model_id
stringlengths 7
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|---|---|---|
shobhits/fm_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. -->
# shobhits/fm_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: 0.2800
- Validation Loss: 0.3851
- Train Accuracy: 0.883
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.2768 | 0.7105 | 0.882 | 0 |
| 0.5938 | 0.4909 | 0.88 | 1 |
| 0.4193 | 0.4286 | 0.896 | 2 |
| 0.3449 | 0.4521 | 0.873 | 3 |
| 0.2800 | 0.3851 | 0.883 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"t - shirt / top",
"trouser",
"pullover",
"dress",
"coat",
"sandal",
"shirt",
"sneaker",
"bag",
"ankle boot"
] |
ppicazo/moon-detector-v5.b
|
<!-- 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. -->
# moon-detector-v5.b
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: 0.0866
- Accuracy: 0.9798
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1338
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0246 | 1.0 | 281 | 0.1017 | 0.9773 |
| 0.0223 | 2.0 | 562 | 0.1100 | 0.9723 |
| 0.061 | 3.0 | 843 | 0.0889 | 0.9798 |
| 0.0143 | 4.0 | 1124 | 0.0878 | 0.9824 |
| 0.01 | 5.0 | 1405 | 0.0866 | 0.9798 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"moon",
"notmoon"
] |
satani/convnext-base-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. -->
# convnext-base-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8160
- Accuracy: 0.5862
## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4118 | 1.0 | 65 | 1.3980 | 0.4483 |
| 0.703 | 2.0 | 130 | 0.9538 | 0.5862 |
| 0.6892 | 3.0 | 195 | 0.8160 | 0.5862 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"long sl",
"long tp",
"short sl",
"short tp"
] |
satani/resnet-18-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. -->
# resnet-18-finetuned-eurosat
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5651
- Accuracy: 0.0
## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9039 | 1.0 | 65 | 3.5209 | 0.0 |
| 0.6023 | 2.0 | 130 | 3.3549 | 0.0 |
| 0.6882 | 3.0 | 195 | 3.5651 | 0.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"long sl",
"long tp",
"short sl",
"short tp"
] |
satani/mobilenet_v1_0.75_192-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. -->
# mobilenet_v1_0.75_192-finetuned-eurosat
This model is a fine-tuned version of [google/mobilenet_v1_0.75_192](https://huggingface.co/google/mobilenet_v1_0.75_192) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3344
- Accuracy: 0.3103
## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3566 | 1.0 | 65 | 1.3639 | 0.3103 |
| 0.9354 | 2.0 | 130 | 1.4389 | 0.2759 |
| 0.8786 | 3.0 | 195 | 1.3344 | 0.3103 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"long sl",
"long tp",
"short sl",
"short tp"
] |
pemujo/vit-base-patch16-224-in21k-gldv2-top-51
|
# Model Card for Model ID
This model is vit-base-patch16-224-in21k fine-tuned with a subset of the 2021 Kaggle Google Landmark Dataset competition, including only the top 51 categories.
The dataset is available as Hugginface dataset on: https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories
- **Developed by:** Pedro Melendez
- **Model type:** Vision transformer
- **Finetuned from model:** vit-base-patch16-224-in21k
### Training Data
Classes with more than 500 images in the 2021 Kaggle Google Landmark competition
https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories
### Results
| | |
|-----------------------:|------:|
| epoch |4 |
|eval_accuracy |0.97411|
|eval_loss |0.11560|
|eval_runtime (secs) |79.0939|
|eval_samples_per_second |115.255|
|eval_steps_per_second |14.413 |
|train_runtime (secs) |4082.92|
|train_samples_per_second|35.722 |
|train_steps_per_second |2.233 |
## Environmental Impact
- **Hardware Type:** Nvidia V100
- **Minutes used:** 68 Minutes
- **Cloud Provider:** Google Cloud
- **Compute Region:** us-central
### Compute Infrastructure
Google Cloud Workbench Instance
#### Hardware
GCP Workbench n1-highmem-8 instance with Nvidia V100 GPU
#### Software
Python 3.9
Pytorch 2.0.1+cu117
|
[
"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"
] |
AdamCod/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. -->
# AdamCod/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: 1.0298
- Validation Loss: 0.7463
- Train Accuracy: 0.855
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.5725 | 2.0070 | 0.654 | 0 |
| 1.8037 | 1.3445 | 0.813 | 1 |
| 1.4419 | 1.0304 | 0.829 | 2 |
| 1.2025 | 0.8695 | 0.842 | 3 |
| 1.0298 | 0.7463 | 0.855 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
AdamCod/indian_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. -->
# indian_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.
## 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 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
loucad/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: 0.0797
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.284 | 1.0 | 190 | 0.1307 | 0.9559 |
| 0.1839 | 2.0 | 380 | 0.1056 | 0.9681 |
| 0.1339 | 3.0 | 570 | 0.0797 | 0.9752 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.1
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v17_4e
|
<!-- 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-v17_4e
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0339
- Accuracy: 0.9885
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2256 | 1.0 | 190 | 0.0818 | 0.9730 |
| 0.1295 | 2.0 | 380 | 0.0581 | 0.98 |
| 0.0901 | 3.0 | 570 | 0.0454 | 0.9867 |
| 0.0666 | 4.0 | 760 | 0.0339 | 0.9885 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
jroberts/my_awesome_pokemon_model_resnet18
|
<!-- 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_pokemon_model_resnet18
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8019
- Accuracy: 0.0108
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.275 | 1.0 | 76 | 6.1680 | 0.0014 |
| 3.3896 | 1.99 | 152 | 6.6421 | 0.0115 |
| 3.0563 | 2.99 | 228 | 6.8019 | 0.0108 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"golbat",
"machoke",
"raichu",
"dragonite",
"fearow",
"slowpoke",
"weezing",
"beedrill",
"weedle",
"cloyster",
"vaporeon",
"gyarados",
"golduck",
"zapdos",
"machamp",
"hitmonlee",
"primeape",
"cubone",
"sandslash",
"scyther",
"haunter",
"metapod",
"tentacruel",
"aerodactyl",
"raticate",
"kabutops",
"ninetales",
"zubat",
"rhydon",
"mew",
"pinsir",
"ditto",
"victreebel",
"omanyte",
"horsea",
"magnemite",
"pikachu",
"blastoise",
"venomoth",
"charizard",
"seadra",
"muk",
"spearow",
"bulbasaur",
"bellsprout",
"electrode",
"ivysaur",
"gloom",
"poliwhirl",
"flareon",
"seaking",
"hypno",
"wartortle",
"mankey",
"tentacool",
"exeggcute",
"meowth",
"growlithe",
"tangela",
"drowzee",
"rapidash",
"venonat",
"omastar",
"pidgeot",
"nidorino",
"porygon",
"lickitung",
"rattata",
"machop",
"charmeleon",
"slowbro",
"parasect",
"eevee",
"diglett",
"starmie",
"staryu",
"psyduck",
"dragonair",
"magikarp",
"vileplume",
"marowak",
"pidgeotto",
"shellder",
"mewtwo",
"lapras",
"farfetchd",
"kingler",
"seel",
"kakuna",
"doduo",
"electabuzz",
"charmander",
"rhyhorn",
"tauros",
"dugtrio",
"kabuto",
"poliwrath",
"gengar",
"exeggutor",
"dewgong",
"jigglypuff",
"geodude",
"kadabra",
"nidorina",
"sandshrew",
"grimer",
"persian",
"mrmime",
"pidgey",
"koffing",
"ekans",
"alolan sandslash",
"venusaur",
"snorlax",
"paras",
"jynx",
"chansey",
"weepinbell",
"hitmonchan",
"gastly",
"kangaskhan",
"oddish",
"wigglytuff",
"graveler",
"arcanine",
"clefairy",
"articuno",
"poliwag",
"golem",
"abra",
"squirtle",
"voltorb",
"ponyta",
"moltres",
"nidoqueen",
"magmar",
"onix",
"vulpix",
"butterfree",
"dodrio",
"krabby",
"arbok",
"clefable",
"goldeen",
"magneton",
"dratini",
"caterpie",
"jolteon",
"nidoking",
"alakazam"
] |
platzi/platzi-vit-model-miguel-uicab
|
<!-- 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-miguel-uicab
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0395
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1414 | 3.85 | 500 | 0.0395 | 0.9850 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
miguel-uicab/vit-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. -->
# vit-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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0036
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0572 | 3.85 | 500 | 0.0036 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
pphildan/vit-base-patch16-224-v20
|
<!-- 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-v20
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0823
- Accuracy: 0.9741
## 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: 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1733 | 1.0 | 190 | 0.2635 | 0.9185 |
| 0.1673 | 2.0 | 380 | 0.1489 | 0.9544 |
| 0.1224 | 3.0 | 570 | 0.1266 | 0.9552 |
| 0.0599 | 4.0 | 760 | 0.1134 | 0.9626 |
| 0.0322 | 5.0 | 950 | 0.1289 | 0.9581 |
| 0.0358 | 6.0 | 1140 | 0.0737 | 0.9756 |
| 0.0156 | 7.0 | 1330 | 0.0823 | 0.9741 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v21
|
<!-- 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-v21
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0347
- Accuracy: 0.99
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2951 | 1.0 | 190 | 0.1895 | 0.9333 |
| 0.2013 | 2.0 | 380 | 0.0813 | 0.9726 |
| 0.1935 | 3.0 | 570 | 0.0902 | 0.9681 |
| 0.1858 | 4.0 | 760 | 0.0916 | 0.9674 |
| 0.118 | 5.0 | 950 | 0.0481 | 0.9833 |
| 0.0834 | 6.0 | 1140 | 0.0578 | 0.9841 |
| 0.0986 | 7.0 | 1330 | 0.0418 | 0.9870 |
| 0.0737 | 8.0 | 1520 | 0.0347 | 0.99 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v22
|
<!-- 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-v22
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0399
- Accuracy: 0.9856
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.282 | 1.0 | 190 | 0.1316 | 0.9530 |
| 0.1866 | 2.0 | 380 | 0.1104 | 0.9644 |
| 0.1409 | 3.0 | 570 | 0.0662 | 0.9781 |
| 0.1085 | 4.0 | 760 | 0.0515 | 0.9826 |
| 0.0655 | 5.0 | 950 | 0.0399 | 0.9856 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v23
|
<!-- 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-v23
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0692
- Accuracy: 0.9770
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1353 | 1.0 | 190 | 0.1414 | 0.9511 |
| 0.0729 | 2.0 | 380 | 0.0934 | 0.9685 |
| 0.0325 | 3.0 | 570 | 0.0692 | 0.9770 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
hamouLmr/vit-vase-patch16-244-fowers
|
<!-- 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-vase-patch16-244-fowers
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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
pphildan/vit-base-patch16-224-v24
|
<!-- 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-v24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0840
- Accuracy: 0.9707
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1694 | 1.0 | 190 | 0.1142 | 0.9607 |
| 0.111 | 2.0 | 380 | 0.1172 | 0.9589 |
| 0.0558 | 3.0 | 570 | 0.1200 | 0.9596 |
| 0.0299 | 4.0 | 760 | 0.0840 | 0.9707 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v25
|
<!-- 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-v25
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1940
- Accuracy: 0.9370
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2144 | 1.0 | 190 | 0.3728 | 0.8770 |
| 0.1232 | 2.0 | 380 | 0.2233 | 0.9274 |
| 0.0706 | 3.0 | 570 | 0.3211 | 0.8985 |
| 0.0261 | 4.0 | 760 | 0.1940 | 0.9370 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
maazie/EfficientNetB0
|
This is a EfficientNetB0 model, trained on the ImageNet1k Dataset.
