",
"176",
"177",
"178",
"179",
"180",
"181",
"182",
"183",
"184",
"185",
"186",
"187",
"188",
"189",
"190",
"191",
"192",
"193",
"194",
"195",
"196",
"197",
"198",
"199",
"20",
"201",
"202",
"203",
"204",
"205",
"206",
"207",
"208",
"209",
"211",
"212",
"213",
"214",
"215",
"216",
"217",
"4",
"7",
"81",
"82",
"83",
"84",
"85",
"86",
"87",
"88",
"89",
"90",
"91",
"93",
"94",
"95",
"96",
"97",
"98",
"99",
"acura",
"amkette",
"apple",
"baccarat",
"baidu",
"batman",
"dell",
"domino's pizza",
"epson",
"hewlett-packard",
"ibm",
"john deere",
"kfc",
"kodak",
"lamborghini",
"lenovo",
"mazda",
"mcdonald's",
"mercedes-benz",
"nestle",
"nintendo",
"nissan",
"ovaltine",
"pepsi max",
"pokemon",
"red bull",
"star wars",
"the pizza company",
"tim hortons",
"b",
"d",
"f",
"h",
"hp",
"huawei",
"pepsi",
"subaru"
] |
erwinsyahh/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4041
- Accuracy: 0.6062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.8541 | 0.325 |
| No log | 2.0 | 20 | 1.6601 | 0.4062 |
| No log | 3.0 | 30 | 1.5194 | 0.525 |
| No log | 4.0 | 40 | 1.4041 | 0.6062 |
| No log | 5.0 | 50 | 1.3033 | 0.5813 |
| No log | 6.0 | 60 | 1.2836 | 0.5687 |
| No log | 7.0 | 70 | 1.2508 | 0.575 |
| No log | 8.0 | 80 | 1.2026 | 0.5938 |
| No log | 9.0 | 90 | 1.2077 | 0.5875 |
| No log | 10.0 | 100 | 1.1930 | 0.575 |
| No log | 11.0 | 110 | 1.2111 | 0.5687 |
| No log | 12.0 | 120 | 1.1794 | 0.5875 |
| No log | 13.0 | 130 | 1.2007 | 0.5938 |
| No log | 14.0 | 140 | 1.1854 | 0.5875 |
| No log | 15.0 | 150 | 1.1905 | 0.5875 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Shresthadev403/food-image-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# food-image-classification
This model was trained from scratch on the food101 dataset
It achieves the following results on the evaluation set:
- eval_loss: 0.4645
- eval_accuracy: 0.8831
- eval_runtime: 156.6057
- eval_samples_per_second: 96.74
- eval_steps_per_second: 6.047
- epoch: 54.91
- step: 52000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 500
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
parvpareek/swin-base-patch4-window7-224-in22k-finetuned-ham
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-base-patch4-window7-224-in22k-finetuned-ham
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3549
- Accuracy: 0.8743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.533 | 0.99 | 70 | 0.5287 | 0.8144 |
| 0.4244 | 2.0 | 141 | 0.3899 | 0.8643 |
| 0.3401 | 2.98 | 210 | 0.3549 | 0.8743 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"mel",
"nv",
"bcc",
"akiec",
"bkl",
"df",
"vasc"
] |
Dricz/emotion_recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5074
- Accuracy: 0.5125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.3274 | 0.5687 |
| No log | 2.0 | 80 | 1.4828 | 0.5188 |
| No log | 3.0 | 120 | 1.2860 | 0.5875 |
| No log | 4.0 | 160 | 1.3801 | 0.5375 |
| No log | 5.0 | 200 | 1.3808 | 0.55 |
| No log | 6.0 | 240 | 1.4464 | 0.525 |
| No log | 7.0 | 280 | 1.5266 | 0.5188 |
| No log | 8.0 | 320 | 1.4280 | 0.5188 |
| No log | 9.0 | 360 | 1.3953 | 0.5687 |
| No log | 10.0 | 400 | 1.4902 | 0.5312 |
| No log | 11.0 | 440 | 1.3965 | 0.5625 |
| No log | 12.0 | 480 | 1.4328 | 0.55 |
| 0.1776 | 13.0 | 520 | 1.5172 | 0.5188 |
| 0.1776 | 14.0 | 560 | 1.6457 | 0.5062 |
| 0.1776 | 15.0 | 600 | 1.4402 | 0.5375 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
SoulPerforms/visual_emotion_classification_vit_base_finetunned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# visual_emotion_classification_vit_base_finetunned
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2429
- Accuracy: 0.5188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.026 | 1.25 | 100 | 2.0071 | 0.275 |
| 1.8882 | 2.5 | 200 | 1.8921 | 0.3625 |
| 1.7186 | 3.75 | 300 | 1.7326 | 0.4188 |
| 1.5892 | 5.0 | 400 | 1.6242 | 0.475 |
| 1.4942 | 6.25 | 500 | 1.5443 | 0.5125 |
| 1.3825 | 7.5 | 600 | 1.4763 | 0.5062 |
| 1.3084 | 8.75 | 700 | 1.4554 | 0.4938 |
| 1.2388 | 10.0 | 800 | 1.4057 | 0.525 |
| 1.1519 | 11.25 | 900 | 1.3756 | 0.4938 |
| 1.1054 | 12.5 | 1000 | 1.3604 | 0.4875 |
| 1.0605 | 13.75 | 1100 | 1.3597 | 0.4938 |
| 1.016 | 15.0 | 1200 | 1.3370 | 0.4938 |
| 0.9601 | 16.25 | 1300 | 1.2981 | 0.4938 |
| 0.8445 | 17.5 | 1400 | 1.2420 | 0.5563 |
| 0.8514 | 18.75 | 1500 | 1.2485 | 0.5625 |
| 0.7899 | 20.0 | 1600 | 1.2861 | 0.4875 |
| 0.7459 | 21.25 | 1700 | 1.2860 | 0.4875 |
| 0.6917 | 22.5 | 1800 | 1.2335 | 0.5813 |
| 0.6864 | 23.75 | 1900 | 1.2726 | 0.5437 |
| 0.6414 | 25.0 | 2000 | 1.2215 | 0.5375 |
| 0.5583 | 26.25 | 2100 | 1.2756 | 0.5312 |
| 0.597 | 27.5 | 2200 | 1.2314 | 0.5375 |
| 0.5654 | 28.75 | 2300 | 1.3791 | 0.5125 |
| 0.5798 | 30.0 | 2400 | 1.1890 | 0.5687 |
| 0.5247 | 31.25 | 2500 | 1.2440 | 0.5687 |
| 0.5099 | 32.5 | 2600 | 1.2787 | 0.5625 |
| 0.496 | 33.75 | 2700 | 1.2628 | 0.55 |
| 0.479 | 35.0 | 2800 | 1.3420 | 0.4875 |
| 0.4685 | 36.25 | 2900 | 1.2817 | 0.5563 |
| 0.4375 | 37.5 | 3000 | 1.3122 | 0.525 |
| 0.4314 | 38.75 | 3100 | 1.1791 | 0.5563 |
| 0.4174 | 40.0 | 3200 | 1.2322 | 0.55 |
| 0.4019 | 41.25 | 3300 | 1.3871 | 0.5125 |
| 0.3738 | 42.5 | 3400 | 1.2854 | 0.5312 |
| 0.3938 | 43.75 | 3500 | 1.3057 | 0.5375 |
| 0.369 | 45.0 | 3600 | 1.2792 | 0.5437 |
| 0.3768 | 46.25 | 3700 | 1.2761 | 0.5625 |
| 0.3202 | 47.5 | 3800 | 1.2704 | 0.5375 |
| 0.3859 | 48.75 | 3900 | 1.2746 | 0.5312 |
| 0.3689 | 50.0 | 4000 | 1.3306 | 0.5563 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
parvpareek/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4876
- Accuracy: 0.8234
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6704 | 0.99 | 70 | 0.7405 | 0.7166 |
| 0.5176 | 2.0 | 141 | 0.5234 | 0.8084 |
| 0.4531 | 2.98 | 210 | 0.4876 | 0.8234 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"mel",
"nv",
"bcc",
"akiec",
"bkl",
"df",
"vasc"
] |
MichalGas/swin-tiny-patch4-window7-224-finetuned-mgasior-2024
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-mgasior-2024
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6359
- Accuracy: 0.3228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 9 | 1.6990 | 0.2283 |
| 1.7601 | 2.0 | 18 | 5.0280 | 0.1654 |
| 1.774 | 3.0 | 27 | 1.6553 | 0.3228 |
| 1.7759 | 4.0 | 36 | 1.6896 | 0.3228 |
| 1.6705 | 5.0 | 45 | 1.6497 | 0.3228 |
| 1.7113 | 6.0 | 54 | 1.6426 | 0.3228 |
| 1.6718 | 7.0 | 63 | 1.6391 | 0.3228 |
| 1.6606 | 8.0 | 72 | 1.6359 | 0.3228 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
[
"bipolars",
"clippers",
"graspers",
"hooks",
"irrigators",
"scissorss"
] |
hafizurUMaine/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hafizurUMaine/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3636
- Validation Loss: 0.3247
- Train Accuracy: 0.919
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7566 | 1.5986 | 0.831 | 0 |
| 1.1979 | 0.7920 | 0.901 | 1 |
| 0.6892 | 0.5138 | 0.902 | 2 |
| 0.4709 | 0.4103 | 0.902 | 3 |
| 0.3636 | 0.3247 | 0.919 | 4 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
IsaacMwesigwa/autotrain-501av-or02z
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.7705780863761902
f1_macro: 0.6666666666666666
f1_micro: 0.6666666666666666
f1_weighted: 0.6666666666666666
precision_macro: 0.7598039215686274
precision_micro: 0.6666666666666666
precision_weighted: 0.7598039215686274
recall_macro: 0.6666666666666666
recall_micro: 0.6666666666666666
recall_weighted: 0.6666666666666666
accuracy: 0.6666666666666666
|
[
"aaron long",
"aaron mooy",
"aaron ramsdale"
] |
huyentls1114/swin-tiny-patch4-window7-224-finetuned-swin-tiny
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-swin-tiny
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5222
- Accuracy: 0.5559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5958 | 0.96 | 20 | 3.5209 | 0.0937 |
| 3.2466 | 1.98 | 41 | 2.9994 | 0.2387 |
| 2.4246 | 2.99 | 62 | 2.0341 | 0.4169 |
| 1.8599 | 4.0 | 83 | 1.6747 | 0.4955 |
| 1.531 | 4.96 | 103 | 1.5218 | 0.4773 |
| 1.3292 | 5.98 | 124 | 1.3834 | 0.5317 |
| 1.2063 | 6.99 | 145 | 1.3381 | 0.5468 |
| 1.0806 | 8.0 | 166 | 1.2748 | 0.5710 |
| 0.9638 | 8.96 | 186 | 1.3062 | 0.5559 |
| 0.8441 | 9.98 | 207 | 1.3322 | 0.5498 |
| 0.7868 | 10.99 | 228 | 1.2873 | 0.5710 |
| 0.7485 | 12.0 | 249 | 1.2012 | 0.5619 |
| 0.6522 | 12.96 | 269 | 1.2264 | 0.5861 |
| 0.6362 | 13.98 | 290 | 1.2796 | 0.5589 |
| 0.6214 | 14.99 | 311 | 1.3406 | 0.5529 |
| 0.5793 | 16.0 | 332 | 1.2479 | 0.5740 |
| 0.5187 | 16.96 | 352 | 1.3203 | 0.5891 |
| 0.4965 | 17.98 | 373 | 1.3429 | 0.5619 |
| 0.4809 | 18.99 | 394 | 1.3453 | 0.5831 |
| 0.4243 | 20.0 | 415 | 1.3759 | 0.5498 |
| 0.4447 | 20.96 | 435 | 1.4275 | 0.5196 |
| 0.3839 | 21.98 | 456 | 1.4660 | 0.5589 |
| 0.414 | 22.99 | 477 | 1.4465 | 0.5408 |
| 0.3741 | 24.0 | 498 | 1.3944 | 0.5650 |
| 0.3802 | 24.96 | 518 | 1.4272 | 0.5650 |
| 0.3733 | 25.98 | 539 | 1.3341 | 0.5589 |
| 0.3558 | 26.99 | 560 | 1.3864 | 0.5589 |
| 0.3448 | 28.0 | 581 | 1.4027 | 0.5589 |
| 0.3373 | 28.96 | 601 | 1.4452 | 0.5589 |
| 0.311 | 29.98 | 622 | 1.4021 | 0.5740 |
| 0.3218 | 30.99 | 643 | 1.4015 | 0.5680 |
| 0.3082 | 32.0 | 664 | 1.4159 | 0.5619 |
| 0.3173 | 32.96 | 684 | 1.4290 | 0.5498 |
| 0.2551 | 33.98 | 705 | 1.4268 | 0.5619 |
| 0.2739 | 34.99 | 726 | 1.4546 | 0.5559 |
| 0.2533 | 36.0 | 747 | 1.4398 | 0.5498 |
| 0.2578 | 36.96 | 767 | 1.4487 | 0.5438 |
| 0.2472 | 37.98 | 788 | 1.4438 | 0.5559 |
| 0.281 | 38.99 | 809 | 1.4916 | 0.5529 |
| 0.2757 | 40.0 | 830 | 1.4758 | 0.5619 |
| 0.2679 | 40.96 | 850 | 1.5104 | 0.5559 |
| 0.2548 | 41.98 | 871 | 1.5024 | 0.5529 |
| 0.2357 | 42.99 | 892 | 1.5286 | 0.5468 |
| 0.2357 | 44.0 | 913 | 1.5150 | 0.5529 |
| 0.2287 | 44.96 | 933 | 1.5234 | 0.5589 |
| 0.2329 | 45.98 | 954 | 1.5334 | 0.5650 |
| 0.2131 | 46.99 | 975 | 1.5296 | 0.5619 |
| 0.2269 | 48.0 | 996 | 1.5221 | 0.5559 |
| 0.2161 | 48.19 | 1000 | 1.5222 | 0.5559 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"attic_door",
"bi-fold_door",
"bi-fold_door_double",
"bi-fold_glass_door",
"bi-fold_glass_door_double",
"casement_diamond_window",
"casement_round_window",
"casement_window",
"concern",
"door_opening",
"door_opening_arch",
"double_hung_window",
"elevator_door",
"exterior_door",
"exterior_door_double",
"exterior_glass_door",
"exterior_glass_door_double",
"fixed_arch_window",
"fixed_diamond_window",
"fixed_round_window",
"fixed_sloping_left_window",
"fixed_sloping_right_window",
"fixed_windows",
"french_door",
"french_glass_door",
"garage_door",
"garage_door_double",
"interior_door",
"interior_glass_door",
"interior_glass_door_double",
"patio_door",
"pocket_door",
"pocket_door_double",
"sauna_door",
"sauna_glass_door",
"sliding_door_double",
"sliding_window"
] |
huyentls1114/mobilevitv2-1.0-imagenet1k-256-finetuned-swin-tiny