|
[
"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"
] |
Govindaramani/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. -->
# Govindaramani/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: 0.3746
- Validation Loss: 0.3760
- Train Accuracy: 0.899
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7487 | 1.6239 | 0.828 | 0 |
| 1.2189 | 0.8179 | 0.889 | 1 |
| 0.6889 | 0.5344 | 0.906 | 2 |
| 0.4833 | 0.5014 | 0.878 | 3 |
| 0.3746 | 0.3760 | 0.899 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- 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"
] |
JesusPorto/Demeter
|
<!-- 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. -->
# JesusPorto/Demeter
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: 0.3811
- Validation Loss: 0.4459
- Train Accuracy: 0.825
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 800, '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6660 | 0.6422 | 0.825 | 0 |
| 0.5984 | 0.5850 | 0.825 | 1 |
| 0.5212 | 0.5331 | 0.8 | 2 |
| 0.4462 | 0.4799 | 0.85 | 3 |
| 0.3811 | 0.4459 | 0.825 | 4 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"cilantro",
"perejil"
] |
pphildan/vit-base-patch16-224-v26
|
<!-- 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-v26
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0689
- Accuracy: 0.9741
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1415 | 1.0 | 190 | 0.1077 | 0.9619 |
| 0.0684 | 2.0 | 380 | 0.1143 | 0.9611 |
| 0.0435 | 3.0 | 570 | 0.0689 | 0.9741 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
pphildan/vit-base-patch16-224-v28
|
<!-- 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-v28
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9822
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.153 | 1.0 | 190 | 0.1216 | 0.96 |
| 0.086 | 2.0 | 380 | 0.0668 | 0.9770 |
| 0.0298 | 3.0 | 570 | 0.0515 | 0.9822 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
aalonso-developer/vit-base-clothing-leafs-example
|
<!-- 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-clothing-leafs-example
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1420
- Accuracy: 0.0448
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 8.6059 | 0.14 | 1000 | 8.5844 | 0.0002 |
| 8.5506 | 0.28 | 2000 | 8.5189 | 0.0010 |
| 8.4931 | 0.41 | 3000 | 8.4641 | 0.0012 |
| 8.4223 | 0.55 | 4000 | 8.3495 | 0.0016 |
| 8.3144 | 0.69 | 5000 | 8.2552 | 0.0021 |
| 8.1936 | 0.83 | 6000 | 8.1385 | 0.0024 |
| 8.0638 | 0.97 | 7000 | 7.9924 | 0.0028 |
| 7.8485 | 1.11 | 8000 | 7.8366 | 0.0036 |
| 7.6933 | 1.24 | 9000 | 7.6595 | 0.0045 |
| 7.5808 | 1.38 | 10000 | 7.5232 | 0.0062 |
| 7.4352 | 1.52 | 11000 | 7.3816 | 0.0070 |
| 7.3279 | 1.66 | 12000 | 7.2853 | 0.0084 |
| 7.2141 | 1.8 | 13000 | 7.1553 | 0.0105 |
| 7.151 | 1.94 | 14000 | 7.0853 | 0.0119 |
| 6.9695 | 2.07 | 15000 | 7.0088 | 0.0134 |
| 6.8563 | 2.21 | 16000 | 6.9409 | 0.0139 |
| 6.8019 | 2.35 | 17000 | 6.8634 | 0.0158 |
| 6.7372 | 2.49 | 18000 | 6.8001 | 0.0175 |
| 6.6903 | 2.63 | 19000 | 6.7323 | 0.0191 |
| 6.6482 | 2.77 | 20000 | 6.6638 | 0.0207 |
| 6.5669 | 2.9 | 21000 | 6.6090 | 0.0239 |
| 6.4484 | 3.04 | 22000 | 6.5441 | 0.0240 |
| 6.2568 | 3.18 | 23000 | 6.5015 | 0.0273 |
| 6.2452 | 3.32 | 24000 | 6.4589 | 0.0304 |
| 6.2002 | 3.46 | 25000 | 6.4312 | 0.0310 |
| 6.1699 | 3.6 | 26000 | 6.3723 | 0.0319 |
| 6.1284 | 3.73 | 27000 | 6.3324 | 0.0343 |
| 6.1186 | 3.87 | 28000 | 6.3029 | 0.0350 |
| 6.0611 | 4.01 | 29000 | 6.2723 | 0.0381 |
| 5.7883 | 4.15 | 30000 | 6.2527 | 0.0383 |
| 5.7684 | 4.29 | 31000 | 6.2186 | 0.0392 |
| 5.7701 | 4.43 | 32000 | 6.2031 | 0.0403 |
| 5.7473 | 4.56 | 33000 | 6.1777 | 0.0430 |
| 5.735 | 4.7 | 34000 | 6.1634 | 0.0442 |
| 5.7324 | 4.84 | 35000 | 6.1494 | 0.0443 |
| 5.6949 | 4.98 | 36000 | 6.1420 | 0.0448 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"embroidered_floral_shorts",
"paneled_drawstring_chinos",
"boxy_basket_woven_top",
"striped_stretch-knit_dress",
"classic_faded_jeggings",
"young_romance_sweater",
"paisley_print_maxi_skirt",
"striped_metallic-woven_sweater",
"distressed_denim_cutoffs",
"daisy_dot_denim_shorts",
"surplice_lace_dress",
"round_collar_chiffon_blouse",
"embroidered_mesh_cardigan",
"acid_wash_jeans",
"pleated_windowpane_skirt",
"drawstring_denim_joggers",
"marled_purl-stitched_sweater",
"cutout_purl_knit_sweater",
"strapless_bodycon_dress",
"curved-hem_tank",
"tie-back_cross_print_dress",
"daring_mesh-trimmed_bodycon_dress",
"ikat_print_chiffon_dress",
"t-back_high-slit_dress",
"floral_print_babydoll_dress",
"striped_strapless_jumpsuit",
"striped_slub_knit_tank",
"city_of_ny_tank",
"chiffon_&_floral_dress",
"v-cut_bodycon_dress",
"boxy_colorblocked_tee",
"destroyed_cropped_jeans",
"waffle-sleeve_raglan_sweater",
"paneled_faux_leather_bomber_jacket",
"sleeveless_combo_jumpsuit",
"embroidered_diamond_print_dress",
"checked_fit_&_flare_dress",
"open-front_cardigan",
"acid_wash_denim_shorts",
"belted_crochet-paneled_peasant_dress",
"abstract_grid-patterned_top",
"faded_denim_cutoffs",
"brooklyn_nets_logo_sweatpants",
"lace_v-back_dress",
"faux_fur_trimmed_moto_jacket",
"high-slit_maxi_skirt",
"mesh-paneled_bodycon_dress",
"cool-girl_biker_coat",
"embroidered_floral_a-line_dress",
"abstract_paisley_boxy_top",
"ornate_paisley_maxi_kimono",
"paisley-embroidered_a-line_dress",
"slub_knit_racerback_tank",
"textured_crepe_maxi_dress",
"open-front_jersey_cardigan",
"stretch_knit_tank",
"cropped_stripe_sweater",
"southwestern-patterned_bodycon_dress",
"flower_power_travel_poncho",
"marled_knit_combo_jacket",
"abstract_floral_fringe_crop_top",
"woven_sheath_dress",
"diamond-stitched_shorts",
"faux_fur-lined_parka",
"sheer-sleeved_pleated_romper",
"abstract_arrow_flounce_romper",
"mesh_tank_dress",
"netted_mesh_drawstring_shorts",
"embroidered_floral_print_kimono",
"cutout_back_tank",
"love_yourself_muscle_tee",
"lace_swing_tank",
"clean_wash_-_slim_fit_jeans",
"floral_crochet_tank",
"bleached_low-rise_skinny_jeans",
"classic_knit_sweater",
"lacy_pintucked_pj_shorts",
"high-low_longline_tee",
"marled_twist-front_blouse",
"clean_wash_denim_joggers",
"diamond_print_skater_dress",
"beaded_sheer_dress",
"scuba_knit_bomber_jacket",
"between_right_longline_tee",
"life_in_progress_striped_combo_romper",
"tie-dye_sweatshorts",
"zip_collar_bomber",
"crinkled_stretch_knit_blouse",
"ripped_&_frayed_denim_cutoffs",
"pleated_open-front_cardigan",
"metallic_floral_print_cami_dress",
"loop_knit_open-front_cardigan",
"split-neck_dolman_blouse",
"splattered_stripe_maxi_dress",
"mineral_wash_athletic-ribbed_joggers",
"bead-embroidered_chiffon_top",
"longline_muscle_tee",
"striped_cami_jumpsuit",
"faux_gem_woven_cardigan",
"strapless_chiffon_maxi_dress",
"lace-edged_cami_romper",
"tribal_patterned_cardigan",
"stripe_burnout_workout_tank",
"leopard_print_skater_skirt",
"nyc_la_sweatshorts",
"definition_of_love_tee",
"mineral_wash_overall_shorts",
"dotted_french_terry_shorts",
"metallic_maxi_dress",
"striped_boat_neck_sweater",
"multi-striped_maxi_dress",
"open-crochet_drawstring_shorts",
"sunburst_printed_athletic_leggings",
"watercolor_floral_print_blouse",
"flat-front_chinos",
"abstract_paisley_button-down_top",
"distressed_acid-wash_denim_shorts",
"elephant_graphic_burnout_tank",
"abstract_dotted_dress",
"marled_side-slit_maxi_dress",
"crocheted_drop_waist_dress",
"french_terry_drawstring_shorts",
"delicate_details_lace_romper",
"tribal_print_bodycon_dress",
"paneled_faux_leather_jacket",
"marled_ombré_sweater",
"favorite_relaxed_fit_tank",
"floral-embroidered_bomber_jacket",
"southwestern-patterned_brocade_shorts",
"southwestern-embroidered_kimono",
"mineral_wash_skinny_jeans",
"striped_southwestern_print_tank",
"textured_collarless_jacket",
"embroidered_sheath_dress",
"heathered_knit_cocoon_jacket",
"polka_dot_chiffon_dress",
"southwestern_print_a-line_dress",
"floral_print_crochet_maxi_dress",
"raw-edged_smock_dress",
"zippered_longline_sweater",
"eptm._geo-paneled_joggers",
"terry_keyhole_back_dress",
"flat-front_skinny_jeans",
"raw-cut_tank",
"box_pleat_a-line_skirt",
"heathered_cities_graphic_hoodie",
"tasseled_neckline_floral_blouse",
"polka_dot_chambray_button-down",
"pintucked_crochet-paneled_blouse",
"low-rise_-__mineral_wash_jeans",
"cutout-back_shift_dress",
"striped_linen-blend_shorts",
"micro-stripe_v-neck_tee",
"checkered_metallic_dress",
"belted_stripe_pattern_dress",
"mixed_print-pocket_tee",
"chevron_floral_print_top",
"striped_crew_neck_dress",
"beba_coca_cola_graphic_tee",
"contrast_marled_knit_sweater",
"cuffed_tee_shirt_dress",
"embroidered_sheer-woven_shift_dress",
"beaded_chiffon_mini_skirt",
"drawstring_tribal_print_jumpsuit",
"mickey_mouse_pj_shorts",
"eptm._heathered_mesh_panel_tee",
"floral_print_chambray_kimono",
"crocheted_open-front_maxi_cardigan",
"cap-sleeved_shift_dress",
"belted_utility_romper",
"colored_denim_shorts",
"safari_print_drawstring_shorts",
"different_tee",
"belted_abstract_print_jumpsuit",
"embroidered-organza_yoke_dress",
"crochet_crepe_woven_shorts",
"quilted_floral_bomber_jacket",
"floral_crochet-paneled_cardigan",
"boxy_plaid_flannel_top",
"bob_dylan_graphic_tee",
"distressed_cuffed_denim_shorts",
"heathered_baseball_stripe_tee",
"camo_utility_jacket",
"striped_boxy_tank",
"loop_knit_jacket",
"rose_print_babydoll_dress",
"tribal_print_drawstring_joggers",
"cuddled_up_bouclé_cardigan",
"everyday_linen-blend_tee",
"textured_midi_skirt",
"boxy_slub_knit_muscle_tee",
"linen_drawstring_shorts",
"diamond-embroidered_kimono",
"sky_culture_kid_cloud_95ers_tee",
"shirred_sateen_maxi_skirt",
"geo_tribal_print_bodycon_dress",
"long-sleeved_shift_dress",
"must-have_slit_maxi_skirt",
"hooded_maxi_cardigan",
"married_to_the_mob_feminist_tank",
"mock_neck_tee",
"distressed_straight-leg_jeans",
"teardrop_back_burnout_tee",
"windbreaker_utility_jacket",
"distressed_mineral_wash_boyfriend_jeans",
"rustic_plaid_flannel",
"buttoned_babydoll_top",
"textured_high-waisted_shorts",
"cuffed_denim_shorts",
"marled_open-front_coat",
"mixed_stripe_linen-blend_culottes",
"ornate-crocheted_boat_neck_blouse",
"layered_bandana_tee",
"plaid_flannel_shirt_dress",
"surplice_floral_wrap_dress",
"charlie_brown_print_tank",
"cropped_trench_coat",
"sun_graphic_racerback_tank",
"yoga_capri_leggings",
"knotted_jersey_tee",
"distressed_mid-rise_boyfriend_jeans",
"map_graphic_mock_pocket_tee",
"floral_crochet_shift_dress",
"classic_v-neck_cardigan",
"mineral_wash_tee",
"boxy_buttoned_blazer",
"strapless_rose_print_dress",
"beaded_sleeveless_shift_dress",
"favorite_capri_leggings",
"sheer_embroidered_tank",
"tribal_print_scalloped_romper",
"miami_heat_athletic_shorts",
"strapless_diamond_print_dress",
"floral_print_kimono",
"striped_crew_neck_sweater",
"tribal_print_chiffon_dress",
"fuzzy_knit_mini_dress",
"flutter-sleeve_blouse",
"abstract_mosaic_racerback_dress",
"m-slit_knit_maxi_skirt",
"boxy_crepe_raglan_top",
"tribal_print_jumpsuit",
"crepe_woven_cami_dress",
"ribbed_racer_tank",
"embroidered_floral_shift_dress",
"classic_flared_jeans",
"caged_v-neck_maxi_dress",
"striped_geo_print_romper",
"flecked_knit_tank",
"striped_colorblock_tank",
"abstract_paint_splatter_blouse",
"tie-dye_cover-up_skirt",
"dolman_sleeve_hoodie",
"lace-trimmed_fit_&_flare_dress",
"la_love_muscle_tee",
"watercolor_floral_print_shorts",
"striped_knit_sweater",
"faux_shearling_hooded_parka",
"draped_crochet-trimmed_kimono",
"windowpane_floral_crochet_blouse",
"open-front_collarless_blazer",
"faded_boxy_tee",
"abstract_diamond_fringe_kimono",
"draped_collar_blazer",
"cinched-side_denim_cutoffs",
"chunky_knit_sweater",
"contrast-paneled_dolman_blouse",
"dotted_flat-front_shorts",
"rose_print_smocked_shorts",
"street-chic_knit_shorts",
"two-tone_knit_cardigan",
"striped_baja_hoodie",
"faded_skinny_jeans",
"cotton_modal-blend_tank",
"destroyed_acid_wash_denim_jacket",
"night_moves_surplice_dress",
"venice_beach_muscle_tee",
"floral_print_blazer",
"polka_dot_print_tee",
"striped_geo_print_tee",
"slub_knit_cutout_tee",
"lost_&_found_wool-blend_sweater",
"baroque_lace_shorts",
"distressed_splatter_print_tee",
"crochet_woven_blouse",
"basic_athletic_racerback_tank",
"pleated_chiffon_maxi_dress",
"distressed_boyfriend_jeans",
"striped_pleated_skirt",
"embroidered_denim_shorts",
"floral_crochet-paneled_sweater",
"crisscross-back_sheath_dress",
"eptm._speckled_longline_tee",
"crepe_drawstring_joggers",
"pleated_diamond_print_dress",
"crochet-paneled_sweater",
"cami_crochet_romper",
"houndstooth_surplice_dress",
"illusion_fit_&_flare_dress",
"embroidered_mini_skirt",
"quilted_drawstring_sweatshorts",
"classic_oxford_shorts",
"mineral_wash_hooded_dress",
"floral_print_peasant_blouse",
"zip-pocket_woven_shorts",
"cuffed_button-front_denim_shorts",
"tasseled_open-knit_sweater",
"ruffled_crepe_woven_blouse",
"you_post_too_much_tee",
"triangle_print_muscle_tee",
"abstract_print_embroidered_kimono",
"texture-paneled_performance_shorts",
"lace_love_maxi_dress",
"boxy_embroidered_top",
"southwestern_print_cami_romper",
"abstract_geo_print_romper",
"floral_paisley_shift_dress",
"cutout_back_striped_sweater",
"life_in_progress_hooded_denim_jacket",
"southwestern-print_maxi_dress",
"fuzzy_striped_sweater",
"chevron-patterned_batwing_cardigan",
"crochet-trimmed_rose_print_blouse",
"crepe_shift_dress",
"mesh-trimmed_skater_dress",
"d-ring_collar_coat",
"abstract_print_bodycon_dress",
"scuba_knit_mesh-paneled_hoodie",
"embroidered_tribal_print_jumpsuit",
"pleated_metallic_skirt",
"crossback_knit_tank",
"tie-front_chiffon_blouse",
"marled_knit_raglan_sweater",
"lace_crisscross-back_dress",
"paisley_print_halter_romper",
"easy_open-knit_sweater",
"pintucked_high-waisted_shorts",
"textured_woven_shorts",
"stripe_fuzzy_cardigan",
"heathered_drawstring_joggers",
"open-front_coat",
"life_in_progress_drawstring_chambray_shorts",
"fold-over_waist_shorts",
"abstract_frond_print_skirt",
"colorblocked_laser_cut_dress",
"crocheted_shift_dress",
"daisy_print_muscle_tee",
"low-rise_skinny_jeans",
"cutout_bow_blouse",
"pleated_combo_dress",
"london_calling_knit_tee",
"crochet-trimmed_surplice_romper",
"tie-dyed_tank",
"everyday_tank",
"heathered_knit_cardigan",
"dainty_lace_tee",
"garden_party_bow_dress",
"colorblocked-hem_mini_skirt",
"tribal-inspired_open-front_cardigan",
"floral-embroidered_a-line_dress",
"belted_ditsy_floral_romper",
"mid-rise_-_zippered_skinny_jeans",
"floral_lace_overlay_dress",
"ombre-paneled_workout_shorts",
"cutout-back_muscle_tee",
"striped_seersucker_shorts",
"mixed_geo_print_tee",
"medallion_print_romper",
"marled_knee-length_shorts",
"burnout_cutout-back_tank",
"crochet-paneled_chiffon_blouse",
"eagle_graphic_macramé_tank",
"multi-stripe_shirt_dress",
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"striped_cardio_shorts",
"chevron_cutout_back_cardigan",
"distressed_low-rise_cutoffs",
"crocheted_southwestern_print_tank",
"diamond_open-knit_cardigan",
"pleated_jacquard_tea_dress",
"funnel-pocket_drawstring_sweatpants",
"textured_shawl_collar_cardigan",
"dolphin_hem_blouse",
"painted_floral_denim_shorts",
"unstructured_cargo_shorts",
"boxy_knit_sweater",
"cuffed_longline_tee",
"simba_muscle_tee",
"strapless_pleated_chiffon_dress",
"but_not_really_muscle_tee",
"athletic_marled_knit_joggers",
"iconic_v-neck_cardigan",
"tonal_check-patterned_blouse",
"camo_print_shorts",
"high-low_zigzag_dress",
"pleated_chiffon_blouse",
"floral_crochet_dress",
"dropped_lapel_chiffon-sleeved_blazer",
"sheer_moment_sweater",
"tribal_inspired_joggers",
"allergic_2_mornings_tank",
"paisley_print_fit_&_flare_dress",
"batwing_open-front_cardigan",
"open-crochet_maxi_dress",
"buttoned_peasant_top",
"colorblocked_tribal_print_blouse",
"ikat_y-back_maxi_dress",
"floral_print_crochet_hem_shorts",
"everday_v-neck_cardigan",
"knotted_lace_tank",
"flowy_embroidered_midi_dress",
"shakira_graphic_tee",
"knit_open-front_blazer",
"damask_pattern_a-line_dress",
"raga_geo_stripe_hooded_cardigan",
"diamond_print_shorts",
"zippered_satin-trimmed_sweatpants",
"embroidered_shift_dress",
"denim_a-line_skirt",
"clear_cut_high-waisted_shorts",
"mandala_print_maxi_dress",
"abstract_printed_romper",
"rose_print_leggings",
"heathered_knit_tank",
"gorgeous_10_graphic_tee",
"crisscross_back_pj_romper",
"abstract_tile_print_dress",
"flower_girl_crocheted_shorts",
"open-knit_cropped_sweater",
"hooded_purl_knit_sweater",
"fair_isle_crew_neck_sweater",
"floral_flutter_sleeve_blouse",
"chambray_peasant_dress",
"frayed_denim_gauchos",
"ruffled_cami_dress",
"heathered_scuba_knit_bomber_jacket",
"popcorn-textured_pocket_tee",
"drawstring_chambray_shorts",
"flutter_sleeve_surplice_dress",
"mixed_stripe_maxi_dress",
"colorblock_athletic_tank",
"geo_tribal_print_muscle_tee",
"cozy_chunky_knit_sweater",
"kaleidoscope_flounce_maxi_dress",
"classic_split-neck_blouse",
"geo-patterned_lace_fringe_cardigan",
"boyfriend_graphic_tee",
"striped_knit_pullover_hoodie",
"caged_side_longline_dress",
"nautical_stripe_jersey_tee",
"mosaic_print_pocket_tee",
"floral_print_crisscross_pj_romper",
"baroque_floral_surplice_top",
"layered_lace_crew_neck_sweater",
"cloud_wash_denim_shorts",
"zippered_mesh-paneled_jacket",
"marled_knit_cropped_tank",
"textured_knit_hoodie",
"stretch_knit_stripe_shorts",
"piqué_knit_sweatpants",
"marled_knit_drawstring_hoodie",
"rose_print_tee",
"linen_overall_dress",
"knit_crossback_tee",
"clustered_floral_print_skater_skirt",
"faux_leather_zippered_skirt",
"mandala_print_knit_jumpsuit",
"faux_leather_varsity_jacket",
"classic_slub_knit_top",
"paneled_stripe_tank",
"raglan_pocket_tee",
"strapless_jumpsuit",
"life_in_progress_striped_linen-blend_shorts",
"colorblocked_workout_shorts",
"city_of_lights_hoodie",
"eyelash_lace_skirt",
"geo-stripe_maxi_dress",
"faux_leather_flared_skirt",
"rhinestone_beaded_sweater",
"diamond-quilted_sweatshorts",
"marled-stripe_v-neck_cardigan",
"quilted_faux_leather_skirt",
"cami_fit_&_flare_dress",
"mod_moment_sheath_dress",
"basic_tank",
"watercolor_tribal_print_shorts",
"dot-patterned_tank",
"floral_print_surplice_dress",
"striped_floral_drawstring_jumpsuit",
"multi-tone_striped_knit_cardigan",
"flounced_watercolor_print_cami_dress",
"striped_lace-up_cami_romper",
"braided_t-back_tank",
"marled_maxi_cardigan",
"favorite_open-knit_sweater",
"collared_abstract_print_dress",
"washed_denim_trench_coat",
"faux-wrap_surplice_printed_dress",
"smocked_floral_maxi_dress",
"elephant_print_pocket_tank",
"geo_print_strappy_maxi_dress",
"marled_stripe_drawstring_hoodie",
"abstract_print_split-back_top",
"abstract_open-back_romper",
"embellished_tile_print_kimono",
"floral_lace_mini_dress",
"distressed_ghostbusters_muscle_tank",
"strapless_abstract_maxi_dress",
"field_of_daisies_dress",
"textured_crochet_lace_tank",
"heathered_knit_pencil_skirt",
"tartan_plaid_cuffed-sleeve_tee",
"do_not_care_striped_tee",
"striped_cropped_sweater",
"side-slit_knit_sweater",
"boxy_heathered_top",
"star_print_t-shirt_dress",
"colorblocked_chevron_knit_sweater",
"notched_open-front_textured_blazer",
"faux_leather_sheath_dress",
"lace-paneled_pj_shorts",
"fringed_floral_crochet_tank",
"faux_leather_drawstring_joggers",
"pleated_floral_print_blouse",
"paisley_floral_pj_shorts",
"faded_tank",
"spongebob_x_mina_kwon_metallic_graphic_tee",
"abstract_print_dolman_top",
"layered_split-front_blouse",
"v-neck_ribbed_dress",
"semi-sheer_curved_hem_dress",
"rose_print_lace-up_dress",
"classic_slim-leg_jeans",
"varsity-striped_bodycon_dress",
"boxy_faux_fur_jacket",
"crocheted_mandala_print_dress",
"daisy_print_blouse",
"floral_print_knit_tank",
"quilted_faux_leather_jacket",
"southwestern_print_woven_joggers",
"flat_collar_woven_blouse",
"retro_knit_skater_dress",
"paisley_dolman_cardigan",
"watercolor_print_layered_dress",
"tribal_print_shorts",
"raga_floral_print_maxi_dress",
"pintuck_pleated_skirt",
"heart_print_boxy_tee",
"eagles_graphic_muscle_tee",
"bonsoir_striped_hoodie",
"vibrant_rose_strapless_dress",
"colorblocked_cotton_tee",
"favorite_ankle-length_skinny_jeans",
"two-tone_open-front_cardigan",
"meander_wrap_cardigan",
"crochet-embroidered_midi_dress",
"marled_ribbed_maxi_dress",
"chiffon_maxi_dress",
"waffle_knit_turtleneck_sweater",
"strapless_southwestern_print_romper",
"crochet-paneled_collared_blouse",
"contrast_trim_henley",
"zippered_woven_joggers",
"raglan_cable_knit_cardigan",
"clustered_rose_print_jumpsuit",
"raw-cut_pocket_tee",
"athletic_mesh_sports_tank",
"clean_wash_slim-leg_jeans",
"watercolor_print_linen_tee",
"metallic_california_burnout_tank",
"favorite_petite_skinny_jeans",
"stripe_print_belted_dress",
"chiffon-sleeve_mesh_bomber_jacket",
"boyz_ii_men_muscle_tee",
"floral_bodycon_dress",
"textured_fisherman_sweater",
"open-front_hooded_cardigan",
"boxy_rugby_striped_sweater",
"lace_shift_dress",
"crochet-paneled_maxi_dress",
"striped_linen_belted_shorts",
"southwestern-inspired_poncho",
"must-have_scuba_skater_dress",
"abstract_print_buttoned_romper",
"classic_midi_dress",
"soft_pleated_skater_dress",
"striped_open-front_cardigan",
"denim_cutout_dress",
"heart_printed_tie-neck_blouse",
"kangaroo_pocket_sweatpants",
"striped_print_maxi_dress",
"belted_peplum_dress",
"textured_high-neck_blouse",
"open-front_knit_cardigan",
"marled_terrycloth_pj_sweatpants",
"defyant_marble_print_mesh_jersey",
"tribal_print_sweatpants",
"floral_mosaic_print_jumpsuit",
"abstract_print_surplice_top",
"rose_print_halter_romper",
"striped_ikat_drawstring_shorts",
"strapless_knot-front_romper",
"menagerie_print_tee",
"drawstring_waist_trench_coat",
"colorblock_tie-dye_tank",
"floral-embroidered_lace-up_dress",
"longline_crepe_blouse",
"eptm._quilted_shorts",
"boho_muse_embroidered_kimono",
"embroidered_peasant_dress",
"scuba_knit_a-line_dress",
"heathered_skater_dress",
"boxy_ombré_tee",
"abstract_paisley_cuffed_shorts",
"boxy_mesh_lace_top",
"chevron_print_dress",
"cutout_knit_bodycon_dress",
"cuffed_stretch_knit_shorts",
"rolling_stones_muscle_tee",
"meander_patterned_cardigan",
"textured_geo-striped_cuffed_sweatshorts",
"drawstring_waist_dress",
"classic_cotton_jersey_tee",
"bow-front_pintucked_blouse",
"safari_animal_print_tank",
"palm_tree_graphic_dress",
"smocked_floral_print_shorts",
"embroidered_lace_sheath_dress",
"life_in_progress_hooded_utility_jacket",
"pretty_in_grunge_tee",
"layered_flounce_floral_romper",
"v-back_tee",
"posh_flounce_mini_dress",
"la_joie_graphic_heathered_tee",
"marled_knit_sweater_dress",
"ornate_mandala_print_kimono",
"hidden_zippered_placket_blouse",
"matelessé_origami_skirt",
"sunflower_print_jumpsuit",
"geo_print_trimmed_tee",
"space_dye_capri_leggings",
"knit_shift_dress",
"faux_leather_panel_hoodie",
"button-down_denim_dress",
"floral_strappy-back_romper",
"vertical_stripe_tee",
"abstract_plaid_babydoll_dress",
"southwestern_print_racerback_tank",
"silverlake_graphic_tee",
"botanical_print_a-line_dress",
"cropped_open-front_blazer",
"easy_open-knit_cardigan",
"hola_la_muscle_tee",
"rhinestone_embellished_shaggy_sweater",
"denim_bodycon_dress",
"super-soft_skinny_jeans",
"faux_leather_a-line_skirt",
"crepe_fit_&_flare_dress",
"burnout_knit_tee",
"slub_knit_buttoned-back_blouse",
"textured_stripe_cowl_neck_sweater",
"longline_baseball_jersey",
"high-waisted_flared_jeans",
"lynyrd_skynyrd_tank",
"barbie_kiss_hoodie",
"classic_slim_denim_jeans",
"strapless_tribal_print_dress",
"southwestern_print_maxi_skirt",
"cutout_sheath_dress",
"crinkled_gauze_skirt",
"southwestern_print_crochet_romper",
"solid_knit_leggings",
"classic_fold-over_maxi_skirt",
"striped_rose_cami_romper",
"ditsy_floral_a-line_dress",
"square_neck_bodycon_dress",
"tribal_print_cami_dress",
"hooded_windbreaker_utility_jacket",
"i_glow_graphic_tank",
"acid_wash_boyfriend_jeans",
"civil_colorblocked_mesh_tee",
"geo-printed_flat_front_shorts",
"pleated_floral_chiffon_skirt",
"linen-blend_shorts",
"bleached_denim_cutoffs",
"striped_fold-over_maxi_skirt",
"heathered_open-front_cardigan",
"draped_chiffon_wrap_jacket",
"sporty_chic_heathered_sweatpants",
"out_west_knit_tank",
"heathered_stripe-sleeve_tee",
"modernist_shift_dress",
"embroidered_fit_&_flare_dress",
"heathered_t-shirt_dress",