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilevitv2-1.0-imagenet1k-256-finetuned-swin-tiny
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3595
- Accuracy: 0.5468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6149 | 0.96 | 20 | 3.6094 | 0.0363 |
| 3.601 | 1.98 | 41 | 3.5936 | 0.0544 |
| 3.5892 | 2.99 | 62 | 3.5643 | 0.1057 |
| 3.5556 | 4.0 | 83 | 3.5195 | 0.1752 |
| 3.505 | 4.96 | 103 | 3.4422 | 0.2870 |
| 3.4072 | 5.98 | 124 | 3.2947 | 0.3172 |
| 3.2477 | 6.99 | 145 | 3.0629 | 0.3233 |
| 3.0508 | 8.0 | 166 | 2.8124 | 0.3444 |
| 2.8381 | 8.96 | 186 | 2.6019 | 0.3867 |
| 2.6407 | 9.98 | 207 | 2.4012 | 0.4018 |
| 2.5312 | 10.99 | 228 | 2.2300 | 0.4441 |
| 2.3687 | 12.0 | 249 | 2.0957 | 0.4411 |
| 2.2963 | 12.96 | 269 | 1.9972 | 0.4653 |
| 2.1898 | 13.98 | 290 | 1.9019 | 0.4743 |
| 2.0632 | 14.99 | 311 | 1.8381 | 0.4834 |
| 2.0279 | 16.0 | 332 | 1.7724 | 0.4955 |
| 1.998 | 16.96 | 352 | 1.7243 | 0.5015 |
| 1.9156 | 17.98 | 373 | 1.6919 | 0.5015 |
| 1.8914 | 18.99 | 394 | 1.6483 | 0.4985 |
| 1.8466 | 20.0 | 415 | 1.6211 | 0.5045 |
| 1.853 | 20.96 | 435 | 1.5899 | 0.5166 |
| 1.8124 | 21.98 | 456 | 1.5613 | 0.5015 |
| 1.7247 | 22.99 | 477 | 1.5355 | 0.5227 |
| 1.7034 | 24.0 | 498 | 1.5121 | 0.5287 |
| 1.6678 | 24.96 | 518 | 1.5000 | 0.5317 |
| 1.6832 | 25.98 | 539 | 1.4876 | 0.5287 |
| 1.6727 | 26.99 | 560 | 1.4796 | 0.5287 |
| 1.5744 | 28.0 | 581 | 1.4712 | 0.5227 |
| 1.5842 | 28.96 | 601 | 1.4492 | 0.5166 |
| 1.5416 | 29.98 | 622 | 1.4345 | 0.5347 |
| 1.5757 | 30.99 | 643 | 1.4229 | 0.5257 |
| 1.5574 | 32.0 | 664 | 1.4138 | 0.5378 |
| 1.5665 | 32.96 | 684 | 1.4077 | 0.5438 |
| 1.4837 | 33.98 | 705 | 1.3861 | 0.5438 |
| 1.5114 | 34.99 | 726 | 1.3956 | 0.5529 |
| 1.5207 | 36.0 | 747 | 1.3883 | 0.5468 |
| 1.4879 | 36.96 | 767 | 1.3750 | 0.5378 |
| 1.4547 | 37.98 | 788 | 1.3817 | 0.5408 |
| 1.4668 | 38.99 | 809 | 1.3643 | 0.5529 |
| 1.457 | 40.0 | 830 | 1.3669 | 0.5408 |
| 1.4604 | 40.96 | 850 | 1.3653 | 0.5498 |
| 1.4556 | 41.98 | 871 | 1.3621 | 0.5438 |
| 1.4852 | 42.99 | 892 | 1.3549 | 0.5498 |
| 1.4198 | 44.0 | 913 | 1.3461 | 0.5498 |
| 1.3824 | 44.96 | 933 | 1.3495 | 0.5498 |
| 1.4035 | 45.98 | 954 | 1.3495 | 0.5589 |
| 1.4586 | 46.99 | 975 | 1.3476 | 0.5529 |
| 1.4265 | 48.0 | 996 | 1.3481 | 0.5498 |
| 1.4563 | 48.19 | 1000 | 1.3595 | 0.5468 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"attic_door",
"bi-fold_door",
"bi-fold_door_double",
"bi-fold_glass_door",
"bi-fold_glass_door_double",
"casement_diamond_window",
"casement_round_window",
"casement_window",
"concern",
"door_opening",
"door_opening_arch",
"double_hung_window",
"elevator_door",
"exterior_door",
"exterior_door_double",
"exterior_glass_door",
"exterior_glass_door_double",
"fixed_arch_window",
"fixed_diamond_window",
"fixed_round_window",
"fixed_sloping_left_window",
"fixed_sloping_right_window",
"fixed_windows",
"french_door",
"french_glass_door",
"garage_door",
"garage_door_double",
"interior_door",
"interior_glass_door",
"interior_glass_door_double",
"patio_door",
"pocket_door",
"pocket_door_double",
"sauna_door",
"sauna_glass_door",
"sliding_door_double",
"sliding_window"
] |
TatersMcgee/autotrain-cjwsi-5jxj6
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.014193599112331867
f1: 0.9965141612200434
precision: 0.9986899563318777
recall: 0.9943478260869565
auc: 0.9999258034026465
accuracy: 0.9965217391304347
|
[
"cat",
"dog"
] |
scastrotorres/platzi-vit-model-sebastian
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-vit-model-sebastian
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0253
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0483 | 3.85 | 500 | 0.0253 | 0.9925 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
cva333/autotrain-6rqop-se1yi
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.6310061812400818
f1: 0.5
precision: 1.0
recall: 0.3333333333333333
auc: 0.75
accuracy: 0.7142857142857143
|
[
"ng",
"ok"
] |
lrzjason/hand-classifier
|
a hand classifier detect the image is showing left hand or right hand
it is trained with hagrid dataset on vit model, google/vit-base-patch16-224-in21k.
Limitation:
it couldn't predict both hand and no hand images.
```
inputs = image_processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_label = logits.argmax(-1).item()
print(f'predicted_label: {model.config.id2label[predicted_label]}')
```
|
[
"left_hand",
"right_hand"
] |
jtalbot832/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
carljohnson40/autotrain-qfdfq-d86gh
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.077054463326931
f1: 0.9904761904761905
precision: 0.9811320754716981
recall: 1.0
auc: 0.9927884615384616
accuracy: 0.9907407407407407
|
[
"post",
"pre"
] |
carljohnson40/autotrain-v81fq-o8647
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.1967389583587646
f1: 0.45454545454545453
precision: 0.29411764705882354
recall: 1.0
auc: 0.9850467289719625
accuracy: 0.4744525547445255
|
[
"after",
"before"
] |
hafizurUMaine/cifar10_m
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hafizurUMaine/cifar10_m
This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0748
- Train Accuracy: 0.9743
- Validation Loss: 0.6597
- Validation Accuracy: 0.8575
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 400000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.5748 | 0.1482 | 3.1655 | 0.4160 | 0 |
| 2.4468 | 0.5135 | 1.7772 | 0.6195 | 1 |
| 1.5927 | 0.6389 | 1.3152 | 0.6770 | 2 |
| 1.2333 | 0.7001 | 1.1226 | 0.7265 | 3 |
| 1.0094 | 0.7334 | 0.9668 | 0.7490 | 4 |
| 0.8748 | 0.7591 | 0.9140 | 0.7510 | 5 |
| 0.7714 | 0.7846 | 0.7881 | 0.7845 | 6 |
| 0.6977 | 0.7999 | 0.8075 | 0.7745 | 7 |
| 0.6524 | 0.8096 | 0.8417 | 0.7675 | 8 |
| 0.5904 | 0.8254 | 0.7763 | 0.7850 | 9 |
| 0.5525 | 0.8321 | 0.7367 | 0.7955 | 10 |
| 0.5083 | 0.8459 | 0.7343 | 0.7990 | 11 |
| 0.4695 | 0.8559 | 0.6768 | 0.8075 | 12 |
| 0.4432 | 0.8615 | 0.6830 | 0.8095 | 13 |
| 0.4125 | 0.8704 | 0.6891 | 0.7980 | 14 |
| 0.3995 | 0.875 | 0.6482 | 0.8155 | 15 |
| 0.3723 | 0.8781 | 0.6653 | 0.8095 | 16 |
| 0.3505 | 0.8859 | 0.6268 | 0.8195 | 17 |
| 0.3390 | 0.8906 | 0.6243 | 0.8205 | 18 |
| 0.3132 | 0.8967 | 0.6338 | 0.8255 | 19 |
| 0.2879 | 0.9071 | 0.5879 | 0.8380 | 20 |
| 0.2845 | 0.9066 | 0.6004 | 0.8320 | 21 |
| 0.2578 | 0.9141 | 0.6228 | 0.8320 | 22 |
| 0.2521 | 0.9178 | 0.6208 | 0.8295 | 23 |
| 0.2375 | 0.9258 | 0.6051 | 0.8410 | 24 |
| 0.2226 | 0.9243 | 0.6138 | 0.8395 | 25 |
| 0.2139 | 0.9298 | 0.5651 | 0.8455 | 26 |
| 0.2094 | 0.9302 | 0.5881 | 0.8470 | 27 |
| 0.1925 | 0.9385 | 0.6298 | 0.8390 | 28 |
| 0.1806 | 0.9399 | 0.5982 | 0.8450 | 29 |
| 0.1758 | 0.9401 | 0.6139 | 0.8435 | 30 |
| 0.1630 | 0.9449 | 0.6105 | 0.8430 | 31 |
| 0.1566 | 0.9449 | 0.5953 | 0.8490 | 32 |
| 0.1423 | 0.9531 | 0.6246 | 0.8440 | 33 |
| 0.1378 | 0.9545 | 0.6249 | 0.8500 | 34 |
| 0.1379 | 0.9553 | 0.6625 | 0.8415 | 35 |
| 0.1305 | 0.9551 | 0.6035 | 0.8575 | 36 |
| 0.1253 | 0.9581 | 0.6503 | 0.8490 | 37 |
| 0.1149 | 0.9607 | 0.5882 | 0.8585 | 38 |
| 0.1026 | 0.9672 | 0.6130 | 0.8530 | 39 |
| 0.1019 | 0.9660 | 0.6373 | 0.8525 | 40 |
| 0.1038 | 0.9645 | 0.6197 | 0.8570 | 41 |
| 0.0938 | 0.9685 | 0.6239 | 0.8545 | 42 |
| 0.0910 | 0.9688 | 0.6439 | 0.8590 | 43 |
| 0.0869 | 0.9711 | 0.5812 | 0.8640 | 44 |
| 0.0818 | 0.9726 | 0.6692 | 0.8565 | 45 |
| 0.0695 | 0.9799 | 0.6652 | 0.8585 | 46 |
| 0.0756 | 0.9765 | 0.6584 | 0.8570 | 47 |
| 0.0669 | 0.9797 | 0.6542 | 0.8610 | 48 |
| 0.0748 | 0.9743 | 0.6597 | 0.8575 | 49 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"tench, tinca tinca",
"goldfish, carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, carcharodon carcharias",
"tiger shark, galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, struthio camelus",
"brambling, fringilla montifringilla",
"goldfinch, carduelis carduelis",
"house finch, linnet, carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, passerina cyanea",
"robin, american robin, turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, american eagle, haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, strix nebulosa",
"european fire salamander, salamandra salamandra",
"common newt, triturus vulgaris",
"eft",
"spotted salamander, ambystoma maculatum",
"axolotl, mud puppy, ambystoma mexicanum",
"bullfrog, rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, ascaphus trui",
"loggerhead, loggerhead turtle, caretta caretta",
"leatherback turtle, leatherback, leathery turtle, dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, iguana iguana",
"american chameleon, anole, anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, chlamydosaurus kingi",
"alligator lizard",
"gila monster, heloderma suspectum",
"green lizard, lacerta viridis",
"african chameleon, chamaeleo chamaeleon",
"komodo dragon, komodo lizard, dragon lizard, giant lizard, varanus komodoensis",
"african crocodile, nile crocodile, crocodylus niloticus",
"american alligator, alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, hypsiglena torquata",
"boa constrictor, constrictor constrictor",
"rock python, rock snake, python sebae",
"indian cobra, naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, cerastes cornutus",
"diamondback, diamondback rattlesnake, crotalus adamanteus",
"sidewinder, horned rattlesnake, crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, phalangium opilio",
"scorpion",
"black and gold garden spider, argiope aurantia",
"barn spider, araneus cavaticus",
"garden spider, aranea diademata",
"black widow, latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"african grey, african gray, psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, kakatoe galerita, cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, mergus serrator",
"goose",
"black swan, cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"dungeness crab, cancer magister",
"rock crab, cancer irroratus",
"fiddler crab",
"king crab, alaska crab, alaskan king crab, alaska king crab, paralithodes camtschatica",
"american lobster, northern lobster, maine lobster, homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, ciconia ciconia",
"black stork, ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, egretta caerulea",
"american egret, great white heron, egretta albus",
"bittern",
"crane",
"limpkin, aramus pictus",
"european gallinule, porphyrio porphyrio",
"american coot, marsh hen, mud hen, water hen, fulica americana",
"bustard",
"ruddy turnstone, arenaria interpres",
"red-backed sandpiper, dunlin, erolia alpina",
"redshank, tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, eschrichtius gibbosus, eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, orcinus orca",
"dugong, dugong dugon",
"sea lion",
"chihuahua",
"japanese spaniel",
"maltese dog, maltese terrier, maltese",
"pekinese, pekingese, peke",
"shih-tzu",
"blenheim spaniel",
"papillon",
"toy terrier",
"rhodesian ridgeback",
"afghan hound, afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"walker hound, walker foxhound",
"english foxhound",
"redbone",
"borzoi, russian wolfhound",
"irish wolfhound",
"italian greyhound",
"whippet",
"ibizan hound, ibizan podenco",
"norwegian elkhound, elkhound",
"otterhound, otter hound",
"saluki, gazelle hound",
"scottish deerhound, deerhound",
"weimaraner",
"staffordshire bullterrier, staffordshire bull terrier",
"american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier",
"bedlington terrier",
"border terrier",
"kerry blue terrier",
"irish terrier",
"norfolk terrier",
"norwich terrier",
"yorkshire terrier",
"wire-haired fox terrier",
"lakeland terrier",
"sealyham terrier, sealyham",
"airedale, airedale terrier",
"cairn, cairn terrier",
"australian terrier",
"dandie dinmont, dandie dinmont terrier",
"boston bull, boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"scotch terrier, scottish terrier, scottie",
"tibetan terrier, chrysanthemum dog",
"silky terrier, sydney silky",
"soft-coated wheaten terrier",
"west highland white terrier",
"lhasa, lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"labrador retriever",
"chesapeake bay retriever",
"german short-haired pointer",
"vizsla, hungarian pointer",
"english setter",
"irish setter, red setter",
"gordon setter",
"brittany spaniel",
"clumber, clumber spaniel",
"english springer, english springer spaniel",
"welsh springer spaniel",
"cocker spaniel, english cocker spaniel, cocker",
"sussex spaniel",
"irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"old english sheepdog, bobtail",
"shetland sheepdog, shetland sheep dog, shetland",
"collie",
"border collie",
"bouvier des flandres, bouviers des flandres",
"rottweiler",
"german shepherd, german shepherd dog, german police dog, alsatian",
"doberman, doberman pinscher",
"miniature pinscher",
"greater swiss mountain dog",
"bernese mountain dog",
"appenzeller",
"entlebucher",
"boxer",
"bull mastiff",
"tibetan mastiff",
"french bulldog",
"great dane",
"saint bernard, st bernard",
"eskimo dog, husky",
"malamute, malemute, alaskan malamute",
"siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"leonberg",
"newfoundland, newfoundland dog",
"great pyrenees",
"samoyed, samoyede",
"pomeranian",
"chow, chow chow",
"keeshond",
"brabancon griffon",
"pembroke, pembroke welsh corgi",
"cardigan, cardigan welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"mexican hairless",
"timber wolf, grey wolf, gray wolf, canis lupus",
"white wolf, arctic wolf, canis lupus tundrarum",
"red wolf, maned wolf, canis rufus, canis niger",
"coyote, prairie wolf, brush wolf, canis latrans",
"dingo, warrigal, warragal, canis dingo",
"dhole, cuon alpinus",
"african hunting dog, hyena dog, cape hunting dog, lycaon pictus",
"hyena, hyaena",
"red fox, vulpes vulpes",
"kit fox, vulpes macrotis",
"arctic fox, white fox, alopex lagopus",
"grey fox, gray fox, urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"persian cat",
"siamese cat, siamese",
"egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, felis concolor",
"lynx, catamount",
"leopard, panthera pardus",
"snow leopard, ounce, panthera uncia",
"jaguar, panther, panthera onca, felis onca",
"lion, king of beasts, panthera leo",
"tiger, panthera tigris",
"cheetah, chetah, acinonyx jubatus",
"brown bear, bruin, ursus arctos",
"american black bear, black bear, ursus americanus, euarctos americanus",
"ice bear, polar bear, ursus maritimus, thalarctos maritimus",
"sloth bear, melursus ursinus, ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"angora, angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, sciurus niger",
"marmot",
"beaver",
"guinea pig, cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, sus scrofa",
"wild boar, boar, sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, hippopotamus amphibius",
"ox",
"water buffalo, water ox, asiatic buffalo, bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, rocky mountain bighorn, rocky mountain sheep, ovis canadensis",
"ibex, capra ibex",
"hartebeest",
"impala, aepyceros melampus",
"gazelle",
"arabian camel, dromedary, camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, mustela putorius",
"black-footed ferret, ferret, mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, bradypus tridactylus",
"orangutan, orang, orangutang, pongo pygmaeus",
"gorilla, gorilla gorilla",
"chimpanzee, chimp, pan troglodytes",
"gibbon, hylobates lar",
"siamang, hylobates syndactylus, symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, nasalis larvatus",
"marmoset",
"capuchin, ringtail, cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, ateles geoffroyi",
"squirrel monkey, saimiri sciureus",
"madagascar cat, ring-tailed lemur, lemur catta",
"indri, indris, indri indri, indri brevicaudatus",
"indian elephant, elephas maximus",
"african elephant, loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, ailurus fulgens",
"giant panda, panda, panda bear, coon bear, ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, oncorhynchus kisutch",
"rock beauty, holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, biro",
"band aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, atm",
"cassette",
"cassette player",
"castle",
"catamaran",
"cd player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"crock pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"french horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"ipod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, t-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"model t",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, cro",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"polaroid camera, polaroid land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, rv, r.v.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, crt screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, u-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"french loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"granny smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, cypripedium calceolus, cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, polyporus frondosus, grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue"
] |
kazuma313/emotion_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1901
- Accuracy: 0.5687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 1.9937 | 0.225 |
| No log | 2.0 | 40 | 1.7466 | 0.4188 |
| No log | 3.0 | 60 | 1.5370 | 0.5375 |
| No log | 4.0 | 80 | 1.4797 | 0.5125 |
| No log | 5.0 | 100 | 1.3531 | 0.55 |
| No log | 6.0 | 120 | 1.3115 | 0.5687 |
| No log | 7.0 | 140 | 1.2982 | 0.5375 |
| No log | 8.0 | 160 | 1.2543 | 0.5437 |
| No log | 9.0 | 180 | 1.2666 | 0.525 |
| No log | 10.0 | 200 | 1.2427 | 0.5312 |
| No log | 11.0 | 220 | 1.2100 | 0.5687 |
| No log | 12.0 | 240 | 1.2494 | 0.5375 |
| No log | 13.0 | 260 | 1.2266 | 0.5625 |
| No log | 14.0 | 280 | 1.2360 | 0.5437 |
| No log | 15.0 | 300 | 1.1901 | 0.5687 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
mhdiqbalpradipta/emotion_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification
This model is a fine-tuned version of [dennisjooo/emotion_classification](https://huggingface.co/dennisjooo/emotion_classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7891
- Accuracy: 0.7575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7123 | 1.0 | 25 | 0.8681 | 0.735 |
| 0.6349 | 2.0 | 50 | 0.8721 | 0.73 |
| 0.6354 | 3.0 | 75 | 0.8732 | 0.725 |
| 0.6189 | 4.0 | 100 | 0.8406 | 0.735 |
| 0.6364 | 5.0 | 125 | 0.8456 | 0.74 |
| 0.5833 | 6.0 | 150 | 0.8503 | 0.725 |
| 0.5384 | 7.0 | 175 | 0.8023 | 0.755 |
| 0.5297 | 8.0 | 200 | 0.8002 | 0.7525 |
| 0.5487 | 9.0 | 225 | 0.8253 | 0.745 |
| 0.5068 | 10.0 | 250 | 0.7891 | 0.7575 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
IsaacMwesigwa/autotrain-hj5w6-ewuft
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 2.895499120347367e-06
f1_micro: 0.0012045290291496024
f1_weighted: 2.8982892905428356e-06
precision_macro: 1.4494934165458512e-06
precision_micro: 0.0012045290291496024
precision_weighted: 1.4508901820640839e-06
recall_macro: 0.0012033694344163659
recall_micro: 0.0012045290291496024
recall_weighted: 0.0012045290291496024
accuracy: 0.0012045290291496024
|
[
"aaron long",
"aaron mooy",
"aaron ramsdale",
"aaron ramsey",
"abde ezzalzouli",
"abdelhamid sabiri",
"abdelkarim hassan",
"abderrazak hamdallah",
"abdou diallo",
"abdul fatawu issahaku",
"abdul manaf nurudeen",
"abdulaziz hatem",
"abdulelah al-amri",
"abdulellah al-malki",
"abdullah madu",
"abdullah otayf",
"abdulrahman al-aboud",
"abolfazl jalali",
"achraf dari",
"achraf hakimi",
"adam davies",
"adrien rabiot",
"agustín canobbio",
"ahmad nourollahi",
"ahmed alaaeldin",
"ahmed reda tagnaouti",
"ajdin hrustic",
"akram afif",
"alan franco",
"alejandro balde",
"aleksandar mitrović",
"alex sandro",
"alex telles",
"alexander bah",
"alexander djiku",
"alexander domínguez",
"alexis mac allister",
"alexis vega",
"alfred gomis",
"alfredo talavera",
"ali abdi",
"ali al-bulaihi",
"ali al-hassan",
"ali assadalla",
"ali gholizadeh",
"ali karimi",
"ali maâloul",
"alidu seidu",
"alireza beiranvand",
"alireza jahanbakhsh",
"alisson",
"alistair johnston",
"almoez ali",
"alphonse areola",
"alphonso davies",
"amadou onana",
"amir abedzadeh",
"anass zaroury",
"andreas christensen",
"andreas cornelius",
"andreas skov olsen",
"andrej kramarić",
"andrew redmayne",
"andries noppert",
"andrija živković",
"andré ayew ",
"andré onana",
"andré silva",
"andré-frank zambo anguissa",
"andrés guardado ",
"anis ben slimane",
"ansu fati",
"ante budimir",
"anthony contreras",
"anthony hernández",
"antoine griezmann",
"antoine semenyo",
"antonee robinson",
"antonio rüdiger",
"antony",
"antónio silva",
"ao tanaka",
"ardon jashari",
"arkadiusz milik",
"armel bella-kotchap",
"arthur theate",
"artur jędrzejczyk",
"assim madibo",
"atiba hutchinson ",
"aurélien tchouaméni",
"awer mabil",
"axel disasi",
"axel witsel",
"ayase ueda",
"aymen dahmen",
"aymen mathlouthi",
"aymeric laporte",
"ayrton preciado",
"aziz behich",
"azzedine ounahi",
"aïssa laïdouni",
"baba rahman",
"badr benoun",
"bailey wright",
"bamba dieng",
"bartosz bereszyński",
"bassam al-rawi",
"bechir ben saïd",
"ben cabango",
"ben davies",
"ben white",
"benjamin pavard",
"bernardo silva",
"bilal el khannous",
"bilel ifa",
"borna barišić",
"borna sosa",
"boualem khoukhi",
"boulaye dia",
"brandon aguilera",
"breel embolo",
"bremer",
"brenden aaronson",
"brennan johnson",
"bruno fernandes",