"defyant_marble_print_mesh_shorts",
"semi-sheer_linen_tee",
"bead-embellished_top",
"longline_ribbed_knit_dress",
"pin_dot_trouser_shorts",
"coca-cola_muscle_tee",
"guns_n_roses_muscle_tee",
"ombré_tank",
"longline_hustle_graphic_tee",
"crochet_yoke_maxi_dress",
"haute_nights_tee",
"topstitched_jodhpurs",
"boxy_dotted_tee",
"square_print_kimono",
"polka_dot_t-shirt_dress",
"marled_knit_tee",
"boxy_beaded_crepe_top",
"ny_muscle_tee",
"high-slit_layered_dress",
"faux_shearling_parka",
"crochet-trimmed_crepe_maxi_dress",
"west_coast_graphic_tank",
"chiffon_baroque_print_tank",
"favorite_scoop_neck_tank",
"boxy_pocket_top",
"i_don_bite_tee",
"flounced_metallic_dotted_cami_dress",
"everyday_combo_maxi_dress",
"chenille_&_metallic_knit_crop_top",
"floral_burnout_boxy_tee",
"ny_drawstring_hoodie",
"floral_foulard_print_jumpsuit",
"high-waisted_skinny_jeans",
"boxy_woven_striped_top",
"lace_insert_bodycon_dress",
"striped_halter_maxi_dress",
"sequined_abstract_pattern_dress",
"langley_fox_beach_graphic_tee",
"cami_babydoll_dress",
"leopard_print_joggers",
"floral_print_woven_shorts",
"l&ver_graphic_hoodie",
"strappy-back_pleated_dress",
"abstract_striped_muscle_tee",
"chiffon-paneled_abstract_top",
"colorblocked_surplice_halter_dress",
"rose_print_lace_cutout_dress",
"lace-trimmed_floral_shorts",
"marled_knit_sweater",
"floral_wide-leg_cami_jumpsuit",
"mom_fit_jeans",
"lace_cami_jumpsuit",
"diamond_print_cami_jumpsuit",
"cotton_modal_crew_neck_tee",
"crocheted_chambray_tulip_shorts",
"palm_tree_print_shorts",
"camera_grid_muscle_tee",
"diamond-cutout_maxi_dress",
"gauze_skater_skirt",
"love_me_or_leave_muscle_tee",
"hooded_woven_jacket",
"bejeweled_floral_lace_sheath_dress",
"abstract_pattern_bodycon_dress",
"tropical_print_shorts",
"destroyed_double-button_jeans",
"self-tie_denim_shirt_dress",
"abstract_buttoned_top",
"pom_trim_cutout_dress",
"metallic-threaded_marled_sweater",
"chained_cutout-back_top",
"geo_matelassé_dress",
"baroque_print_tee",
"pleated_floral_chiffon_dress",
"eyelash_lace_a-line_dress",
"marled_loop-knit_cardigan",
"y-back_flounce_dress",
"heathered_crossover-waist_shorts",
"fluted_velvet_skirt",
"beaded_mesh-paneled_dress",
"multi-stripe_open-front_jacket",
"upside-down_eiffel_tower_tee",
"southwestern-patterned_joggers",
"sunglass_girl_graphic_tee",
"v-cut_back_romper",
"classic_baseball_tee",
"heathered_slit-back_tee",
"basic_heathered_tee",
"ornate_floral_print_cardigan",
"embroidered_tunic_dress",
"woven_bermuda_shorts",
"floral_print_tulip-hem_shorts",
"heathered_colorblock_performance_shorts",
"cardio_graphic_sweater",
"globetrotter_skater_dress",
"buttoned_knee-length_skirt",
"faux_leather_combo_dress",
"buttoned_lace_panel_top",
"crochet-trimmed_pj_romper",
"mesh-paneled_sweatpants",
"velveteen_surplice_dress",
"self-tie_knotted_floral_jumpsuit",
"ribbed_knit_sweater",
"checkered_leggings",
"athletic-striped_pencil_skirt",
"be_cool_crop_top",
"striped_lace_dress",
"collarless_popover_blouse",
"tribal_print_peasant_blouse",
"chiffon-paneled_high-slit_top",
"striped_swim_trunks",
"fitted_ribbed-knit_sweater",
"flounced_chiffon_jumpsuit",
"solid_raglan_sweater",
"crepe_woven_surplice_dress",
"open_knit_raglan_sweater",
"lace_print_chiffon_jacket",
"marled_knit_v-neck_tee",
"lace-paneled_fit_&_flare_dress",
"classic_button-down_cardigan",
"paisley_print_romper",
"bell-sleeved_lace_dress",
"boxy_belted_top",
"abstract_grid_print_pleated_shorts",
"textured_sheath_dress",
"high-waisted_rose_print_shorts",
"lace_peplum_sheath_dress",
"twist_cutout_back_dress",
"scallop_lace_pencil_skirt",
"distressed_wash_-_slim_zippered_jeans",
"pleated_baroque_print_shorts",
"purl_knit_cardigan",
"chiffon-paneled_peplum_top",
"textured_knit_dress",
"clean_wash_-_ripped_slim-leg_jeans",
"classic_hooded_duffle_coat",
"blurred_floral_bomber_jacket",
"classic_utility_jumpsuit",
"faux_leather_pencil_skirt",
"striped_floral_shift_dress",
"cutout_sunflower_tank",
"zippered_knit_dress",
"chiffon_halter_maxi_dress",
"velveteen_damask_pattern_kimono",
"twisted_open-back_tee",
"longline_boyfriend_blazer",
"bleach-splatter_oversized_tee",
"open_knit_cardigan",
"metallic_beaded_chiffon_tank",
"crochet_cami_dress",
"25_mesh-paneled_jersey_dress",
"crochet-paneled_a-line_dress",
"pintuck_pleated_chiffon_blouse",
"embroidered_surplice_cami_romper",
"strapless_sweetheart_dress",
"ladder_racerback_maxi_dress",
"flared_jeans",
"marled_tuxedo-stripe_joggers",
"diamond-patterned_two-tone_cardigan",
"bandana_print_sweatpants",
"plaid_midi_dress",
"floral_brocade_a-line_dress",
"prairie-embroidered_peplum_blouse",
"rose_print_cami_dress",
"sleeveless_vented_blouse",
"hooded_puff_parka",
"cinched_training_shorts",
"classic_self-tie_blouse",
"polka_dot_swim_trunks",
"tribal_print_pocket_dress",
"west_east_coast_muscle_tee",
"open-knit_hooded_cardigan",
"the_beatles_graphic_tank",
"diamond_print_chiffon_cardigan",
"tribal_print_pleated_dress",
"distressed_whisker_wash_jeans",
"drop-sleeve_heathered_tee",
"embroidered_drawstring_romper",
"embroidered_floral_pattern_blouse",
"marled_knit_t-shirt_dress",
"arrow_printed_romper",
"crochet-paneled_gauze_romper",
"crochet_fringe_tank",
"rainbow_print_workout_leggings",
"embroidered_gauze_dress",
"boxy_angel-sleeved_blouse",
"contrast-trim_pocket_tee",
"boxy_lace_ruffle_top",
"braided_crew_neck_sweater",
"colorblocked_toujours_la_vôtre_tank",
"southwestern_print_denim_joggers",
"seam-stitched_shift_dress",
"ripped_denim_cutoffs",
"boxy_slit-sleeve_top",
"faux_snakeskin-trimmed_blazer",
"high-waisted_tweed_shorts",
"scalloped_crochet_skirt",
"sheer-paneled_sweater",
"lace-cutout_sheath_dress",
"abstract_geo_stripe_romper",
"faux_fur_jacket",
"mesh_paneled_running_shorts",
"floral_print_surplice_jumpsuit",
"embroidered_southwestern_pattern_shorts",
"textured_knit_tulip_romper",
"hooded_longline_cardigan",
"space_dye_yoga_leggings",
"fuzzy_knit_v-neck_cardigan",
"rose_print_m-slit_dress",
"crinkled_mandala_print_dress",
"boxy_cotton_muscle_tee",
"ruffled_maxi_dress",
"classic_denim_romper",
"civil_regime_tropical_print_jersey",
"longline_striped_cardigan",
"ribbed_knit_cropped_sweater",
"abstract_print_draped_blazer",
"chicago_bulls_faux_leather_top",
"polka_dot_tee",
"collarless_one-button_blazer",
"tonal_striped_skater_dress",
"open-knit_longline_cardigan",
"abstract_tribal_print_halter_jumpsuit",
"civil_varsity-striped_longline_tee",
"west_coast_jersey",
"luxe_voyager_shorts",
"tribal-inspired_chino_shorts",
"flutter_sleeve_blouse",
"striped_fuzzy_knit_sweater",
"varsity-striped_brooklyn_sweatpants",
"life_in_progress_distressed_denim_jacket",
"tropical_print_chino_shorts",
"out_of_bed_tee",
"peter_pan_collar_dress",
"distressed_bleach_wash_cutoffs",
"woven_dolphin_shorts",
"diamond-patterned_peasant_dress",
"ribbed_dolman_cardigan",
"cutout_layered_t-shirt_dress",
"boxy_floral_print_tee",
"triangle_print_tank_dress",
"inverse_leaf_print_tee",
"colorblock_woven_running_shorts",
"lace_print_bodycon_dress",
"striped_t-shirt_dress",
"crochet-paneled_shift_dress",
"floral_wrap-front_maxi_skirt",
"sheer_polka_dot_skirt",
"striped_box-pleated_dress",
"strapless_matelassé_skater_dress",
"metallic_polka_dot_blouse",
"acid_wash_denim_skirt",
"drapey_woven_jacket",
"high_waist-_quilted_moto_jeggings",
"layered_cutout_midi_dress",
"tasseled_fringe_crochet_tank",
"cozy_cropped_sweater",
"mesh_patch-pocket_sweatpants",
"snoopy_graphic_baseball_tee",
"paisley_floral_print_blouse",
"chiffon-paneled_sheath_dress",
"southwestern_burnout_tee",
"floral_&_bird_print_shorts",
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"essential_knit_tank",
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"tropical_print_pocket_tank",
"baseball_muscle_tee",
"don't_be_jealous_muscle_tee",
"tweed_cutaway_jacket",
"floral_tulip-back_marled_sweater",
"cotton_v-neck_tee",
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"quilted_&_hooded_utility_jacket",
"varsity-striped_pocket_tee",
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"contrast-trimmed_sweater",
"cutout_sleeveless_jumpsuit",
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"heathered_high-waisted_leggings",
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"abstract_tile_ladder-back_top",
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"crisscross-back_jumpsuit",
"crochet-paneled_chambray_dress",
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"crochet_trim_tribal_print_shorts",
"knotted_racerback_tank",
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"striped_hot_dog_tee",
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"chiffon-paneled_tee",
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"bandage_bodycon_dress",
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"follow_your_heart_tee",
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"be_true_cutoff_tee",
"destinations_graphic_heathered_skirt",
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"lace-back_henley_tank",
"sunburst_print_swim_trunks",
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"abstract_tile_print_tee",
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"xo_print_crisscross-back_dress",
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"not_your_wifey_hoodie",
"pizza_print_tank",
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"abstract_zigzag_top",
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"striped_ringer_pocket_tee",
"et_graphic_muscle_tee",
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"buttoned_front_tank",
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"mineral_wash_romper",
"classic_pocket_tee",
"tupac_muscle_tee",
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"smart_graphic_muscle_tee",
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"life_in_progress_railroad_stripe_jeans",
"longline_split-back_tee",
"mineral_wash_boxy_tee",
"ribbed_knit_cardigan",
"spongebob_v-neck_tee",
"embroidered_mesh_bomber_jacket",
"love_sleep_boxy_hoodie",
"abstract_print_flounce_maxi_dress",
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"basquiat_graphic_tee",
"slouchy_marled_sweater",
"colorblocked_tee",
"belted_lace_print_dress",
"raw-cut_fringe_tee",
"suspender_button-down_maxi_dress",
"woven_colorblock_romper",
"abstract_print_peasant_dress",
"clashist_oil_slick_print_tee",
"tri-colored_sweater",
"reason_tropical_print_baseball_jersey",
"tribal_print_a-line_dress",
"chiffon_twist-front_top",
"side_pocket_capri_leggings",
"sleek_draped_blouse",
"mesh_scuba_knit_dress",
"chambray_dolphin_shorts",
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"kaleidoscope_print_racerback_dress",
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"crisscross_back_jumpsuit",
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"boxy_open_mesh_top",
"high-waisted_distressed_boyfriend_jeans",
"embroidered_lace-up_poncho",
"western-inspired_surplice_dress",
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"chevron-quilted_sweater",
"so_fine_graphic_tee",
"slub_knit_tee",
"daydreamer_crocheted_dress",
"caged_cutout_dress",
"woven_shift_dress",
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"flower_print_raglan_tee",
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"paneled_a-line_dress",
"hooded_denim_jacket",
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"abstract_plaid_printed_tee",
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"destroyed_tee",
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"dip-dyed_knotted_sweater",
"brooklyn_nets_baseball_tee",
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"giraffe_print_shorts",
"miami_heat_graphic_tee",
"fancy_little_number_tee",
"abstract_print_boxy_top",
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"varsity-striped_ringer_tank",
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"roman_numeral_graphic_tee",
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"colorblocked_track_jacket",
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"clashist_sand_art_shift_dress",