"bruno guimarães",
"bruno petković",
"bryan mbeumo",
"bryan oviedo",
"bryan ruiz ",
"bukayo saka",
"callum wilson",
"cameron carter-vickers",
"cameron devlin",
"carlos gruezo",
"carlos martínez",
"carlos rodríguez",
"carlos soler",
"casemiro",
"celso borges",
"charles de ketelaere",
"cheikhou kouyaté",
"cho gue-sung",
"cho yu-min",
"chris gunter",
"chris mepham",
"christian bassogog",
"christian eriksen",
"christian fassnacht",
"christian günter",
"christian nørgaard",
"christian pulisic",
"christopher wooh",
"cody gakpo",
"collins fai",
"connor roberts",
"conor coady",
"conor gallagher",
"craig goodwin",
"cristian roldan",
"cristian romero",
"cristiano ronaldo ",
"cyle larin",
"césar azpilicueta",
"césar montes",
"daichi kamada",
"daizen maeda",
"daley blind",
"damian szymański",
"dani alves",
"dani carvajal",
"dani olmo",
"daniel afriyie",
"daniel amartey",
"daniel chacón",
"daniel james",
"daniel schmidt",
"daniel wass",
"daniel-kofi kyereh",
"danilo",
"danilo pereira",
"danny vukovic",
"danny ward",
"darko lazović",
"darwin núñez",
"david raum",
"david raya",
"david wotherspoon",
"davy klaassen",
"dayne st. clair",
"dayot upamecano",
"deandre yedlin",
"declan rice",
"dejan lovren",
"denis odoi",
"denis zakaria",
"denzel dumfries",
"derek cornelius",
"devis epassy",
"diego godín ",
"diego palacios",
"diogo costa",
"diogo dalot",
"djibril sow",
"djorkaeff reasco",
"domagoj vida",
"dominik livaković",
"douglas lópez",
"dries mertens",
"dušan tadić ",
"dušan vlahović",
"dylan bronn",
"dylan levitt",
"eden hazard ",
"ederson",
"edimilson fernandes",
"edinson cavani",
"edson álvarez",
"eduardo camavinga",
"ehsan hajsafi ",
"eiji kawashima",
"elisha owusu",
"ellyes skhiri",
"emiliano martínez",
"enner valencia ",
"enzo ebosse",
"enzo fernández",
"eray cömert",
"eric dier",
"eric garcía",
"eric maxim choupo-moting",
"esteban alvarado",
"ethan ampadu",
"ethan horvath",
"exequiel palacios",
"fabian frei",
"fabian rieder",
"fabian schär",
"fabinho",
"facundo pellistri",
"facundo torres",
"famara diédhiou",
"federico valverde",
"ferjani sassi",
"fernando muslera",
"ferran torres",
"filip kostić",
"filip mladenović",
"filip đuričić",
"firas al-buraikan",
"fodé ballo-touré",
"formose mendy",
"fran karačić",
"francisco calvo",
"franco armani",
"fred",
"frederik rønnow",
"frenkie de jong",
"félix torres",
"gabriel jesus",
"gabriel martinelli",
"gaku shibasaki",
"garang kuol",
"gareth bale ",
"gavi",
"gaël ondoua",
"georges-kévin nkoudou",
"gerardo arteaga",
"germán pezzella",
"gerson torres",
"gerónimo rulli",
"ghailene chaalali",
"gideon mensah",
"giorgian de arrascaeta",
"giovanni reyna",
"gonzalo montiel",
"gonzalo plata",
"gonçalo ramos",
"granit xhaka ",
"gregor kobel",
"grzegorz krychowiak",
"guido rodríguez",
"guillermo ochoa",
"guillermo varela",
"haitham asiri",
"haji wright",
"hakim ziyech",
"hannibal mejbri",
"hans vanaken",
"haris seferovic",
"harry kane ",
"harry maguire",
"harry souttar",
"harry wilson",
"hassan al-haydos ",
"hassan al-tambakti",
"hattan bahebri",
"henry martín",
"hernán galíndez",
"hidemasa morita",
"hiroki ito",
"hiroki sakai",
"hirving lozano",
"homam ahmed",
"hong chul",
"hossein hosseini",
"hossein kanaanizadegan",
"hugo guillamón",
"hugo lloris ",
"hwang hee-chan",
"hwang in-beom",
"hwang ui-jo",
"héctor herrera",
"héctor moreno",
"ibrahim danlad",
"ibrahima konaté",
"idrissa gueye",
"iké ugbo",
"ilias chair",
"iliman ndiaye",
"ismaeel mohammad",
"ismail jakobs",
"ismaël koné",
"ismaïla sarr",
"issam jebali",
"ivan ilić",
"ivan perišić",
"ivica ivušić",
"ivo grbić",
"iñaki williams",
"jack grealish",
"jackson irvine",
"jackson porozo",
"jakub kamiński",
"jakub kiwior",
"jamal musiala",
"james maddison",
"james pantemis",
"jamie maclaren",
"jan bednarek",
"jan vertonghen",
"jason cummings",
"jassem gaber",
"jawad el yamiq",
"jean-charles castelletto",
"jean-pierre nsame",
"jens stryger larsen",
"jeong woo-yeong",
"jeremie frimpong",
"jeremy sarmiento",
"jerome ngom mbekeli",
"jesper lindstrøm",
"jesús ferreira",
"jesús gallardo",
"jewison bennette",
"jo hyeon-woo",
"joachim andersen",
"joakim mæhle",
"joe allen",
"joe morrell",
"joe rodon",
"joe scally",
"joel campbell",
"joel king",
"joel waterman",
"johan venegas",
"johan vásquez",
"john stones",
"jonas hofmann",
"jonas omlin",
"jonas wind",
"jonathan david",
"jonathan osorio",
"jonny williams",
"jordan ayew",
"jordan henderson",
"jordan morris",
"jordan pickford",
"jordan veretout",
"jordi alba",
"jorge sánchez",
"joseph aidoo",
"josh sargent",
"joshua kimmich",
"josip juranović",
"josip stanišić",
"josip šutalo",
"josé cifuentes",
"josé giménez",
"josé luis rodríguez",
"josé sá",
"joão cancelo",
"joão félix",
"joão mário",
"joão palhinha",
"joško gvardiol",
"juan foyth",
"juan pablo vargas",
"jude bellingham",
"jules koundé",
"julian brandt",
"julián álvarez",
"jung woo-young",
"junior hoilett",
"junya ito",
"jurriën timber",
"justin bijlow",
"jérémy doku",
"kai havertz",
"kalidou koulibaly ",
"kalvin phillips",
"kamal miller",
"kamal sowah",
"kamaldeen sulemana",
"kamil glik",
"kamil grabara",
"kamil grosicki",
"kaoru mitoma",
"karim adeyemi",
"karim ansarifard",
"karim benzema",
"karim boudiaf",
"karl toko ekambi",
"karol świderski",
"kasper dolberg",
"kasper schmeichel",
"keanu baccus",
"kellyn acosta",
"kendall waston",
"kenneth taylor",
"kevin de bruyne",
"kevin rodríguez",
"kevin trapp",
"kevin álvarez",
"keylor navas",
"keysher fuller",
"khalid muneer",
"kieffer moore",
"kieran trippier",
"kim jin-su",
"kim min-jae",
"kim moon-hwan",
"kim seung-gyu",
"kim tae-hwan",
"kim young-gwon",
"kingsley coman",
"ko itakura",
"koen casteels",
"koke",
"kristijan jakić",
"krystian bielik",
"krzysztof piątek",
"krépin diatta",
"kwon chang-hoon",
"kwon kyung-won",
"kye rowles",
"kyle walker",
"kylian mbappé",
"lautaro martínez",
"lawrence ati-zigi",
"leander dendoncker",
"leandro paredes",
"leandro trossard",
"lee jae-sung",
"lee kang-in",
"leon goretzka",
"leroy sané",
"liam fraser",
"liam millar",
"lionel messi ",
"lisandro martínez",
"lovro majer",
"loïs openda",
"luca de la torre",
"lucas cavallini",
"lucas hernandez",
"lucas paquetá",
"lucas torreira",
"luis chávez",
"luis romo",
"luis suárez",
"luka jović",
"luka modrić ",
"luka sučić",
"lukas klostermann",
"luke shaw",
"luuk de jong",
"majid hosseini",
"mamadou loum",
"manuel akanji",
"manuel neuer ",
"manuel ugarte",
"marc-andré ter stegen",
"marcelo brozović",
"marco asensio",
"marcos acuña",
"marcos llorente",
"marcus rashford",
"marcus thuram",
"mario götze",
"mario pašalić",
"mark harris",
"mark-anthony kaye",
"marko dmitrović",
"marko grujić",
"marko livaja",
"marquinhos",
"marten de roon",
"martin boyle",
"martin braithwaite",
"martin erlić",
"martin hongla",
"martín cáceres",
"mason mount",
"mateo kovačić",
"mateusz wieteska",
"matheus nunes",
"mathew leckie",
"mathew ryan ",
"mathias jensen",
"mathías olivera",
"matt turner",
"matteo guendouzi",
"matthew smith",
"matthias ginter",
"matthijs de ligt",
"matty cash",
"matías vecino",
"matías viña",
"maxi gómez",
"maya yoshida ",
"mehdi taremi",
"mehdi torabi",
"memphis depay",
"meshaal barsham",
"michael estrada",
"michał skóraś",
"michel aebischer",
"michy batshuayi",
"miki yamane",
"mikkel damsgaard",
"milad mohammadi",
"milan borjan",
"miloš degenek",
"miloš veljković",
"mislav oršić",
"mitchell duke",
"mohamed ali ben romdhane",
"mohamed dräger",
"mohamed kanno",
"mohammed al-breik",
"mohammed al-owais",
"mohammed al-rubaie",
"mohammed kudus",
"mohammed muntari",
"mohammed salisu",
"mohammed waad",
"moisés caicedo",
"moisés ramírez",
"montassar talbi",
"morteza pouraliganji",
"mostafa meshaal",
"mouez hassen",
"moumi ngamaleu",
"moustapha name",
"munir mohamedi",
"musab kheder",
"na sang-ho",
"nader ghandri",
"nahuel molina",
"naif al-hadhrami",
"nampalys mendy",
"nasser al-dawsari",
"nathan aké",
"nathaniel atkinson",
"nawaf al-abed",
"nawaf al-aqidi",
"nayef aguerd",
"naïm sliti",
"neco williams",
"nemanja gudelj",
"nemanja maksimović",
"nemanja radonjić",
"neymar",
"nick pope",
"niclas füllkrug",
"nico elvedi",
"nico schlotterbeck",
"nico williams",
"nicola zalewski",
"nicolas jackson",
"nicolas nkoulou",
"nicolás otamendi",
"nicolás tagliafico",
"nicolás de la cruz",
"niklas süle",
"nikola milenković",
"nikola vlašić",
"noa lang",
"noah okafor",
"nouhou tolo",
"noussair mazraoui",
"nuno mendes",
"néstor araujo",
"oliver christensen",
"olivier giroud",
"olivier mbaizo",
"olivier ntcham",
"orbelín pineda",
"osman bukari",
"otávio",
"ousmane dembélé",
"pablo sarabia",
"paik seung-ho",
"pape abou cissé",
"pape gueye",
"pape matar sarr",
"papu gómez",
"pathé ciss",
"patrick sequeira",
"pau torres",
"paulo dybala",
"payam niazmand",
"pedri",
"pedro",
"pepe",
"pervis estupiñán",
"phil foden",
"philipp köhn",
"piero hincapié",
"pierre kunde",
"pierre-emile højbjerg",
"piotr zieliński",
"predrag rajković",
"przemysław frankowski",
"rafael leão",
"raheem sterling",
"ramin rezaeian",
"randal kolo muani",
"raphaël guerreiro",
"raphaël varane",
"raphinha",
"rasmus kristensen",
"raúl jiménez",
"remko pasveer",
"remo freuler",
"renato steffen",
"ricardo horta",
"ricardo rodriguez",
"richarlison",
"richie laryea",
"riley mcgree",
"ritsu dōan",
"riyadh sharahili",
"roan wilson",
"robert arboleda",
"robert gumny",
"robert lewandowski ",
"robert skov",
"robert sánchez",
"roberto alvarado",
"rodolfo cota",
"rodri",
"rodrigo bentancur",
"rodrigo de paul",
"rodrygo",
"rogelio funes mori",
"romain saïss ",
"romario ibarra",
"romelu lukaku",
"ronald araújo",
"rouzbeh cheshmi",
"ruben vargas",
"rubin colwill",
"rui patrício",
"ró-ró",
"rónald matarrita",
"rúben dias",
"rúben neves",
"saad al-sheeb",
"sadegh moharrami",
"sadio mané",
"saeid ezatolahi",
"saleh al-shehri",
"salem al-dawsari",
"salem al-hajri",
"salis abdul samed",
"salman al-faraj ",
"sam adekugbe",
"saman ghoddos",
"sami al-najei",
"samuel gouet",
"samuel piette",
"sardar azmoun",
"saud abdulhamid",
"saša lukić",
"sean johnson",
"sebas méndez",
"sebastian szymański",
"sebastián coates",
"sebastián sosa",
"seifeddine jaziri",
"selim amallah",
"seny dieng",
"serge gnabry",
"sergej milinković-savić",
"sergio busquets ",
"sergio rochet",
"sergiño dest",
"shaq moore",
"shogo taniguchi",
"shojae khalilzadeh",
"shuto machino",
"shūichi gonda",
"silvan widmer",
"simon kjær ",
"simon mignolet",
"simon ngapandouetnbu",
"sofiane boufal",
"sofyan amrabat",
"son heung-min ",
"son jun-ho",
"song bum-keun",
"song min-kyu",
"sorba thomas",
"souaibou marou",
"srđan babić",
"stefan mitrović",
"stefan de vrij",
"stephen eustáquio",
"steve mandanda",
"steven berghuis",
"steven bergwijn",
"steven vitória",
"strahinja eraković",
"strahinja pavlović",