"geo_print_capri_workout_leggings",
"faux_leather_drawstring_shorts",
"abstract-paneled_running_shorts",
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"lace-paneled_blouse",
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"off-the-shoulder_bodycon_dress",
"layered_watercolor_print_burnout_tee",
"smocked_floral_gauze_skirt",
"sheer_moments_cropped_tank",
"tie-dye_hoodie",
"longline_satin_jacket",
"tropical_print_fit_&_flare_dress",
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"loose-knit_sweater",
"zippered_abstract_print_dress",
"nyc_graphic_tee",
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"high-waisted_denim_skirt",
"white_distressed_skinny_jeans",
"tie-dye_muscle_tee",
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"classic_pocket_tank",
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"metallic_brocade_sheath_dress",
"tribal_printed_pocket_tee",
"cutout-back_lace_dress",
"ribbed_cutout_sweater",
"basquiat_crown_tee",
"nacho_friend_muscle_tee",
"leef_paris_lightning_graphic_drawstring_shorts",
"true_love_muscle_tee",
"bejeweled_slit-sleeve_top",
"floral_chiffon_high-neck_dress",
"cute_black_tee",
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"easy_chino_shorts",
"high-slit_tile_print_kimono",
"free_spirit_tee",
"heathered_high-slit_tee_dress",
"paisley_crochet_skirt",
"elephant_graphic_muscle_tee",
"space_dye_shorts",
"abstract_print_lace-paneled_blouse",
"eyelash_lace-paneled_blouse",
"embroidered_peasant_tank",
"heathered_zip-up_hoodie",
"geo_tribal_print_a-line_skirt",
"scalloped_lace_shorts",
"crepe_woven_cami_romper",
"fuzzy_plaid_skirt",
"colorblock_high_performance_shorts",
"cinched_ankle_jeans",
"keyhole_abstract_print_blouse",
"sequined_drawstring_shorts",
"ribbed_turtleneck_sweater_dress",
"beaded_paisley-embroidered_top",
"floral_cami_maxi_dress",
"life_in_progress_wrap_belt_chambray_dress",
"striped_racerback_tank",
"v-cut_fringe_tank",
"lace-paneled_floral_print_dress",
"ruffled_tie-neck_blouse",
"loose-knit_zippered_sweater",
"ruffle-trimmed_blouse",
"kurt_cobain_graphic_tee",
"plaid_flannel_mini_skirt",
"ornate_crochet_back_romper",
"classic_striped_sweater",
"sheer_back_tee",
"pointelle_knit_crop_tank",
"open-front_pocket_kimono",
"striped_boat_neck_tee",
"pleated_a-line_dress",
"fit_&_flare_combo_cami_dress",
"abstract_asymmetrical_hem_top",
"heathered_colorblock_raglan_tee",
"distressed_denim_overall_shorts",
"plaid_drawstring_sweatpants",
"eptm._zip-paneled_drawstring_hoodie",
"woven_pencil_skirt",
"heathered_pocket_tee",
"drawstring_chino_shorts",
"voyager_maxi_skirt",
"embroidered-mesh_drop-waist_cami_dress",
"lace-paneled_tank",
"flatfront_denim_skirt",
"textured_maxi_cardigan",
"forget-me-not_sweater",
"cloud_wash_overall_shorts",
"ornate_print_tank",
"athletic_mesh-paneled_capri_leggings",
"floral_crochet_racerback_romper",
"ornate-embroidered_denim_shorts",
"rose_print_skirt",
"double-breasted_drawstring_parka",
"fit_&_flare_knit_dress",
"side-slit_longline_tee",
"tropical_print_bodycon_dress",
"tropical_print_mini_skirt",
"lace_overlay_dress",
"heathered_babydoll_dress",
"lace_raglan_romper",
"heathered_scuba_knit_shorts",
"cuffed_colorblock_sweatpants",
"boxy_striped_knit_top",
"wide-leg_surplice_jumpsuit",
"polka_dot_matelot_shorts",
"boxy_floral_crochet_top",
"cargo_joggers",
"stop_following_me_tee",
"textured_bow-front_blouse",
"ruffled-sleeve_layered_blouse",
"abstract_diamond_print_jumpsuit",
"tribal_print_strapless_jumpsuit",
"grid_print_jumpsuit",
"floral_lace_a-line_skirt",
"marled_knit_dolman-sleeve_cardigan",
"classic_crocheted_lace_blouse",
"boxy_denim_crop_top",
"striped_seersucker_dress",
"striped_knit_cardigan",
"v-neck_tank",
"pleated_wide-leg_culottes",
"southwestern_pattern_sweater",
"two-button_chino_suit_jacket",
"n.w.a._graphic_tank",
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"striped_turtleneck_dress",
"classic_knit_tee",
"belted_lace_trim_shorts",
"striped_floral_blouse",
"heart_print_denim_shorts",
"los_angeles_pocket_tee",
"classic_cotton_knit_tee",
"caged_halter_top",
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"lady_lace_mini_skirt",
"polka_dot_skinny_jeans",
"sunburst_print_workout_shorts",
"heathered_seamless_workout_tank",
"faded_high-waisted_denim_shorts",
"adios_jersey",
"contrast-paneled_henley",
"mesh_striped_performance_shorts",
"chiffon_surplice_dress",
"varsity-striped_skirt",
"ribbed_button-down_tank",
"kitten_graphic_sweater",
"crochet_cover-up_tank",
"run_graphic_workout_shorts",
"embroidered_panel_cutoffs",
"mesh-paneled_running_shorts",
"cropped_purl_knit_sweater",
"tie-dye_cami_romper",
"open-front_gauze_kimono",
"reason_heathered_ny_baseball_jersey",
"high-slit_ribbed_maxi_dress",
"elephant_print_strapless_romper",
"shirred_peasant_dress",
"striped_bodycon_dress",
"clean_wash_-_skinny_jeans",
"faux_leather_cami_dress",
"femme_fatale_crochet_dress",
"r2-d2_graphic_tank",
"beaded_mini_skirt",
"embroidered_high-neck_blouse",
"eptm._tribal_print_track_jacket",
"fuzzy_polka_dot_cardigan",
"chiffon-paneled_top",
"ornate_surplice_maxi_dress",
"striped_linen_ringer_tee",
"zippered_sweatpants",
"collared_crossover_sweater_coat",
"southwestern-patterned_fringe_cardigan",
"abstract_print_fluted_dress",
"woven_ruffle_cami_jumpsuit",
"chunky_knit_longline_cardigan",
"tribal_print_halter_dress",
"watercolor_floral_kimono",
"classic_cotton_tank",
"botanical_print_shift_dress",
"peter_pan_collar_sweater",
"paint_it_red_valentina_dress",
"self-tie_shift_dress",
"pintucked_lace_button-down_blouse",
"curved_hem_raglan_tee",
"collared_woven_top",
"tribal-inspired_maxi_dress",
"studded_racerback_tank",
"pleated_chiffon_romper",
"longline_loop-knit_cardigan",
"on_the_range_t-shirt_dress",
"eiffel_tower_graphic_linen_tee",
"watercolor_floral_print_blazer",
"striped_henley",
"woodstock_rib-paneled_tank",
"scuba_knit_peplum_jacket",
"faux_mohair_sweater",
"burnout_racerback_tank",
"lace_trim_cami_romper",
"floral_print_trapeze_romper",
"strapless_paisley_dress",
"rose_print_crepe_dress",
"l.a._graphic_tee",
"cutout_shoulder_knit_dress",
"abstract_print_button-shoulder_dress",
"get_cray_muscle_tee",
"contrast_crew_neck_tee",
"popcorn-knit_sweater",
"los_angeles_dreamer_tank",
"buttoned_polka_dot_maxi_skirt",
"ribbed_knit_curved_hem_tee",
"rugby_striped_sweater",
"layered_eyelet_dress",
"paisley_print_chiffon_kimono",
"belted_plaid_shirt_dress",
"stripe-pocket_tank",
"boat_neck_striped_top",
"floral_embroidered_fit_&_flare_dress",
"go_baroque_trapeze_dress",
"rose_print_cardigan",
"faux_leather_trimmed_utility_coat",
"abstract_tile_print_cropped_top",
"chunky_knit_boxy_sweater",
"retro_graphic_tee",
"open-knit_geo_tank",
"heathered_tropical_print_tee",
"corduroy_hooded_parka",
"floral_print_maxi_dress",
"heathered_ribbed_knit_tank",
"crisp_woven_suit_jacket",
"hashtags_graphic_muscle_tee",
"california_66_tie-dye_tank",
"buttoned_collarless_blouse",
"pleated_woven_joggers",
"chevron-striped_maxi_dress",
"ombré_contrast_workout_shorts",
"zippered_sheath_dress",
"abstract_organza_a-line_skirt",
"geo_embellished_beadwork_dress",
"two-tone_sweatshorts",
"abstract_print_peasant_top",
"asymmetric_zipper_cardigan",
"crochet-trimmed_longline_kimono",
"two-pocket_cardigan",
"quilted_dolphin_shorts",
"distressed_light_wash_-_slim_fit_jeans",
"metallic_chevron-patterned_dress",
"classic_slub_jersey_tank",
"eptm._longline_dip-dye_tee",
"cotton_&_linen_pleated_shorts",
"embroidered_fringe-trim_kimono",
"abstract_zigzag_romper",
"heathered_knit_midi_dress",
"lace-paneled_stripe_tank",
"slim-striped_tee",
"curved_hem_ribbed_tank",
"heathered_dolman_cardigan",
"floral_print_tank",
"smocked_drawstring_shorts",
"sleeveless_quilted_skater_dress",
"crepe_zippered_blouse",
"knit_skater_skirt",
"ribbed_knit_flared_dress",
"tribal_print_tee",
"spongebob_x_mina_kwon_patrick_star_nylon_hoodie",
"woven_drawstring_skirt",
"floral_crochet_a-line_dress",
"varsity-striped_baseball_tee",
"classic_collared_button-down",
"sushi_roll_graphic_tee",
"dotted_tulip-sleeve_blouse",
"heathered_joggers",
"cap-sleeved_lace_blouse",
"langley_fox_graphic_muscle_tee",
"linen-blend_cargo_shorts",
"life_in_progress_denim_pencil_skirt",
"floral_stripe_midi_skirt",
"lace-up_striped_baja_hoodie",
"abstract_print_surplice_jumpsuit",
"life_in_progress_linen-blend_drawstring_shorts",
"boxy_floral_print_top",
"reason_statue_of_liberty_mesh_tank",
"hooded_longline_toggle_coat",
"stonewash_-_skinny_jeans",
"life_in_progress_drawstring_denim_jumpsuit",
"heathered_performance_shorts",
"dotted_clean_wash_-_skinny_jeans",
"paris_france_sleeveless_hoodie",
"floral_lace_cutout_dress",
"tonal-patterned_halter_dress",
"floral_print_crepe_kimono",
"classic_leggings",
"draped_pocket_kimono_cardigan",
"mixed_ornate_print_blouse",
"abstract-geo_print_mini_skirt",
"paisley_print_cami_maxi_dress",
"geo_pattern_sweater",
"mineral_wash_denim_drawstring_shorts",
"beaded_tuxedo_stripe_skirt",
"sheer_abstract_print_blouse",
"polka_dot_skater_dress",
"marled_knit_racerback_jumpsuit",
"southwestern-inspired_cardigan",
"longline_chiffon_baseball_jersey",
"hooded_maxi_dress",
"marled_ribbed_knit_hoodie",
"heathered_ribbed-knit_cardigan",
"ladder_cutout_cami_dress",
"cami_crop_top",
"turtleneck_sweater_dress",
"west_coast_mesh_jersey",
"tile_print_halter_maxi_dress",
"ribbed_open-knit_cardigan",
"ditsy_floral-patterned_shorts",
"marled_knit_pleated-back_tank",
"ruffle_drawstring_shorts",
"acid_wash_denim_romper",
"woven_surplice_romper",
"heart_muscle_tee",
"draped-front_sleeveless_romper",
"textured_bodycon_dress",
"diamond_print_pocket_tank",
"classic_double-breasted_trench_coat",
"polka_dot_rose-printed_romper",
"genuine_suede_shorts",
"boxy_cuff-sleeve_top",
"swan_print_pj_shorts",
"crepe_woven_a-line_dress",
"striped_drapey_tee",
"abstract_mandala_print_skirt",
"bold_baja_knit_shorts",
"tulip_sleeve_satin_blouse",
"ribbed_crop_tee",
"tasseled_neckline_stripe_blouse",
"lace-paneled_romper",
"marled_knit_pencil_skirt",
"asymmetrical_twist-front_top",
"floral_print_surplice_romper",
"abstract_diamond_print_dress",
"keith_haring_heart_print_tee",
"abstract_floral_strapless_dress",
"slub_knit_v-neck_tee",
"pleated_plaid_a-line_dress",
"tribal_print_kimono",
"ornate-patterned_dolman_cardigan",
"heathered_side-pocket_performance_leggings",
"open-knit_poncho_sweater",
"pleated_midi_skirt",
"baroque_print_bodycon_dress",
"no_way_baseball_tee",
"ruffled_mineral_wash_cami_dress",
"star-spangled_cropped_tee",
"leef_paris_splatter_print_tee",
"mesh-paneled_colorblock_workout_shorts",
"french_terry_hoodie",
"floral_print_slit_maxi_dress",
"cuffed_mineral_wash_tee",
"colorblocked_southwestern_print_tee",
"tab-sleeved_chiffon_blouse",
"boxy_open-knit_sweater",
"leopard_love_jumpsuit",
"stretch-knit_mini_skirt",
"longline_tulip-back_tee",
"zip-pocket_drawstring_shorts",
"asymmetrical_hem_top",
"crochet-back_tank",
"side_pocket_yoga_leggings",
"lace-paneled_crepe_blouse",
"floral_lace_pencil_skirt",
"belted_stripe_print_shorts",
"crochet-trimmed_crepe_tank",
"seamless_athletic_tank",
"mesh-paneled_fit_&_flare_dress",
"clean_wash_jeggings",
"chiffon-paneled_cutout_top",
"crochet-trimmed_abstract_print_shorts",
"varsity_stripe_a-line_dress",
"marled_metallic_knit_tank",
"distressed_chino_joggers",
"geo_print_romper",
"side-slit_striped_blouse",
"crocheted_tank",
"married_to_the_mob_shaved_my_legs_tank",
"floral-embroidered_mesh_shorts",
"sateen_fit_&_flare_dress",
"athletic_mesh-paneled_track_jacket",
"accordion-pleated_raglan_blouse",
"belted_tribal_print_maxi_dress",
"layered_sleeveless_dress",
"paisley_print_tank",
"clean_wash_bootleg_jeans",
"zip_paneled_skirt",
"bonjour_au_revoir_tee",
"abstract_geo_print_top",
"palm_tree_tee",
"classic_denim_blazer",
"la_longline_baseball_tee",
"beauty_&_brains_muscle_tee",
"lattice_knit_sweater",
"tassel-trimmed_shorts",
"rose_print_corduroy_skater_skirt",
"french_terry_pocket_tee",
"power_brunch_flounce_dress",
"ribbed_knit_sweater_tank",
"crochet-trimmed_chiffon_dress",
"floral_foliage_peasant_dress",
"flower_&_dot_cami_dress",
"but_first_coffee_tank",
"shirred_panel_shorts",
"marled_drop-sleeve_sweater",
"mandala_print_skirt",
"sleek_woven_pocket_blouse",
"cable_knit_fringe_cardigan",
"scarf-print_pencil_skirt",
"l.a.t.h.c._victory_circle_tee",
"v-neck_woven_romper",
"lace-paneled_lattice-back_blouse",
"wide-leg_jumpsuit",
"abstract_splatter_print_dress",
"damask_patterned_mini_dress",
"fitted_v-neck_tee",
"control_sector_striped_cotton-blend_shorts",
"watercolor_floral_print_romper",
"crisscross_bodycon_dress",
"flat_front_textured_shorts",
"hooded_chambray_parka_jacket",
"crochet-back_maxi_dress",
"scoop_neck_slub_knit_tee",
"favorite_strappy_shift_dress",
"civil_zippered_longline_tee",
"shawl_collar_geo-patterned_cardigan",
"heathered_knit_drawstring_jumpsuit",
"ornate_floral_drawstring_shorts",
"lace_paneled_shift_dress",
"sophisticated_jersey_knit_dress",
"seamless_studio_capri_leggings",
"hooded_utility_jacket",
"slit_midi_skirt",
"classic_knit_pencil_skirt",
"basic_scoop_neck_tank",
"marled_knit_slit_tee",
"metallic_knit_tee",
"southwestern_print_muscle_tee",
"rose_print_maxi_dress",
"faux_suede_moto_jacket",
"damask_mesh_bodycon_dress",
"oversized_v-neck_tee",
"tribal_print_linen_shorts",
"mid_rise-_destroyed_straight_leg_ankle_jeans",
"leaf_embroidery_tiered_dress",
"floral_lace_cami_dress",
"sleeveless_snap-button_jumpsuit",
"beaded_chiffon_top",
"collarless_bouclé_coat",
"classic_twill_chinos",
"embroidered_pintucked_blouse",
"draped_v-neck_speckled_tee",
"destroyed_linen_tee",
"baroque-embossed_romper",
"sleeveless_hoodie",
"linen-blend_drawstring_shorts",
"sheer-paneled_printed_dress",
"pink_floyd_crochet_trim_tee",
"classic_woven_top",
"fringed_shawl_cardigan",
"tribal-inspired_print_tank",
"textured_floral_lace_romper",
"draped_chiffon_lace_dress",
"abstract_print_tasseled_mini_dress",
"daisy_print_maxi_skirt",
"belted_open-knit_cardigan",
"high-waisted_denim_shorts",
"belted_floral_print_romper",
"crochet_maxi_cardigan",
"textured_stripe_tank",
"leopard_print_longline_cardigan",
"floral_crochet_cutout_blouse",
"embroidered_faux_gem_shorts",
"embroidered_gauze_belted_dress",
"floral_print_dolman_kimono",
"dream_alive_cuffed-sleeve_tee",
"high-waisted_denim_cutoffs",
"wildflower_print_leggings",
"abstract_tile_print_halter_top",
"flawless_knit_tank",
"distressed_high-waisted_cutoffs",
"abstract_floral_tie-waist_dress",
"seaside_strapless_maxi_dress",
"cutout-mixed_print_dress",
"pink_floyd_knit_tee",
"faded_mid-rise_skinny_jeans",
"shawl_collar_cardigan",
"metallic_geo-patterned_dress",
"faux_leather_shorts",
"glasses_print_tee",
"curved_hem_pleat-back_dress",
"mineral_wash_utility_jacket",
"eyelet_fit_&_flare_dress",
"gathered_floral_lace_dress",
"basic_v-neck_tee",
"pebbled_faux_leather_moto_jacket",
"floral-embroidered_cami_romper",
"braided-strap_tour_graphic_tank",
"buttoned_knit_tank",
"textured_knit_a-line_dress",
"ribbed-trim_sheath_dress",
"tie-dye_crochet_maxi_dress",
"colorblocked_pocket_striped_tee",
"classic_slouchy_sweater",
"split-neck_chiffon_blouse",
"abstract_brushstroke_pocket_top",
"nailed_it_graphic_tee",
"lace-hem_floral_pj_shorts",
"layered_a-line_dress",
"drawstring_cargo_dress",
"crisscross_back_knit_dress",
"crocheted_gauze_maxi_dress",
"french_terry_tee",
"crochet-hem_cami_romper",
"chevron_embroidered_kimono",
"cat_print_crop_top",
"la_ny_graphic_tee",
"acid_washed_denim_skater_skirt",
"floral_print_leggings",
"ribbed_knit_cropped_tank",
"striped_mesh-paneled_capri_leggings",
"single-button_blazer",
"betty_boop_pj_shorts",
"cities_graphic_boxy_tee",
"longline_faux_fur_coat",
"front_slit_pencil_skirt",
"high-waisted_matelot_skinny_jeans",
"embellished_boxy_tee",
"sleek_v-back_dress",
"colorblocked_bomber_jacket",
"ribbed_open-front_cardigan",
"heathered_woven_running_shorts",
"pintucked_rose_print_blouse",
"striped_sailor_shorts",
"eyelash_lace_maxi_dress",
"pleated_skater_skirt",
"southwestern-patterned_tee",
"metallic_open-knit_cardigan",
"embroidered_gauze_layered_dress",
"zippered_open-front_jacket",
"flutter_self-tie_blouse",
"faux_leather-trim_blouse",
"chambray_drawstring_shorts",
"strapless_romper",
"seaside_sweet_tube_dress",
"colorblocked_cable_knit_sweater",
"abstract_geo_print_blouse",
"southwestern_print_cardigan",
"crochet-hemmed_denim_shorts",
"cowl_neck_sweater",
"slouchy_racerback_tank",
"contrast_mesh-paneled_tee",
"collared_chiffon_surplice_top",
"chevron_print_cami_maxi_dress",
"floral_print_robe_kimono",
"slub_knit_t-shirt_dress",
"button-down_maxi_cardigan",
"abstract_print_jumpsuit",
"denim_utility_romper",
"collarless_open-front_blazer",
"abstract_southwestern_print_flounce_dress",
"collarless_a-line_bouclé_coat",
"colorblocked_popover_shirt_dress",
"floral_windowpane_print_dress",
"knit_mini_skirt",
"longline_linen-blend_tee",
"waffle_knit_sweater",
"crinkled_crepe_halter_dress",
"must-have_ruched_blazer",
"slub_knit_high-slit_maxi_dress",
"cloud_wash_cuffed_shorts",
"ornate_print_tulip_shorts",
"wild_cropped_muscle_tee",
"heathered_midi_dress",
"lace-paneled_racerback_tank",
"floral-paneled_mini_skirt",
"l.a.t.h.c._abstract_print_tee",
"beaded_bandage_dress",
"cheetah_print_high-neck_blouse",
"pleated-waist_shorts",
"utility_jumpsuit",
"tropical_print_combo_dress",
"strapless_tile_print_romper",
"rose_print_chiffon_dress",
"open-knit_fisherman_sweater",
"tile_print_strapless_jumpsuit",
"colorblock_heathered_capri_leggings",
"boxy_marled_ringer_tee",
"scoop_neck_maxi_dress",
"strappy_flounce_gauze_dress",
"rose_print_fit_&_flare_dress",
"floral_surplice_cutout_romper",
"tropical_curved-hem_tee",
"abstract_chevron_print_kimono",
"longline_slit_blazer",
"classic_chino_shorts",
"paneled_grid_print_shorts",
"reverse_abstract_print_sweatshorts",
"notorious_b.i.g._graphic_tee",
"lace_paneled_blouse",
"ruffled_wrap_dress",
"tulip_front_blazer",
"houndstooth_plaid_shorts",
"open-knit_batwing_cardigan",
"plaid_shirt_dress",
"maxi_utility_dress",
"matelassé_skater_dress",
"crinkled_satin_halter_dress",
"cubist_print_v-back_romper",
"woven_chelsea_collar_blouse",
"tribal_pattern_cardigan",
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"waffle_knit_crew_neck_sweater",
"young,_beautiful,_&_glamorous_sweater",
"side-cutout_heathered_shorts",
"buttoned_geo_jumpsuit",
"rad_graphic_tee",
"control_sector_shard_print_tee",
"cutout_maxi_cami_dress",
"striped_eyelash-knit_sweater",
"dip-dyed_sweater_tank",
"m-slit_slip_dress",
"cutout_abstract_print_dress"
] |
Carina124/plant-vit-model-1
|
<!-- 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. -->
# plant-vit-model-1
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: 0.1560
- Accuracy: 0.9995
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.296 | 1.0 | 83 | 1.0361 | 0.9227 |
| 0.4476 | 2.0 | 166 | 0.3646 | 0.9904 |
| 0.2731 | 3.0 | 249 | 0.2174 | 0.9952 |
| 0.2097 | 4.0 | 332 | 0.1560 | 0.9995 |
| 0.1679 | 5.0 | 415 | 0.1288 | 0.9973 |
| 0.135 | 6.0 | 498 | 0.1052 | 0.9984 |
| 0.118 | 7.0 | 581 | 0.0918 | 0.9989 |
| 0.1054 | 8.0 | 664 | 0.0826 | 0.9989 |
| 0.1083 | 9.0 | 747 | 0.0777 | 0.9989 |
| 0.0918 | 10.0 | 830 | 0.0756 | 0.9995 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"apple___apple_scab",
"apple___black_rot",
"apple___cedar_apple_rust",
"apple___healthy",
"blueberry___healthy",
"grape___black_rot",
"grape___healthy",
"peach___bacterial_spot",
"peach___healthy",
"potato___early_blight",
"potato___late_blight",
"potato___healthy",
"raspberry___healthy",
"soybean___healthy"
] |
maxingenio/platzi-vit-model-massimo
|
<!-- 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-massimo
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 pokemon-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 7.8941
- Accuracy: 0.0820
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9383 | 0.82 | 500 | 6.3834 | 0.0360 |
| 0.3399 | 1.64 | 1000 | 7.1051 | 0.0755 |
| 0.0749 | 2.46 | 1500 | 7.6120 | 0.0885 |
| 0.0332 | 3.28 | 2000 | 7.8941 | 0.0820 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"porygon",
"goldeen",
"articuno",
"clefable",
"cubone",
"marowak",
"nidorino",
"jolteon",
"muk",
"magikarp",
"slowbro",
"tauros",
"kabuto",
"seaking",
"spearow",
"sandshrew",
"eevee",
"kakuna",
"omastar",
"ekans",
"geodude",
"magmar",
"snorlax",
"meowth",
"dugtrio",
"pidgeotto",
"venusaur",
"persian",
"rhydon",
"starmie",
"charmeleon",
"lapras",
"alakazam",
"graveler",
"psyduck",
"machop",
"rapidash",
"doduo",
"magneton",
"arcanine",
"electrode",
"omanyte",
"poliwhirl",
"mew",
"alolan sandslash",
"mewtwo",
"jynx",
"weezing",
"gastly",
"victreebel",
"ivysaur",
"mrmime",
"shellder",
"scyther",
"diglett",
"primeape",
"raichu",
"oddish",
"dodrio",
"dragonair",
"weedle",
"golduck",
"hitmonlee",
"flareon",
"krabby",
"parasect",
"ninetales",
"nidoqueen",
"kabutops",
"drowzee",
"caterpie",
"jigglypuff",
"machamp",
"hitmonchan",
"clefairy",
"kangaskhan",
"dragonite",
"weepinbell",
"fearow",
"bellsprout",
"grimer",
"nidorina",
"staryu",
"horsea",
"gloom",
"electabuzz",
"dratini",
"machoke",
"magnemite",
"squirtle",
"gyarados",
"pidgeot",
"bulbasaur",
"nidoking",
"golem",
"aerodactyl",
"dewgong",
"moltres",
"zapdos",
"poliwrath",
"vulpix",
"beedrill",
"charmander",
"abra",
"zubat",
"golbat",
"mankey",
"wigglytuff",
"charizard",
"slowpoke",
"poliwag",
"tentacruel",
"rhyhorn",
"onix",
"butterfree",
"exeggcute",
"sandslash",
"seadra",
"pinsir",
"rattata",
"growlithe",
"haunter",
"pidgey",
"ditto",
"farfetchd",
"pikachu",
"raticate",
"wartortle",
"gengar",
"vaporeon",
"cloyster",
"hypno",
"arbok",
"metapod",
"tangela",
"kingler",
"exeggutor",
"kadabra",
"seel",
"venonat",
"voltorb",
"chansey",
"venomoth",
"ponyta",
"vileplume",
"koffing",
"blastoise",
"tentacool",
"lickitung",
"paras"
] |
platzi/platzi-vit-model-franco-medina
|
<!-- 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-franco-medina
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0776
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1331 | 3.85 | 500 | 0.0776 | 0.9850 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
tlord/vit-base-dogs
|
<!-- 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-dogs
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1696
- Accuracy: 0.9607
## 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-05
- train_batch_size: 10
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2208 | 1.0 | 591 | 0.3024 | 0.9343 |
| 0.4149 | 2.0 | 1182 | 0.2548 | 0.9268 |
| 0.3095 | 3.0 | 1773 | 0.2700 | 0.9329 |
| 0.2928 | 4.0 | 2364 | 0.1921 | 0.9444 |
| 0.2352 | 5.0 | 2955 | 0.1947 | 0.9472 |
| 0.1731 | 6.0 | 3546 | 0.2024 | 0.9458 |
| 0.1778 | 7.0 | 4137 | 0.1967 | 0.9526 |
| 0.156 | 8.0 | 4728 | 0.1780 | 0.9546 |
| 0.135 | 9.0 | 5319 | 0.1818 | 0.9553 |
| 0.1403 | 10.0 | 5910 | 0.1696 | 0.9607 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"abyssinian",
"bengal",
"siamese",
"sphynx",
"american bulldog",
"american pit bull terrier",
"basset hound",
"beagle",
"boxer",
"chihuahua",
"english cocker spaniel",
"english setter",
"birman",
"german shorthaired",
"great pyrenees",
"havanese",
"japanese chin",
"keeshond",
"leonberger",
"miniature pinscher",
"newfoundland",
"pomeranian",
"pug",
"bombay",
"saint bernard",
"samoyed",
"scottish terrier",
"shiba inu",
"staffordshire bull terrier",
"wheaten terrier",
"yorkshire terrier",
"british shorthair",
"egyptian mau",
"maine coon",
"persian",
"ragdoll",
"russian blue"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final
|
<!-- 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-brain-tumor-final
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.9396
- F1 Score: 0.9385
- Precision: 0.9408
## 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: 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 | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.1562 | 0.99 | 41 | 1.1378 | 0.6378 | 0.6191 | 0.6537 |
| 0.4878 | 1.99 | 82 | 0.6477 | 0.7591 | 0.7499 | 0.7874 |
| 0.2623 | 2.98 | 123 | 0.4410 | 0.8337 | 0.8311 | 0.8488 |