"sultan al-ghannam",
"szymon żurkowski",
"taha yassine khenissi",
"tajon buchanan",
"takefusa kubo",
"takehiro tomiyasu",
"takuma asano",
"takumi minamino",
"tarek salman",
"tariq lamptey",
"teun koopmeiners",
"theo hernandez",
"thiago almada",
"thiago silva ",
"thibaut courtois",
"thilo kehrer",
"thomas delaney",
"thomas deng",
"thomas meunier",
"thomas müller",
"thomas partey",
"thorgan hazard",
"tim ream",
"timothy castagne",
"timothy weah",
"toby alderweireld",
"tom lockyer",
"trent alexander-arnold",
"tyler adams",
"tyrell malacia",
"unai simón",
"uriel antuna",
"uroš račić",
"vahid amiri",
"vanja milinković-savić",
"victor nelsson",
"vincent aboubakar ",
"vincent janssen",
"vinícius júnior",
"virgil van dijk ",
"vitinha",
"wahbi khazri",
"wajdi kechrida",
"walid cheddira",
"walker zimmerman",
"wataru endo",
"wayne hennessey",
"weston mckennie",
"weverton",
"william carvalho",
"william pacho",
"william saliba",
"wojciech szczęsny",
"wout faes",
"wout weghorst",
"xavi simons",
"xavier arreaga",
"xherdan shaqiri",
"yahia attiyat allah",
"yahya jabrane",
"yann sommer",
"yannick carrasco",
"yasser al-shahrani",
"yassine bounou",
"yassine meriah",
"yeltsin tejeda",
"yeremy pino",
"yoon jong-gyu",
"youri tielemans",
"yousef hassan",
"youssef en-nesyri",
"youssef msakni ",
"youssouf fofana",
"youssouf sabaly",
"youssoufa moukoko",
"youstin salas",
"yuki soma",
"yunus musah",
"yussuf poulsen",
"yuto nagatomo",
"zakaria aboukhlal",
"zeno debast",
"álvaro morata",
"álvaro zamora",
"ángel correa",
"ángel di maría",
"ángel mena",
"ángelo preciado",
"éder militão",
"édouard mendy",
"érick gutiérrez",
"éverton ribeiro",
"óscar duarte",
"i̇lkay gündoğan",
"łukasz skorupski"
] |
IsaacMwesigwa/autotrain-74s1b-3bdvq
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 8.737913685897285e+36
f1_macro: 0.16666666666666666
f1_micro: 0.3333333333333333
f1_weighted: 0.16666666666666666
precision_macro: 0.1111111111111111
precision_micro: 0.3333333333333333
precision_weighted: 0.1111111111111111
recall_macro: 0.3333333333333333
recall_micro: 0.3333333333333333
recall_weighted: 0.3333333333333333
accuracy: 0.3333333333333333
|
[
"aaron long",
"aaron mooy",
"aaron ramsdale"
] |
parvpareek/convnext-base-224-22k-1k-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-base-224-22k-1k-finetuned-eurosat
This model is a fine-tuned version of [facebook/convnext-base-224-22k-1k](https://huggingface.co/facebook/convnext-base-224-22k-1k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4190
- Accuracy: 0.8483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6643 | 0.99 | 70 | 0.6090 | 0.7754 |
| 0.4729 | 2.0 | 141 | 0.4641 | 0.8253 |
| 0.3858 | 2.98 | 210 | 0.4190 | 0.8483 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"mel",
"nv",
"bcc",
"akiec",
"bkl",
"df",
"vasc"
] |
Ening/dog_or_foot_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dog_or_foot_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0346
- Accuracy: 0.9976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3161 | 0.99 | 26 | 0.1164 | 0.9976 |
| 0.0495 | 1.98 | 52 | 0.0490 | 0.9905 |
| 0.0371 | 2.97 | 78 | 0.0346 | 0.9976 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"dog",
"food"
] |
jjunhaoo/food_classifier
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jjunhaoo/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0402
- Validation Loss: 2.9013
- Train Accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 4.5433 | 4.3217 | 0.75 | 0 |
| 4.1725 | 3.9809 | 1.0 | 1 |
| 3.8289 | 3.6061 | 1.0 | 2 |
| 3.4173 | 3.2314 | 1.0 | 3 |
| 3.0402 | 2.9013 | 1.0 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
IsaacMwesigwa/autotrain-1pwox-g76oa
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: nan
f1_macro: 2.895499120347367e-06
f1_micro: 0.0012045290291496024
f1_weighted: 2.8982892905428356e-06
precision_macro: 1.4494934165458512e-06
precision_micro: 0.0012045290291496024
precision_weighted: 1.4508901820640839e-06
recall_macro: 0.0012033694344163659
recall_micro: 0.0012045290291496024
recall_weighted: 0.0012045290291496024
accuracy: 0.0012045290291496024
|
[
"aaron long",
"aaron mooy",
"aaron ramsdale",
"aaron ramsey",
"abde ezzalzouli",
"abdelhamid sabiri",
"abdelkarim hassan",
"abderrazak hamdallah",
"abdou diallo",
"abdul fatawu issahaku",
"abdul manaf nurudeen",
"abdulaziz hatem",
"abdulelah al-amri",
"abdulellah al-malki",
"abdullah madu",
"abdullah otayf",
"abdulrahman al-aboud",
"abolfazl jalali",
"achraf dari",
"achraf hakimi",
"adam davies",
"adrien rabiot",
"agustín canobbio",
"ahmad nourollahi",
"ahmed alaaeldin",
"ahmed reda tagnaouti",
"ajdin hrustic",
"akram afif",
"alan franco",
"alejandro balde",
"aleksandar mitrović",
"alex sandro",
"alex telles",
"alexander bah",
"alexander djiku",
"alexander domínguez",
"alexis mac allister",
"alexis vega",
"alfred gomis",
"alfredo talavera",
"ali abdi",
"ali al-bulaihi",
"ali al-hassan",
"ali assadalla",
"ali gholizadeh",
"ali karimi",
"ali maâloul",
"alidu seidu",
"alireza beiranvand",
"alireza jahanbakhsh",
"alisson",
"alistair johnston",
"almoez ali",
"alphonse areola",
"alphonso davies",
"amadou onana",
"amir abedzadeh",
"anass zaroury",
"andreas christensen",
"andreas cornelius",
"andreas skov olsen",
"andrej kramarić",
"andrew redmayne",
"andries noppert",
"andrija živković",
"andré ayew ",
"andré onana",
"andré silva",
"andré-frank zambo anguissa",
"andrés guardado ",
"anis ben slimane",
"ansu fati",
"ante budimir",
"anthony contreras",
"anthony hernández",
"antoine griezmann",
"antoine semenyo",
"antonee robinson",
"antonio rüdiger",
"antony",
"antónio silva",
"ao tanaka",
"ardon jashari",
"arkadiusz milik",
"armel bella-kotchap",
"arthur theate",
"artur jędrzejczyk",
"assim madibo",
"atiba hutchinson ",
"aurélien tchouaméni",
"awer mabil",
"axel disasi",
"axel witsel",
"ayase ueda",
"aymen dahmen",
"aymen mathlouthi",
"aymeric laporte",
"ayrton preciado",
"aziz behich",
"azzedine ounahi",
"aïssa laïdouni",
"baba rahman",
"badr benoun",
"bailey wright",
"bamba dieng",
"bartosz bereszyński",
"bassam al-rawi",
"bechir ben saïd",
"ben cabango",
"ben davies",
"ben white",
"benjamin pavard",
"bernardo silva",
"bilal el khannous",
"bilel ifa",
"borna barišić",
"borna sosa",
"boualem khoukhi",
"boulaye dia",
"brandon aguilera",
"breel embolo",
"bremer",
"brenden aaronson",
"brennan johnson",
"bruno fernandes",
"bruno guimarães",
"bruno petković",
"bryan mbeumo",
"bryan oviedo",
"bryan ruiz ",
"bukayo saka",
"callum wilson",
"cameron carter-vickers",
"cameron devlin",
"carlos gruezo",
"carlos martínez",
"carlos rodríguez",
"carlos soler",
"casemiro",
"celso borges",
"charles de ketelaere",
"cheikhou kouyaté",
"cho gue-sung",
"cho yu-min",
"chris gunter",
"chris mepham",
"christian bassogog",
"christian eriksen",
"christian fassnacht",
"christian günter",
"christian nørgaard",
"christian pulisic",
"christopher wooh",
"cody gakpo",
"collins fai",
"connor roberts",
"conor coady",
"conor gallagher",
"craig goodwin",
"cristian roldan",
"cristian romero",
"cristiano ronaldo ",
"cyle larin",
"césar azpilicueta",
"césar montes",
"daichi kamada",
"daizen maeda",
"daley blind",
"damian szymański",
"dani alves",
"dani carvajal",
"dani olmo",
"daniel afriyie",
"daniel amartey",
"daniel chacón",
"daniel james",
"daniel schmidt",
"daniel wass",
"daniel-kofi kyereh",
"danilo",
"danilo pereira",
"danny vukovic",
"danny ward",
"darko lazović",
"darwin núñez",
"david raum",
"david raya",
"david wotherspoon",
"davy klaassen",
"dayne st. clair",
"dayot upamecano",
"deandre yedlin",
"declan rice",
"dejan lovren",
"denis odoi",
"denis zakaria",
"denzel dumfries",
"derek cornelius",
"devis epassy",
"diego godín ",
"diego palacios",
"diogo costa",
"diogo dalot",
"djibril sow",
"djorkaeff reasco",
"domagoj vida",
"dominik livaković",
"douglas lópez",
"dries mertens",
"dušan tadić ",
"dušan vlahović",
"dylan bronn",
"dylan levitt",
"eden hazard ",
"ederson",
"edimilson fernandes",
"edinson cavani",
"edson álvarez",
"eduardo camavinga",
"ehsan hajsafi ",
"eiji kawashima",
"elisha owusu",
"ellyes skhiri",
"emiliano martínez",
"enner valencia ",
"enzo ebosse",
"enzo fernández",
"eray cömert",
"eric dier",
"eric garcía",
"eric maxim choupo-moting",
"esteban alvarado",
"ethan ampadu",
"ethan horvath",
"exequiel palacios",
"fabian frei",
"fabian rieder",
"fabian schär",
"fabinho",
"facundo pellistri",
"facundo torres",
"famara diédhiou",
"federico valverde",
"ferjani sassi",
"fernando muslera",
"ferran torres",
"filip kostić",
"filip mladenović",
"filip đuričić",
"firas al-buraikan",
"fodé ballo-touré",
"formose mendy",
"fran karačić",
"francisco calvo",
"franco armani",
"fred",
"frederik rønnow",
"frenkie de jong",
"félix torres",
"gabriel jesus",
"gabriel martinelli",
"gaku shibasaki",
"garang kuol",
"gareth bale ",
"gavi",
"gaël ondoua",
"georges-kévin nkoudou",
"gerardo arteaga",
"germán pezzella",
"gerson torres",
"gerónimo rulli",
"ghailene chaalali",
"gideon mensah",
"giorgian de arrascaeta",
"giovanni reyna",
"gonzalo montiel",
"gonzalo plata",
"gonçalo ramos",
"granit xhaka ",
"gregor kobel",
"grzegorz krychowiak",
"guido rodríguez",
"guillermo ochoa",
"guillermo varela",
"haitham asiri",
"haji wright",
"hakim ziyech",
"hannibal mejbri",
"hans vanaken",
"haris seferovic",
"harry kane ",
"harry maguire",
"harry souttar",
"harry wilson",
"hassan al-haydos ",
"hassan al-tambakti",
"hattan bahebri",
"henry martín",
"hernán galíndez",
"hidemasa morita",
"hiroki ito",
"hiroki sakai",
"hirving lozano",
"homam ahmed",
"hong chul",
"hossein hosseini",
"hossein kanaanizadegan",
"hugo guillamón",
"hugo lloris ",
"hwang hee-chan",
"hwang in-beom",
"hwang ui-jo",
"héctor herrera",
"héctor moreno",
"ibrahim danlad",
"ibrahima konaté",
"idrissa gueye",
"iké ugbo",
"ilias chair",
"iliman ndiaye",
"ismaeel mohammad",
"ismail jakobs",
"ismaël koné",
"ismaïla sarr",
"issam jebali",
"ivan ilić",
"ivan perišić",
"ivica ivušić",
"ivo grbić",
"iñaki williams",
"jack grealish",
"jackson irvine",
"jackson porozo",
"jakub kamiński",
"jakub kiwior",
"jamal musiala",
"james maddison",
"james pantemis",
"jamie maclaren",
"jan bednarek",
"jan vertonghen",
"jason cummings",
"jassem gaber",
"jawad el yamiq",
"jean-charles castelletto",
"jean-pierre nsame",
"jens stryger larsen",
"jeong woo-yeong",
"jeremie frimpong",
"jeremy sarmiento",
"jerome ngom mbekeli",
"jesper lindstrøm",
"jesús ferreira",
"jesús gallardo",
"jewison bennette",
"jo hyeon-woo",
"joachim andersen",
"joakim mæhle",
"joe allen",
"joe morrell",
"joe rodon",
"joe scally",
"joel campbell",
"joel king",
"joel waterman",
"johan venegas",
"johan vásquez",
"john stones",
"jonas hofmann",
"jonas omlin",
"jonas wind",
"jonathan david",
"jonathan osorio",
"jonny williams",
"jordan ayew",
"jordan henderson",
"jordan morris",
"jordan pickford",
"jordan veretout",
"jordi alba",
"jorge sánchez",
"joseph aidoo",
"josh sargent",
"joshua kimmich",
"josip juranović",
"josip stanišić",
"josip šutalo",
"josé cifuentes",
"josé giménez",
"josé luis rodríguez",
"josé sá",
"joão cancelo",
"joão félix",
"joão mário",
"joão palhinha",
"joško gvardiol",
"juan foyth",
"juan pablo vargas",
"jude bellingham",
"jules koundé",
"julian brandt",
"julián álvarez",
"jung woo-young",
"junior hoilett",
"junya ito",
"jurriën timber",
"justin bijlow",
"jérémy doku",
"kai havertz",
"kalidou koulibaly ",
"kalvin phillips",
"kamal miller",
"kamal sowah",
"kamaldeen sulemana",
"kamil glik",
"kamil grabara",
"kamil grosicki",
"kaoru mitoma",
"karim adeyemi",
"karim ansarifard",
"karim benzema",
"karim boudiaf",
"karl toko ekambi",
"karol świderski",
"kasper dolberg",
"kasper schmeichel",
"keanu baccus",
"kellyn acosta",
"kendall waston",
"kenneth taylor",
"kevin de bruyne",
"kevin rodríguez",
"kevin trapp",
"kevin álvarez",
"keylor navas",
"keysher fuller",
"khalid muneer",
"kieffer moore",
"kieran trippier",
"kim jin-su",
"kim min-jae",
"kim moon-hwan",
"kim seung-gyu",
"kim tae-hwan",
"kim young-gwon",
"kingsley coman",
"ko itakura",
"koen casteels",
"koke",
"kristijan jakić",
"krystian bielik",
"krzysztof piątek",
"krépin diatta",
"kwon chang-hoon",
"kwon kyung-won",
"kye rowles",
"kyle walker",
"kylian mbappé",
"lautaro martínez",
"lawrence ati-zigi",
"leander dendoncker",
"leandro paredes",
"leandro trossard",
"lee jae-sung",
"lee kang-in",
"leon goretzka",
"leroy sané",
"liam fraser",
"liam millar",
"lionel messi ",
"lisandro martínez",
"lovro majer",
"loïs openda",
"luca de la torre",
"lucas cavallini",
"lucas hernandez",
"lucas paquetá",
"lucas torreira",
"luis chávez",
"luis romo",
"luis suárez",
"luka jović",
"luka modrić ",
"luka sučić",
"lukas klostermann",
"luke shaw",
"luuk de jong",
"majid hosseini",
"mamadou loum",
"manuel akanji",
"manuel neuer ",
"manuel ugarte",
"marc-andré ter stegen",
"marcelo brozović",
"marco asensio",
"marcos acuña",
"marcos llorente",
"marcus rashford",
"marcus thuram",
"mario götze",
"mario pašalić",
"mark harris",
"mark-anthony kaye",
"marko dmitrović",
"marko grujić",
"marko livaja",
"marquinhos",
"marten de roon",
"martin boyle",
"martin braithwaite",
"martin erlić",
"martin hongla",
"martín cáceres",
"mason mount",
"mateo kovačić",
"mateusz wieteska",
"matheus nunes",
"mathew leckie",
"mathew ryan ",
"mathias jensen",
"mathías olivera",
"matt turner",
"matteo guendouzi",
"matthew smith",
"matthias ginter",
"matthijs de ligt",
"matty cash",
"matías vecino",
"matías viña",
"maxi gómez",
"maya yoshida ",
"mehdi taremi",
"mehdi torabi",
"memphis depay",
"meshaal barsham",
"michael estrada",
"michał skóraś",
"michel aebischer",
"michy batshuayi",
"miki yamane",
"mikkel damsgaard",
"milad mohammadi",
"milan borjan",
"miloš degenek",
"miloš veljković",
"mislav oršić",
"mitchell duke",
"mohamed ali ben romdhane",
"mohamed dräger",
"mohamed kanno",
"mohammed al-breik",
"mohammed al-owais",
"mohammed al-rubaie",
"mohammed kudus",
"mohammed muntari",
"mohammed salisu",
"mohammed waad",
"moisés caicedo",
"moisés ramírez",
"montassar talbi",
"morteza pouraliganji",
"mostafa meshaal",
"mouez hassen",
"moumi ngamaleu",
"moustapha name",
"munir mohamedi",
"musab kheder",
"na sang-ho",
"nader ghandri",
"nahuel molina",
"naif al-hadhrami",
"nampalys mendy",
"nasser al-dawsari",
"nathan aké",
"nathaniel atkinson",
"nawaf al-abed",
"nawaf al-aqidi",
"nayef aguerd",
"naïm sliti",
"neco williams",
"nemanja gudelj",
"nemanja maksimović",
"nemanja radonjić",
"neymar",
"nick pope",
"niclas füllkrug",
"nico elvedi",
"nico schlotterbeck",
"nico williams",
"nicola zalewski",
"nicolas jackson",
"nicolas nkoulou",
"nicolás otamendi",
"nicolás tagliafico",
"nicolás de la cruz",
"niklas süle",
"nikola milenković",
"nikola vlašić",
"noa lang",
"noah okafor",
"nouhou tolo",
"noussair mazraoui",
"nuno mendes",
"néstor araujo",
"oliver christensen",
"olivier giroud",
"olivier mbaizo",
"olivier ntcham",
"orbelín pineda",
"osman bukari",
"otávio",
"ousmane dembélé",
"pablo sarabia",
"paik seung-ho",
"pape abou cissé",
"pape gueye",
"pape matar sarr",
"papu gómez",
"pathé ciss",
"patrick sequeira",
"pau torres",
"paulo dybala",
"payam niazmand",
"pedri",
"pedro",
"pepe",
"pervis estupiñán",
"phil foden",
"philipp köhn",
"piero hincapié",
"pierre kunde",
"pierre-emile højbjerg",
"piotr zieliński",
"predrag rajković",
"przemysław frankowski",
"rafael leão",
"raheem sterling",
"ramin rezaeian",
"randal kolo muani",
"raphaël guerreiro",
"raphaël varane",
"raphinha",
"rasmus kristensen",
"raúl jiménez",
"remko pasveer",
"remo freuler",
"renato steffen",
"ricardo horta",
"ricardo rodriguez",
"richarlison",
"richie laryea",
"riley mcgree",
"ritsu dōan",
"riyadh sharahili",
"roan wilson",
"robert arboleda",
"robert gumny",
"robert lewandowski ",
"robert skov",
"robert sánchez",
"roberto alvarado",
"rodolfo cota",
"rodri",
"rodrigo bentancur",
"rodrigo de paul",
"rodrygo",
"rogelio funes mori",
"romain saïss ",
"romario ibarra",
"romelu lukaku",
"ronald araújo",
"rouzbeh cheshmi",
"ruben vargas",
"rubin colwill",
"rui patrício",
"ró-ró",
"rónald matarrita",
"rúben dias",
"rúben neves",
"saad al-sheeb",
"sadegh moharrami",
"sadio mané",
"saeid ezatolahi",
"saleh al-shehri",
"salem al-dawsari",
"salem al-hajri",
"salis abdul samed",
"salman al-faraj ",
"sam adekugbe",
"saman ghoddos",
"sami al-najei",
"samuel gouet",
"samuel piette",
"sardar azmoun",
"saud abdulhamid",
"saša lukić",
"sean johnson",
"sebas méndez",
"sebastian szymański",
"sebastián coates",
"sebastián sosa",
"seifeddine jaziri",
"selim amallah",
"seny dieng",
"serge gnabry",
"sergej milinković-savić",
"sergio busquets ",
"sergio rochet",
"sergiño dest",
"shaq moore",
"shogo taniguchi",
"shojae khalilzadeh",
"shuto machino",
"shūichi gonda",
"silvan widmer",
"simon kjær ",
"simon mignolet",
"simon ngapandouetnbu",
"sofiane boufal",
"sofyan amrabat",
"son heung-min ",
"son jun-ho",
"song bum-keun",
"song min-kyu",
"sorba thomas",
"souaibou marou",
"srđan babić",
"stefan mitrović",
"stefan de vrij",
"stephen eustáquio",
"steve mandanda",
"steven berghuis",
"steven bergwijn",
"steven vitória",
"strahinja eraković",
"strahinja pavlović",
"sultan al-ghannam",
"szymon żurkowski",
"taha yassine khenissi",
"tajon buchanan",
"takefusa kubo",
"takehiro tomiyasu",
"takuma asano",
"takumi minamino",
"tarek salman",
"tariq lamptey",
"teun koopmeiners",
"theo hernandez",
"thiago almada",
"thiago silva ",
"thibaut courtois",
"thilo kehrer",
"thomas delaney",
"thomas deng",
"thomas meunier",
"thomas müller",
"thomas partey",
"thorgan hazard",
"tim ream",
"timothy castagne",
"timothy weah",
"toby alderweireld",
"tom lockyer",
"trent alexander-arnold",
"tyler adams",
"tyrell malacia",
"unai simón",
"uriel antuna",
"uroš račić",
"vahid amiri",
"vanja milinković-savić",
"victor nelsson",
"vincent aboubakar ",
"vincent janssen",
"vinícius júnior",
"virgil van dijk ",
"vitinha",
"wahbi khazri",
"wajdi kechrida",
"walid cheddira",
"walker zimmerman",
"wataru endo",
"wayne hennessey",
"weston mckennie",
"weverton",
"william carvalho",
"william pacho",
"william saliba",
"wojciech szczęsny",
"wout faes",
"wout weghorst",
"xavi simons",
"xavier arreaga",
"xherdan shaqiri",
"yahia attiyat allah",
"yahya jabrane",
"yann sommer",
"yannick carrasco",
"yasser al-shahrani",
"yassine bounou",
"yassine meriah",
"yeltsin tejeda",
"yeremy pino",
"yoon jong-gyu",
"youri tielemans",
"yousef hassan",
"youssef en-nesyri",
"youssef msakni ",
"youssouf fofana",
"youssouf sabaly",
"youssoufa moukoko",
"youstin salas",
"yuki soma",
"yunus musah",
"yussuf poulsen",
"yuto nagatomo",
"zakaria aboukhlal",
"zeno debast",
"álvaro morata",
"álvaro zamora",
"ángel correa",
"ángel di maría",
"ángel mena",
"ángelo preciado",
"éder militão",
"édouard mendy",
"érick gutiérrez",
"éverton ribeiro",
"óscar duarte",
"i̇lkay gündoğan",
"łukasz skorupski"
] |
IsaacMwesigwa/footballer-recognition
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 4.222931861877441
f1_macro: 0.20794824311359378
f1_micro: 0.24259214647072994
f1_weighted: 0.20814862686990657
precision_macro: 0.23546798702040436
precision_micro: 0.24259214647072994
precision_weighted: 0.23569488944104558
recall_macro: 0.24235860409145613
recall_micro: 0.24259214647072994
recall_weighted: 0.24259214647072994
accuracy: 0.24259214647072994
|
[
"aaron long",
"aaron mooy",
"aaron ramsdale",
"aaron ramsey",
"abde ezzalzouli",
"abdelhamid sabiri",
"abdelkarim hassan",
"abderrazak hamdallah",
"abdou diallo",
"abdul fatawu issahaku",
"abdul manaf nurudeen",
"abdulaziz hatem",
"abdulelah al-amri",
"abdulellah al-malki",
"abdullah madu",
"abdullah otayf",
"abdulrahman al-aboud",
"abolfazl jalali",
"achraf dari",
"achraf hakimi",
"adam davies",
"adrien rabiot",
"agustín canobbio",
"ahmad nourollahi",
"ahmed alaaeldin",
"ahmed reda tagnaouti",
"ajdin hrustic",
"akram afif",
"alan franco",
"alejandro balde",
"aleksandar mitrović",
"alex sandro",
"alex telles",
"alexander bah",
"alexander djiku",
"alexander domínguez",
"alexis mac allister",
"alexis vega",
"alfred gomis",
"alfredo talavera",
"ali abdi",
"ali al-bulaihi",
"ali al-hassan",
"ali assadalla",
"ali gholizadeh",
"ali karimi",
"ali maâloul",
"alidu seidu",
"alireza beiranvand",
"alireza jahanbakhsh",
"alisson",
"alistair johnston",
"almoez ali",
"alphonse areola",
"alphonso davies",
"amadou onana",
"amir abedzadeh",
"anass zaroury",
"andreas christensen",
"andreas cornelius",
"andreas skov olsen",
"andrej kramarić",
"andrew redmayne",
"andries noppert",
"andrija živković",
"andré ayew ",
"andré onana",
"andré silva",
"andré-frank zambo anguissa",
"andrés guardado ",
"anis ben slimane",
"ansu fati",
"ante budimir",
"anthony contreras",
"anthony hernández",
"antoine griezmann",
"antoine semenyo",
"antonee robinson",
"antonio rüdiger",
"antony",
"antónio silva",
"ao tanaka",
"ardon jashari",
"arkadiusz milik",
"armel bella-kotchap",
"arthur theate",
"artur jędrzejczyk",
"assim madibo",
"atiba hutchinson ",
"aurélien tchouaméni",
"awer mabil",
"axel disasi",
"axel witsel",
"ayase ueda",
"aymen dahmen",
"aymen mathlouthi",
"aymeric laporte",
"ayrton preciado",
"aziz behich",
"azzedine ounahi",
"aïssa laïdouni",
"baba rahman",
"badr benoun",
"bailey wright",
"bamba dieng",
"bartosz bereszyński",
"bassam al-rawi",
"bechir ben saïd",
"ben cabango",
"ben davies",
"ben white",
"benjamin pavard",
"bernardo silva",
"bilal el khannous",
"bilel ifa",
"borna barišić",
"borna sosa",
"boualem khoukhi",
"boulaye dia",