| 0.1985 | 4.0 | 165 | 0.4660 | 0.8144 | 0.8115 | 0.8455 |
| 0.1736 | 4.99 | 206 | 0.3230 | 0.8776 | 0.8760 | 0.8894 |
| 0.124 | 5.99 | 247 | 0.2684 | 0.9026 | 0.9014 | 0.9090 |
| 0.1278 | 6.98 | 288 | 0.2210 | 0.9180 | 0.9166 | 0.9210 |
| 0.0959 | 8.0 | 330 | 0.2151 | 0.9208 | 0.9195 | 0.9260 |
| 0.0849 | 8.99 | 371 | 0.2154 | 0.9220 | 0.9205 | 0.9291 |
| 0.0805 | 9.99 | 412 | 0.2112 | 0.9191 | 0.9179 | 0.9251 |
| 0.0682 | 10.98 | 453 | 0.1563 | 0.9385 | 0.9369 | 0.9402 |
| 0.0624 | 12.0 | 495 | 0.1577 | 0.9396 | 0.9385 | 0.9408 |
| 0.0415 | 12.99 | 536 | 0.1836 | 0.9305 | 0.9294 | 0.9332 |
| 0.0465 | 13.99 | 577 | 0.2145 | 0.9203 | 0.9192 | 0.9252 |
| 0.056 | 14.98 | 618 | 0.1710 | 0.9339 | 0.9325 | 0.9369 |
| 0.0545 | 16.0 | 660 | 0.2094 | 0.9248 | 0.9236 | 0.9298 |
| 0.0591 | 16.99 | 701 | 0.1752 | 0.9317 | 0.9303 | 0.9341 |
| 0.0512 | 17.99 | 742 | 0.1781 | 0.9311 | 0.9297 | 0.9342 |
| 0.0424 | 18.98 | 783 | 0.1873 | 0.9305 | 0.9293 | 0.9338 |
| 0.0438 | 19.88 | 820 | 0.1955 | 0.9265 | 0.9252 | 0.9307 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
Mytical/ClassificationOfLeaves
|
<!-- 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 Bean Leaves
Este modelo es una versión mejorada de [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) en el conjunto de datos beans.
Obtiene los siguientes resultados en el conjunto de evaluación:
- Pérdida: 0.0454
- Precisión: 0.9925
## Descripción del modelo
Al procesar imágenes de hojas, la IA puede realizar análisis y comparaciones con una base de datos de imágenes previamente etiquetadas para identificar patrones y características distintivas asociadas con diferentes enfermedades o daños.
### Hiperparámetros de entrenamiento
Durante el entrenamiento se utilizaron los siguientes hiperparámetros:
- learning_rate 0.0002
- tamaño_lote_entrenamiento: 16
- tamaño_lote_evaluación: 8
- semilla: 42
- optimizador: Adam con betas=(0.9,0.999) y epsilon=1e-08
- lr_scheduler_type: lineal
- número de épocas: 5
- entrenamiento_precisión_mezclada: Native AMP
### Resultados del entrenamiento
| Pérdida de entrenamiento| Epoch | Step | Pérdida de Validación | Precisión |
|:-----------------------:|:-----:|:----:|:---------------------:|:---------:|
| 0.0705 | 1.54 | 100 | 0.0562 | 0.9925 |
| 0.0123 | 3.08 | 200 | 0.0124 | 1.0 |
| 0.008 | 4.62 | 300 | 0.0099 | 1.0 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu102
- Conjuntos de datos 1.11.0
- Tokenizadores 0.10.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
anasalashqar/autotrain-hhhh-61081134661
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 61081134661
- CO2 Emissions (in grams): 0.0311
## Validation Metrics
- Loss: 0.491
- Accuracy: 0.975
- Precision: 0.952
- Recall: 1.000
- AUC: 1.000
- F1: 0.976
|
[
"lion",
"tiger"
] |
Zetatech/pvt-large-224
|
# Pyramid Vision Transformer (large-sized model)
Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in [this repository](https://github.com/whai362/PVT).
Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya).
## Model description
The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of variable-size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/Xrenya) 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 PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-large-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-large-224')
inputs = processor(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])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/pvt.html#).
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/whai362/PVT/blob/v2/classification/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
```
|
[
"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"
] |
Zetatech/pvt-medium-224
|
# Pyramid Vision Transformer (medium-sized model)
Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in [this repository](https://github.com/whai362/PVT).
Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya).
## Model description
The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of variable-size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/Xrenya) 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 PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-medium-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-medium-224')
inputs = processor(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])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/pvt.html#).
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/whai362/PVT/blob/v2/classification/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
```
|
[
"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"
] |
Zetatech/pvt-tiny-224
|
# Pyramid Vision Transformer (tiny-sized model)
Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in [this repository](https://github.com/whai362/PVT).
Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya).
## Model description
The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of variable-size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/Xrenya) 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 PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-tiny-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-tiny-224')
inputs = processor(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])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/pvt.html#).
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/whai362/PVT/blob/v2/classification/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
```
|
[
"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"
] |
Zetatech/pvt-small-224
|
# Pyramid Vision Transformer (small-sized model)
Pyramid Vision Transformer (PVT) model pre-trained on ImageNet-1K (1 million images, 1000 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://arxiv.org/abs/2102.12122) by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and first released in [this repository](https://github.com/whai362/PVT).
Disclaimer: The team releasing PVT did not write a model card for this model so this model card has been written by [Rinat S. [@Xrenya]](https://huggingface.co/Xrenya).
## Model description
The Pyramid Vision Transformer (PVT) is a transformer encoder model (BERT-like) pretrained on ImageNet-1k (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of variable-size patches, which are linearly embedded. Unlike ViT models, PVT is using a progressive shrinking pyramid to reduce computations of large feature maps at each stage. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/Xrenya) 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 PvtImageProcessor, PvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = PvtImageProcessor.from_pretrained('Zetatech/pvt-small-224')
model = PvtForImageClassification.from_pretrained('Zetatech/pvt-small-224')
inputs = processor(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])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/pvt.html#).
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/whai362/PVT/blob/v2/classification/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225).
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2021pyramid,
title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={568--578},
year={2021}
}
```
|
[
"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"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_02
|
<!-- 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-brain-tumor-final_02
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5751
- Accuracy: 0.7637
- F1 Score: 0.7582
- Precision: 0.8412
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2461 | 0.96 | 20 | 1.2674 | 0.4083 | 0.3093 | 0.4583 |
| 0.7271 | 1.98 | 41 | 0.9923 | 0.5666 | 0.5074 | 0.6374 |
| 0.3593 | 2.99 | 62 | 0.7205 | 0.7084 | 0.6803 | 0.7511 |
| 0.2162 | 4.0 | 83 | 0.7662 | 0.6925 | 0.6572 | 0.7751 |
| 0.1575 | 4.96 | 103 | 0.6434 | 0.7335 | 0.7161 | 0.8072 |
| 0.1174 | 5.98 | 124 | 0.6836 | 0.7141 | 0.6923 | 0.8004 |
| 0.0988 | 6.99 | 145 | 0.5644 | 0.7665 | 0.7572 | 0.8313 |
| 0.079 | 8.0 | 166 | 0.5964 | 0.7506 | 0.7376 | 0.8172 |
| 0.0786 | 8.96 | 186 | 0.5741 | 0.7574 | 0.7503 | 0.8315 |
| 0.0677 | 9.98 | 207 | 0.5306 | 0.7802 | 0.7697 | 0.8363 |
| 0.0518 | 10.99 | 228 | 0.5828 | 0.7625 | 0.7525 | 0.8331 |
| 0.0466 | 12.0 | 249 | 0.6159 | 0.7460 | 0.7370 | 0.8328 |
| 0.0344 | 12.96 | 269 | 0.6178 | 0.7489 | 0.7418 | 0.8339 |
| 0.0296 | 13.98 | 290 | 0.6194 | 0.7483 | 0.7389 | 0.8298 |
| 0.0246 | 14.99 | 311 | 0.6697 | 0.7329 | 0.7209 | 0.8281 |
| 0.0202 | 16.0 | 332 | 0.5769 | 0.7642 | 0.7570 | 0.8382 |
| 0.0261 | 16.96 | 352 | 0.5960 | 0.7551 | 0.7485 | 0.8363 |
| 0.0183 | 17.98 | 373 | 0.5145 | 0.7904 | 0.7856 | 0.8488 |
| 0.0169 | 18.99 | 394 | 0.5720 | 0.7648 | 0.7594 | 0.8412 |
| 0.0172 | 19.28 | 400 | 0.5751 | 0.7637 | 0.7582 | 0.8412 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_03
|
<!-- 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-brain-tumor-final_03
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3844
- Accuracy: 0.2739
- F1 Score: 0.2367
- Precision: 0.3487
## 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-08
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.407 | 0.96 | 20 | 1.3971 | 0.2648 | 0.2170 | 0.3446 |
| 1.4124 | 1.98 | 41 | 1.3943 | 0.2665 | 0.2219 | 0.3467 |
| 1.4035 | 2.99 | 62 | 1.3918 | 0.2682 | 0.2255 | 0.3508 |
| 1.3978 | 4.0 | 83 | 1.3897 | 0.2705 | 0.2293 | 0.3512 |
| 1.3903 | 4.96 | 103 | 1.3880 | 0.2699 | 0.2295 | 0.3492 |
| 1.3978 | 5.98 | 124 | 1.3866 | 0.2733 | 0.2342 | 0.3537 |
| 1.3864 | 6.99 | 145 | 1.3855 | 0.2739 | 0.2362 | 0.3552 |
| 1.3913 | 8.0 | 166 | 1.3848 | 0.2733 | 0.2357 | 0.3507 |
| 1.3901 | 8.96 | 186 | 1.3844 | 0.2739 | 0.2367 | 0.3487 |
| 1.3921 | 9.64 | 200 | 1.3844 | 0.2739 | 0.2367 | 0.3487 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
Dhika/leaves
|
<!-- 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. -->
# leaves
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 defect dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2249 | 1.25 | 10 | 0.0323 | 1.0 |
| 0.0177 | 2.5 | 20 | 0.0112 | 1.0 |
| 0.0086 | 3.75 | 30 | 0.0075 | 1.0 |
| 0.0063 | 5.0 | 40 | 0.0059 | 1.0 |
| 0.0051 | 6.25 | 50 | 0.0050 | 1.0 |
| 0.0045 | 7.5 | 60 | 0.0044 | 1.0 |
| 0.004 | 8.75 | 70 | 0.0040 | 1.0 |
| 0.0036 | 10.0 | 80 | 0.0036 | 1.0 |
| 0.0033 | 11.25 | 90 | 0.0034 | 1.0 |
| 0.0031 | 12.5 | 100 | 0.0031 | 1.0 |
| 0.0028 | 13.75 | 110 | 0.0029 | 1.0 |
| 0.0026 | 15.0 | 120 | 0.0027 | 1.0 |
| 0.0025 | 16.25 | 130 | 0.0025 | 1.0 |
| 0.0023 | 17.5 | 140 | 0.0024 | 1.0 |
| 0.0022 | 18.75 | 150 | 0.0023 | 1.0 |
| 0.0021 | 20.0 | 160 | 0.0021 | 1.0 |
| 0.002 | 21.25 | 170 | 0.0020 | 1.0 |
| 0.0019 | 22.5 | 180 | 0.0019 | 1.0 |
| 0.0018 | 23.75 | 190 | 0.0019 | 1.0 |
| 0.0017 | 25.0 | 200 | 0.0018 | 1.0 |
| 0.0016 | 26.25 | 210 | 0.0017 | 1.0 |
| 0.0016 | 27.5 | 220 | 0.0017 | 1.0 |
| 0.0015 | 28.75 | 230 | 0.0016 | 1.0 |
| 0.0015 | 30.0 | 240 | 0.0015 | 1.0 |
| 0.0014 | 31.25 | 250 | 0.0015 | 1.0 |
| 0.0014 | 32.5 | 260 | 0.0015 | 1.0 |
| 0.0013 | 33.75 | 270 | 0.0014 | 1.0 |
| 0.0013 | 35.0 | 280 | 0.0014 | 1.0 |
| 0.0013 | 36.25 | 290 | 0.0014 | 1.0 |
| 0.0013 | 37.5 | 300 | 0.0013 | 1.0 |
| 0.0012 | 38.75 | 310 | 0.0013 | 1.0 |
| 0.0012 | 40.0 | 320 | 0.0013 | 1.0 |
| 0.0012 | 41.25 | 330 | 0.0013 | 1.0 |
| 0.0012 | 42.5 | 340 | 0.0013 | 1.0 |
| 0.0012 | 43.75 | 350 | 0.0012 | 1.0 |
| 0.0012 | 45.0 | 360 | 0.0012 | 1.0 |
| 0.0011 | 46.25 | 370 | 0.0012 | 1.0 |
| 0.0012 | 47.5 | 380 | 0.0012 | 1.0 |
| 0.0011 | 48.75 | 390 | 0.0012 | 1.0 |
| 0.0011 | 50.0 | 400 | 0.0012 | 1.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"cacat",
"healthy"
] |
Tamakoma/CowboyHatEmoji
|
# Cowboy Hat emoji 🤠 (Western)
This model is a fine-tuned version of [facebook/convnextv2-large-22k-384](https://huggingface.co/facebook/convnextv2-large-22k-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4250
- Accuracy: 0.7778
## Model description
When you want to know if an art is 🤠 or not 🤠.