"brandon aguilera",
"breel embolo",
"bremer",
"brenden aaronson",
"brennan johnson",
"bruno fernandes",
"bruno guimarães",
"bruno petković",
"bryan mbeumo",
"bryan oviedo",
"bryan ruiz ",
"bukayo saka",
"callum wilson",
"cameron carter-vickers",
"cameron devlin",
"carlos gruezo",
"carlos martínez",
"carlos rodríguez",
"carlos soler",
"casemiro",
"celso borges",
"charles de ketelaere",
"cheikhou kouyaté",
"cho gue-sung",
"cho yu-min",
"chris gunter",
"chris mepham",
"christian bassogog",
"christian eriksen",
"christian fassnacht",
"christian günter",
"christian nørgaard",
"christian pulisic",
"christopher wooh",
"cody gakpo",
"collins fai",
"connor roberts",
"conor coady",
"conor gallagher",
"craig goodwin",
"cristian roldan",
"cristian romero",
"cristiano ronaldo ",
"cyle larin",
"césar azpilicueta",
"césar montes",
"daichi kamada",
"daizen maeda",
"daley blind",
"damian szymański",
"dani alves",
"dani carvajal",
"dani olmo",
"daniel afriyie",
"daniel amartey",
"daniel chacón",
"daniel james",
"daniel schmidt",
"daniel wass",
"daniel-kofi kyereh",
"danilo",
"danilo pereira",
"danny vukovic",
"danny ward",
"darko lazović",
"darwin núñez",
"david raum",
"david raya",
"david wotherspoon",
"davy klaassen",
"dayne st. clair",
"dayot upamecano",
"deandre yedlin",
"declan rice",
"dejan lovren",
"denis odoi",
"denis zakaria",
"denzel dumfries",
"derek cornelius",
"devis epassy",
"diego godín ",
"diego palacios",
"diogo costa",
"diogo dalot",
"djibril sow",
"djorkaeff reasco",
"domagoj vida",
"dominik livaković",
"douglas lópez",
"dries mertens",
"dušan tadić ",
"dušan vlahović",
"dylan bronn",
"dylan levitt",
"eden hazard ",
"ederson",
"edimilson fernandes",
"edinson cavani",
"edson álvarez",
"eduardo camavinga",
"ehsan hajsafi ",
"eiji kawashima",
"elisha owusu",
"ellyes skhiri",
"emiliano martínez",
"enner valencia ",
"enzo ebosse",
"enzo fernández",
"eray cömert",
"eric dier",
"eric garcía",
"eric maxim choupo-moting",
"esteban alvarado",
"ethan ampadu",
"ethan horvath",
"exequiel palacios",
"fabian frei",
"fabian rieder",
"fabian schär",
"fabinho",
"facundo pellistri",
"facundo torres",
"famara diédhiou",
"federico valverde",
"ferjani sassi",
"fernando muslera",
"ferran torres",
"filip kostić",
"filip mladenović",
"filip đuričić",
"firas al-buraikan",
"fodé ballo-touré",
"formose mendy",
"fran karačić",
"francisco calvo",
"franco armani",
"fred",
"frederik rønnow",
"frenkie de jong",
"félix torres",
"gabriel jesus",
"gabriel martinelli",
"gaku shibasaki",
"garang kuol",
"gareth bale ",
"gavi",
"gaël ondoua",
"georges-kévin nkoudou",
"gerardo arteaga",
"germán pezzella",
"gerson torres",
"gerónimo rulli",
"ghailene chaalali",
"gideon mensah",
"giorgian de arrascaeta",
"giovanni reyna",
"gonzalo montiel",
"gonzalo plata",
"gonçalo ramos",
"granit xhaka ",
"gregor kobel",
"grzegorz krychowiak",
"guido rodríguez",
"guillermo ochoa",
"guillermo varela",
"haitham asiri",
"haji wright",
"hakim ziyech",
"hannibal mejbri",
"hans vanaken",
"haris seferovic",
"harry kane ",
"harry maguire",
"harry souttar",
"harry wilson",
"hassan al-haydos ",
"hassan al-tambakti",
"hattan bahebri",
"henry martín",
"hernán galíndez",
"hidemasa morita",
"hiroki ito",
"hiroki sakai",
"hirving lozano",
"homam ahmed",
"hong chul",
"hossein hosseini",
"hossein kanaanizadegan",
"hugo guillamón",
"hugo lloris ",
"hwang hee-chan",
"hwang in-beom",
"hwang ui-jo",
"héctor herrera",
"héctor moreno",
"ibrahim danlad",
"ibrahima konaté",
"idrissa gueye",
"iké ugbo",
"ilias chair",
"iliman ndiaye",
"ismaeel mohammad",
"ismail jakobs",
"ismaël koné",
"ismaïla sarr",
"issam jebali",
"ivan ilić",
"ivan perišić",
"ivica ivušić",
"ivo grbić",
"iñaki williams",
"jack grealish",
"jackson irvine",
"jackson porozo",
"jakub kamiński",
"jakub kiwior",
"jamal musiala",
"james maddison",
"james pantemis",
"jamie maclaren",
"jan bednarek",
"jan vertonghen",
"jason cummings",
"jassem gaber",
"jawad el yamiq",
"jean-charles castelletto",
"jean-pierre nsame",
"jens stryger larsen",
"jeong woo-yeong",
"jeremie frimpong",
"jeremy sarmiento",
"jerome ngom mbekeli",
"jesper lindstrøm",
"jesús ferreira",
"jesús gallardo",
"jewison bennette",
"jo hyeon-woo",
"joachim andersen",
"joakim mæhle",
"joe allen",
"joe morrell",
"joe rodon",
"joe scally",
"joel campbell",
"joel king",
"joel waterman",
"johan venegas",
"johan vásquez",
"john stones",
"jonas hofmann",
"jonas omlin",
"jonas wind",
"jonathan david",
"jonathan osorio",
"jonny williams",
"jordan ayew",
"jordan henderson",
"jordan morris",
"jordan pickford",
"jordan veretout",
"jordi alba",
"jorge sánchez",
"joseph aidoo",
"josh sargent",
"joshua kimmich",
"josip juranović",
"josip stanišić",
"josip šutalo",
"josé cifuentes",
"josé giménez",
"josé luis rodríguez",
"josé sá",
"joão cancelo",
"joão félix",
"joão mário",
"joão palhinha",
"joško gvardiol",
"juan foyth",
"juan pablo vargas",
"jude bellingham",
"jules koundé",
"julian brandt",
"julián álvarez",
"jung woo-young",
"junior hoilett",
"junya ito",
"jurriën timber",
"justin bijlow",
"jérémy doku",
"kai havertz",
"kalidou koulibaly ",
"kalvin phillips",
"kamal miller",
"kamal sowah",
"kamaldeen sulemana",
"kamil glik",
"kamil grabara",
"kamil grosicki",
"kaoru mitoma",
"karim adeyemi",
"karim ansarifard",
"karim benzema",
"karim boudiaf",
"karl toko ekambi",
"karol świderski",
"kasper dolberg",
"kasper schmeichel",
"keanu baccus",
"kellyn acosta",
"kendall waston",
"kenneth taylor",
"kevin de bruyne",
"kevin rodríguez",
"kevin trapp",
"kevin álvarez",
"keylor navas",
"keysher fuller",
"khalid muneer",
"kieffer moore",
"kieran trippier",
"kim jin-su",
"kim min-jae",
"kim moon-hwan",
"kim seung-gyu",
"kim tae-hwan",
"kim young-gwon",
"kingsley coman",
"ko itakura",
"koen casteels",
"koke",
"kristijan jakić",
"krystian bielik",
"krzysztof piątek",
"krépin diatta",
"kwon chang-hoon",
"kwon kyung-won",
"kye rowles",
"kyle walker",
"kylian mbappé",
"lautaro martínez",
"lawrence ati-zigi",
"leander dendoncker",
"leandro paredes",
"leandro trossard",
"lee jae-sung",
"lee kang-in",
"leon goretzka",
"leroy sané",
"liam fraser",
"liam millar",
"lionel messi ",
"lisandro martínez",
"lovro majer",
"loïs openda",
"luca de la torre",
"lucas cavallini",
"lucas hernandez",
"lucas paquetá",
"lucas torreira",
"luis chávez",
"luis romo",
"luis suárez",
"luka jović",
"luka modrić ",
"luka sučić",
"lukas klostermann",
"luke shaw",
"luuk de jong",
"majid hosseini",
"mamadou loum",
"manuel akanji",
"manuel neuer ",
"manuel ugarte",
"marc-andré ter stegen",
"marcelo brozović",
"marco asensio",
"marcos acuña",
"marcos llorente",
"marcus rashford",
"marcus thuram",
"mario götze",
"mario pašalić",
"mark harris",
"mark-anthony kaye",
"marko dmitrović",
"marko grujić",
"marko livaja",
"marquinhos",
"marten de roon",
"martin boyle",
"martin braithwaite",
"martin erlić",
"martin hongla",
"martín cáceres",
"mason mount",
"mateo kovačić",
"mateusz wieteska",
"matheus nunes",
"mathew leckie",
"mathew ryan ",
"mathias jensen",
"mathías olivera",
"matt turner",
"matteo guendouzi",
"matthew smith",
"matthias ginter",
"matthijs de ligt",
"matty cash",
"matías vecino",
"matías viña",
"maxi gómez",
"maya yoshida ",
"mehdi taremi",
"mehdi torabi",
"memphis depay",
"meshaal barsham",
"michael estrada",
"michał skóraś",
"michel aebischer",
"michy batshuayi",
"miki yamane",
"mikkel damsgaard",
"milad mohammadi",
"milan borjan",
"miloš degenek",
"miloš veljković",
"mislav oršić",
"mitchell duke",
"mohamed ali ben romdhane",
"mohamed dräger",
"mohamed kanno",
"mohammed al-breik",
"mohammed al-owais",
"mohammed al-rubaie",
"mohammed kudus",
"mohammed muntari",
"mohammed salisu",
"mohammed waad",
"moisés caicedo",
"moisés ramírez",
"montassar talbi",
"morteza pouraliganji",
"mostafa meshaal",
"mouez hassen",
"moumi ngamaleu",
"moustapha name",
"munir mohamedi",
"musab kheder",
"na sang-ho",
"nader ghandri",
"nahuel molina",
"naif al-hadhrami",
"nampalys mendy",
"nasser al-dawsari",
"nathan aké",
"nathaniel atkinson",
"nawaf al-abed",
"nawaf al-aqidi",
"nayef aguerd",
"naïm sliti",
"neco williams",
"nemanja gudelj",
"nemanja maksimović",
"nemanja radonjić",
"neymar",
"nick pope",
"niclas füllkrug",
"nico elvedi",
"nico schlotterbeck",
"nico williams",
"nicola zalewski",
"nicolas jackson",
"nicolas nkoulou",
"nicolás otamendi",
"nicolás tagliafico",
"nicolás de la cruz",
"niklas süle",
"nikola milenković",
"nikola vlašić",
"noa lang",
"noah okafor",
"nouhou tolo",
"noussair mazraoui",
"nuno mendes",
"néstor araujo",
"oliver christensen",
"olivier giroud",
"olivier mbaizo",
"olivier ntcham",
"orbelín pineda",
"osman bukari",
"otávio",
"ousmane dembélé",
"pablo sarabia",
"paik seung-ho",
"pape abou cissé",
"pape gueye",
"pape matar sarr",
"papu gómez",
"pathé ciss",
"patrick sequeira",
"pau torres",
"paulo dybala",
"payam niazmand",
"pedri",
"pedro",
"pepe",
"pervis estupiñán",
"phil foden",
"philipp köhn",
"piero hincapié",
"pierre kunde",
"pierre-emile højbjerg",
"piotr zieliński",
"predrag rajković",
"przemysław frankowski",
"rafael leão",
"raheem sterling",
"ramin rezaeian",
"randal kolo muani",
"raphaël guerreiro",
"raphaël varane",
"raphinha",
"rasmus kristensen",
"raúl jiménez",
"remko pasveer",
"remo freuler",
"renato steffen",
"ricardo horta",
"ricardo rodriguez",
"richarlison",
"richie laryea",
"riley mcgree",
"ritsu dōan",
"riyadh sharahili",
"roan wilson",
"robert arboleda",
"robert gumny",
"robert lewandowski ",
"robert skov",
"robert sánchez",
"roberto alvarado",
"rodolfo cota",
"rodri",
"rodrigo bentancur",
"rodrigo de paul",
"rodrygo",
"rogelio funes mori",
"romain saïss ",
"romario ibarra",
"romelu lukaku",
"ronald araújo",
"rouzbeh cheshmi",
"ruben vargas",
"rubin colwill",
"rui patrício",
"ró-ró",
"rónald matarrita",
"rúben dias",
"rúben neves",
"saad al-sheeb",
"sadegh moharrami",
"sadio mané",
"saeid ezatolahi",
"saleh al-shehri",
"salem al-dawsari",
"salem al-hajri",
"salis abdul samed",
"salman al-faraj ",
"sam adekugbe",
"saman ghoddos",
"sami al-najei",
"samuel gouet",
"samuel piette",
"sardar azmoun",
"saud abdulhamid",
"saša lukić",
"sean johnson",
"sebas méndez",
"sebastian szymański",
"sebastián coates",
"sebastián sosa",
"seifeddine jaziri",
"selim amallah",
"seny dieng",
"serge gnabry",
"sergej milinković-savić",
"sergio busquets ",
"sergio rochet",
"sergiño dest",
"shaq moore",
"shogo taniguchi",
"shojae khalilzadeh",
"shuto machino",
"shūichi gonda",
"silvan widmer",
"simon kjær ",
"simon mignolet",
"simon ngapandouetnbu",
"sofiane boufal",
"sofyan amrabat",
"son heung-min ",
"son jun-ho",
"song bum-keun",
"song min-kyu",
"sorba thomas",
"souaibou marou",
"srđan babić",
"stefan mitrović",
"stefan de vrij",
"stephen eustáquio",
"steve mandanda",
"steven berghuis",
"steven bergwijn",
"steven vitória",
"strahinja eraković",
"strahinja pavlović",
"sultan al-ghannam",
"szymon żurkowski",
"taha yassine khenissi",
"tajon buchanan",
"takefusa kubo",
"takehiro tomiyasu",
"takuma asano",
"takumi minamino",
"tarek salman",
"tariq lamptey",
"teun koopmeiners",
"theo hernandez",
"thiago almada",
"thiago silva ",
"thibaut courtois",
"thilo kehrer",
"thomas delaney",
"thomas deng",
"thomas meunier",
"thomas müller",
"thomas partey",
"thorgan hazard",
"tim ream",
"timothy castagne",
"timothy weah",
"toby alderweireld",
"tom lockyer",
"trent alexander-arnold",
"tyler adams",
"tyrell malacia",
"unai simón",
"uriel antuna",
"uroš račić",
"vahid amiri",
"vanja milinković-savić",
"victor nelsson",
"vincent aboubakar ",
"vincent janssen",
"vinícius júnior",
"virgil van dijk ",
"vitinha",
"wahbi khazri",
"wajdi kechrida",
"walid cheddira",
"walker zimmerman",
"wataru endo",
"wayne hennessey",
"weston mckennie",
"weverton",
"william carvalho",
"william pacho",
"william saliba",
"wojciech szczęsny",
"wout faes",
"wout weghorst",
"xavi simons",
"xavier arreaga",
"xherdan shaqiri",
"yahia attiyat allah",
"yahya jabrane",
"yann sommer",
"yannick carrasco",
"yasser al-shahrani",
"yassine bounou",
"yassine meriah",
"yeltsin tejeda",
"yeremy pino",
"yoon jong-gyu",
"youri tielemans",
"yousef hassan",
"youssef en-nesyri",
"youssef msakni ",
"youssouf fofana",
"youssouf sabaly",
"youssoufa moukoko",
"youstin salas",
"yuki soma",
"yunus musah",
"yussuf poulsen",
"yuto nagatomo",
"zakaria aboukhlal",
"zeno debast",
"álvaro morata",
"álvaro zamora",
"ángel correa",
"ángel di maría",
"ángel mena",
"ángelo preciado",
"éder militão",
"édouard mendy",
"érick gutiérrez",
"éverton ribeiro",
"óscar duarte",
"i̇lkay gündoğan",
"łukasz skorupski"
] |
ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL
|
# Model Card for AI Image Classification - Midjourney V6 & SDXL
## Model Details
### Model Description
This model is a **Swin Transformer-based classifier** designed to distinguish between **AI-generated** and **human-created** images, specifically focusing on outputs from **Midjourney V6** and **Stable Diffusion XL (SDXL)**. It has been trained on a curated dataset of AI-generated images.
- **Developed by:** Deepankar Sharma
- **Model type:** Image Classification (Swin Transformer)
- **Finetuned from model:** SwinForImageClassification
### Model Sources
- **Repository:** [Hugging Face Model Repository](https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL)
## Uses
### Direct Use
This model can be used for **detecting AI-generated images** from Midjourney V6 and SDXL. It is useful for content moderation, fact-checking, and detecting synthetic media.
### Out-of-Scope Use
- The model is **not designed** for detecting AI-generated images from all generative models.
- It **may not perform well** on heavily edited AI-generated images or images mixed with human elements.
- It is **not intended for forensic-level deepfake detection**.
## Bias, Risks, and Limitations
This model is trained specifically on **Midjourney V6** and **Stable Diffusion XL** datasets. It may not generalize well to images generated by other AI models. Additionally, biases in the dataset could lead to **false positives** (flagging real images as AI-generated) or **false negatives** (failing to detect AI-generated content).
### Recommendations
Users should verify results with additional tools and **not solely rely on this model** for high-stakes decisions. Model performance should be tested on domain-specific datasets before deployment.
## How to Get Started with the Model
You can use this model with the 🤗 Transformers library:
```python
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch
# Load model and feature extractor
model_name = "ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
# Load and preprocess image
image = Image.open("path_to_image.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_label = logits.argmax(-1).item()
# Label Mapping
id2label = {0: "ai_gen", 1: "human"}
print("Predicted label:", id2label[predicted_label])
```
## Training Details
### Training Data
The model was trained on the following datasets:
- [ImageClassificationStableDiffusion_small](https://huggingface.co/datasets/ideepankarsharma2003/ImageClassificationStableDiffusion_small)
- [Midjourney_v6_Classification_small_shuffled](https://huggingface.co/datasets/ideepankarsharma2003/Midjourney_v6_Classification_small_shuffled)
- [AIGeneratedImages_Midjourney](https://huggingface.co/datasets/ideepankarsharma2003/AIGeneratedImages_Midjourney)
### Training Procedure
- **Image Size:** 224x224
- **Patch Size:** 4
- **Embedding Dimension:** 128
- **Layers:** 4
- **Attention Heads per Stage:** [4, 8, 16, 32]
- **Dropout Rates:**
- Attention: 0.0
- Hidden: 0.0
- Drop Path: 0.1
- **Activation Function:** GeLU
- **Optimizer:** AdamW
- **Learning Rate Scheduler:** Cosine Annealing
- **Precision:** float32
- **Training Steps:** 3414
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was evaluated on a separate validation split from the training datasets.
#### Metrics
- **Accuracy**
- **Precision & Recall**
- **F1 Score**
### Summary
The model effectively distinguishes between AI-generated and human-created images, but its performance may be affected by dataset biases and out-of-distribution examples.
## Citation
If you use this model, please cite:
```bibtex
@misc{ai_image_classification,
author = {Deepankar Sharma},
title = {AI Image Classification - Midjourney V6 & SDXL},
year = {2024},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL}}
}
```
## Model Card Authors
- **Author:** Deepankar Sharma
---
|
[
"ai_gen",
"human"
] |
VishalMishraTss/deit-base-patch16-224-finetuned-ind-14-imbalanced-pan-10847-train
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-base-patch16-224-finetuned-ind-14-imbalanced-pan-10847-train
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4660
- Accuracy: 0.8703
- Recall: 0.8703
- F1: 0.8412
- Precision: 0.8253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7292 | 0.99 | 43 | 0.6759 | 0.7925 | 0.7925 | 0.7582 | 0.7420 |
| 0.5224 | 2.0 | 87 | 0.5146 | 0.8501 | 0.8501 | 0.8228 | 0.8057 |
| 0.5103 | 2.97 | 129 | 0.4916 | 0.8674 | 0.8674 | 0.8391 | 0.8244 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"aadhaarback",
"aadhaarfront",
"passportregular",
"votercardback",
"votercardfront",
"votercardregular",
"aadhaarregular",
"aadhaarselfgenerated",
"drivinglicensenew",
"drivinglicenseold",
"multipleovds",
"pancard",
"passportfirst",
"passportlast"
] |
Diginsa/Plant-Disease-Detection-Project
|
# MobileNet V2
MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet).
Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md):
> MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier and **224** is the resolution of the input images the model was trained on.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224")
model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224")
inputs = preprocessor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0).
Currently, both the feature extractor and model support PyTorch.
### BibTeX entry and citation info
```bibtex
@inproceedings{mobilenetv22018,
title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
booktitle={CVPR},
year={2018}
}
```
|
[
"apple apple scab",
"apple black rot",
"apple cedar apple rust",
"apple healthy",
"blueberry healthy",
"cherry (including sour) powdery mildew",
"cherry (including sour) healthy",
"corn (maize) cercospora leaf spot gray leaf spot",
"corn (maize) common rust ",
"corn (maize) northern leaf blight",
"corn (maize) healthy",
"grape black rot",
"grape esca (black measles)",
"grape leaf blight (isariopsis leaf spot)",
"grape healthy",
"orange haunglongbing (citrus greening)",
"peach bacterial spot",
"peach healthy",
"pepper, bell bacterial spot",
"pepper, bell healthy",
"potato early blight",
"potato late blight",
"potato healthy",
"raspberry healthy",
"soybean healthy",
"squash powdery mildew",
"strawberry leaf scorch",
"strawberry healthy",
"tomato bacterial spot",
"tomato early blight",
"tomato late blight",
"tomato leaf mold",
"tomato septoria leaf spot",
"tomato spider mites two-spotted spider mite",
"tomato target spot",
"tomato tomato yellow leaf curl virus",
"tomato tomato mosaic virus",
"tomato healthy"
] |
chethanuk/classify_food_items
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classify_food_items
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5776
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5846 | 0.99 | 62 | 2.5776 | 0.84 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
ideepankarsharma2003/Midjourney_v6_ImageClassifier
|
# Midjourney v6 Image Classification
AI ImageClassifier: Fine-tuned a classifier with 96% accuracy for identifying AI-generated images from
Midjourney V6
|
[
"ai_gen",
"human"
] |
silvering/vit-emotions-classification-fp16
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotions-fp16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3314
- Accuracy: 0.9287
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 50 | 1.7532 | 0.4263 |
| No log | 2.0 | 100 | 1.4569 | 0.535 |
| No log | 3.0 | 150 | 1.3329 | 0.5262 |
| No log | 4.0 | 200 | 1.1306 | 0.6475 |
| No log | 5.0 | 250 | 1.0279 | 0.7275 |
| No log | 6.0 | 300 | 0.8815 | 0.7863 |
| No log | 7.0 | 350 | 0.7592 | 0.8337 |
| No log | 8.0 | 400 | 0.7329 | 0.785 |
| No log | 9.0 | 450 | 0.6043 | 0.875 |
| 1.1234 | 10.0 | 500 | 0.5688 | 0.8612 |
| 1.1234 | 11.0 | 550 | 0.5193 | 0.88 |
| 1.1234 | 12.0 | 600 | 0.4879 | 0.8938 |
| 1.1234 | 13.0 | 650 | 0.4170 | 0.9038 |
| 1.1234 | 14.0 | 700 | 0.4425 | 0.8912 |
| 1.1234 | 15.0 | 750 | 0.4089 | 0.905 |
| 1.1234 | 16.0 | 800 | 0.3781 | 0.9263 |
| 1.1234 | 17.0 | 850 | 0.3431 | 0.9225 |
| 1.1234 | 18.0 | 900 | 0.3388 | 0.93 |
| 1.1234 | 19.0 | 950 | 0.2973 | 0.9475 |
| 0.3972 | 20.0 | 1000 | 0.3314 | 0.9287 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
arpanl/Fine-Tuned_Model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Fine-Tuned_Model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
[
"aeroplane",
"blackboard",
"clouds",
"coins",
"cycles",
"deer",
"desk",
"dogs",
"dogsledge",
"door-frame",
"factory",
"fireman",
"boat",
"firetruck",
"food",
"helicopter",
"horse",
"horsepipe",
"instrument",
"ladder",
"lake",
"landscape",
"machinaries",
"books",
"mountains",
"painting",
"people",
"pole",
"railway",
"river",
"road",
"ship",
"ski",
"sky",
"bridge",
"snow",
"stairs",
"table",
"telephone",
"tree",
"water",
"building",
"car",
"chair",
"children",
"church"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.