- Current iteration: v3.5 (Continuous Image Integration)
## Wait, why?
gelbooru contains a lot of images, however not all of them are in the same region as south eas asia. As such, to filter out such images we have created a classifier that in theory learns the differences between western (USA/Europe/etc.) and not western (Japan/China/SEA).
The definition of "Not Western" is limited to the the asian region (Japan, Korea, China, Taiwan, Thailand and the surroundign region). The author believes that the art is similar enough with the same "style" which he personally prefers over western art.
## Intended uses & limitations
filter gelbooru data on 🤠 or not 🤠
## Training and evaluation data
Selected 358 images of 🤠 and not 🤠.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 802565
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7384 | 1.0 | 152 | 0.4268 | 0.7963 |
| 0.2888 | 2.0 | 304 | 0.4250 | 0.7778 |
| 0.2953 | 3.0 | 456 | 0.4250 | 0.7778 |
| 0.4914 | 4.0 | 608 | 0.4250 | 0.7778 |
| 0.4099 | 5.0 | 760 | 0.4250 | 0.7778 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"not_western",
"western"
] |
stevelee609/my_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_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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3194
- Accuracy: 0.939
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8638 | 0.99 | 62 | 0.9578 | 0.913 |
| 0.6163 | 2.0 | 125 | 0.7060 | 0.911 |
| 0.5103 | 2.99 | 187 | 0.4994 | 0.936 |
| 0.3659 | 4.0 | 250 | 0.4539 | 0.927 |
| 0.3207 | 4.99 | 312 | 0.3999 | 0.933 |
| 0.2523 | 6.0 | 375 | 0.3799 | 0.921 |
| 0.2257 | 6.99 | 437 | 0.3703 | 0.922 |
| 0.1937 | 8.0 | 500 | 0.3160 | 0.936 |
| 0.1854 | 8.99 | 562 | 0.3229 | 0.93 |
| 0.2048 | 9.92 | 620 | 0.3194 | 0.939 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- 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"
] |
rsadaphule/vit-base-patch16-224-finetuned-flower-ravindrasadaphule
|
<!-- 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-ravindrasadaphule
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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_04
|
<!-- 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-brain-tumor-final_04
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1011
- Accuracy: 0.9675
- F1 Score: 0.9673
- Precision: 0.9675
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 0.8612 | 0.96 | 20 | 0.5617 | 0.8041 | 0.7839 | 0.8166 |
| 0.2544 | 1.98 | 41 | 0.3496 | 0.8759 | 0.8723 | 0.8858 |
| 0.1305 | 2.99 | 62 | 0.1768 | 0.9294 | 0.9285 | 0.9323 |
| 0.0766 | 4.0 | 83 | 0.1201 | 0.9510 | 0.9503 | 0.9507 |
| 0.0651 | 4.96 | 103 | 0.1046 | 0.9636 | 0.9633 | 0.9641 |
| 0.0532 | 5.98 | 124 | 0.1001 | 0.9664 | 0.9660 | 0.9663 |
| 0.0374 | 6.99 | 145 | 0.1416 | 0.9505 | 0.9498 | 0.9515 |
| 0.0263 | 8.0 | 166 | 0.1032 | 0.9630 | 0.9626 | 0.9628 |
| 0.0234 | 8.96 | 186 | 0.1011 | 0.9675 | 0.9673 | 0.9675 |
| 0.0207 | 9.64 | 200 | 0.1026 | 0.9647 | 0.9644 | 0.9647 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
venradenator/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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Guldeniz/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. -->
# Guldeniz/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 Lung and Colon Histopathological Images dataset. This dataset can be reach via [Kaggle](https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images).
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: 0.0084
- Validation Accuracy: 0.9997
- Validation Top-3-accuracy: 1.0
- Epoch: 3
## Model description
The vision transformer model, trained by Google, has been fine-tuned using a lung and colon cancer image dataset consisting of a total of 25,000 images across 5 labels. The obtained results are highly promising, and the model demonstrates the ability to predict the following listed labels.
- colon_aca
- colon_n
- lung_aca
- lung_n
- lung_scc
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3325, '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}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.1870 | 0.9784 | 0.9985 | 0.0455 | 0.9987 | 1.0 | 0 |
| 0.0345 | 0.9972 | 1.0 | 0.0189 | 0.9995 | 1.0 | 1 |
| 0.0134 | 1.0 | 1.0 | 0.0110 | 0.9997 | 1.0 | 2 |
| 0.0088 | 1.0 | 1.0 | 0.0084 | 0.9997 | 1.0 | 3 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.12.0
- Datasets 2.10.1
- Tokenizers 0.13.3
|
[
"lung_aca",
"colon_n",
"colon_aca",
"lung_n",
"lung_scc"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_05
|
<!-- 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-brain-tumor-final_05
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1136
- Accuracy: 0.9584
- F1 Score: 0.9562
- Precision: 0.9576
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2801 | 0.98 | 13 | 0.6953 | 0.7335 | 0.6819 | 0.7815 |
| 0.5928 | 1.96 | 26 | 0.3691 | 0.8440 | 0.8218 | 0.8629 |
| 0.3122 | 2.94 | 39 | 0.1664 | 0.9402 | 0.9377 | 0.9373 |
| 0.1513 | 4.0 | 53 | 0.1292 | 0.9493 | 0.9468 | 0.9467 |
| 0.1227 | 4.98 | 66 | 0.1030 | 0.9601 | 0.9577 | 0.9585 |
| 0.1201 | 5.96 | 79 | 0.1312 | 0.9522 | 0.9496 | 0.9508 |
| 0.0806 | 6.94 | 92 | 0.1306 | 0.9522 | 0.9494 | 0.9520 |
| 0.0645 | 8.0 | 106 | 0.1474 | 0.9482 | 0.9457 | 0.9490 |
| 0.0668 | 8.98 | 119 | 0.0947 | 0.9613 | 0.9589 | 0.9600 |
| 0.0577 | 9.81 | 130 | 0.1136 | 0.9584 | 0.9562 | 0.9576 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
palbee/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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
DanggoRyo/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: 0.0635
- Accuracy: 0.98
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2759 | 1.0 | 190 | 0.1109 | 0.9670 |
| 0.1744 | 2.0 | 380 | 0.0871 | 0.9715 |
| 0.1316 | 3.0 | 570 | 0.0635 | 0.98 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
SriPrasanna/autotrain-8v13-xs57-x55z-61491134800
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
- CO2 Emissions (in grams): 0.0000
## Validation Metricsg
loss: 0.9600464105606079
f1_macro: 0.5260821309655938
f1_micro: 0.675
f1_weighted: 0.5832408435072142
precision_macro: 0.5353535353535354
precision_micro: 0.675
precision_weighted: 0.553030303030303
recall_macro: 0.5666666666666667
recall_micro: 0.675
recall_weighted: 0.675
accuracy: 0.675
|
[
"grade1",
"grade2",
"grade3"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_06
|
<!-- 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-brain-tumor-final_06
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2384
- Accuracy: 0.9226
- F1 Score: 0.9210
- Precision: 0.9214
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3082 | 0.98 | 13 | 0.7669 | 0.7819 | 0.7689 | 0.7723 |
| 0.5415 | 1.96 | 26 | 0.3100 | 0.8867 | 0.8812 | 0.8816 |
| 0.279 | 2.94 | 39 | 0.2901 | 0.8992 | 0.8967 | 0.8961 |
| 0.1563 | 4.0 | 53 | 0.2655 | 0.9089 | 0.9078 | 0.9084 |
| 0.1304 | 4.98 | 66 | 0.2971 | 0.8964 | 0.8935 | 0.8958 |
| 0.1058 | 5.96 | 79 | 0.2358 | 0.9243 | 0.9218 | 0.9224 |
| 0.0971 | 6.94 | 92 | 0.2298 | 0.9260 | 0.9245 | 0.9258 |
| 0.079 | 8.0 | 106 | 0.2468 | 0.9134 | 0.9123 | 0.9125 |
| 0.0638 | 8.98 | 119 | 0.2534 | 0.9112 | 0.9097 | 0.9101 |
| 0.0538 | 9.81 | 130 | 0.2384 | 0.9226 | 0.9210 | 0.9214 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_07
|
<!-- 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-brain-tumor-final_07
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2904
- Accuracy: 0.9095
- F1 Score: 0.9095
- Precision: 0.9189
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3335 | 0.98 | 13 | 0.9195 | 0.6281 | 0.6111 | 0.7245 |
| 0.6062 | 1.96 | 26 | 0.6114 | 0.7625 | 0.7673 | 0.8385 |
| 0.274 | 2.94 | 39 | 0.5468 | 0.7802 | 0.7772 | 0.8533 |
| 0.1211 | 4.0 | 53 | 0.3922 | 0.8417 | 0.8417 | 0.8749 |
| 0.0991 | 4.98 | 66 | 0.4734 | 0.8172 | 0.8209 | 0.8802 |
| 0.0682 | 5.96 | 79 | 0.3751 | 0.8599 | 0.8600 | 0.8882 |
| 0.0414 | 6.94 | 92 | 0.2951 | 0.8986 | 0.8995 | 0.9100 |
| 0.0264 | 8.0 | 106 | 0.3485 | 0.8844 | 0.8855 | 0.9069 |
| 0.021 | 8.98 | 119 | 0.3803 | 0.8764 | 0.8782 | 0.9031 |
| 0.0151 | 9.81 | 130 | 0.2904 | 0.9095 | 0.9095 | 0.9189 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
pumpitup521/vit_finedtuned_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. -->
# vit_finedtuned_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2214
- Accuracy: 0.934
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6064 | 0.99 | 62 | 0.4593 | 0.872 |
| 0.2607 | 2.0 | 125 | 0.2649 | 0.92 |
| 0.2074 | 2.99 | 187 | 0.2421 | 0.924 |
| 0.0908 | 4.0 | 250 | 0.2442 | 0.921 |
| 0.1237 | 4.99 | 312 | 0.2353 | 0.923 |
| 0.0915 | 6.0 | 375 | 0.2402 | 0.923 |
| 0.0549 | 6.99 | 437 | 0.2053 | 0.933 |
| 0.0645 | 8.0 | 500 | 0.2190 | 0.929 |
| 0.087 | 8.99 | 562 | 0.2313 | 0.935 |
| 0.0544 | 9.92 | 620 | 0.2214 | 0.934 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- 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"
] |
Vikas-nnresearch/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: 0.7400
- Accuracy: 0.6667
## 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 | 0.8 | 1 | 0.7400 | 0.6667 |
| No log | 1.6 | 2 | 0.8959 | 0.3333 |
| No log | 2.4 | 3 | 0.9678 | 0.3333 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"knob",
"no knob"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_08
|
<!-- 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-brain-tumor-final_08
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1422
- Accuracy: 0.9537
- F1 Score: 0.9549
- Precision: 0.9564
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.3618 | 0.99 | 19 | 0.6238 | 0.7541 | 0.7431 | 0.7821 |
| 0.3833 | 1.97 | 38 | 0.3097 | 0.8865 | 0.8884 | 0.8970 |
| 0.2011 | 2.96 | 57 | 0.2600 | 0.9053 | 0.9078 | 0.9171 |
| 0.1124 | 4.0 | 77 | 0.1793 | 0.9328 | 0.9342 | 0.9381 |
| 0.0711 | 4.99 | 96 | 0.1385 | 0.9497 | 0.9509 | 0.9522 |
| 0.0518 | 5.97 | 115 | 0.1506 | 0.9485 | 0.9501 | 0.9523 |
| 0.0393 | 6.96 | 134 | 0.1422 | 0.9537 | 0.9549 | 0.9564 |
| 0.0361 | 8.0 | 154 | 0.1545 | 0.9482 | 0.9497 | 0.9522 |
| 0.025 | 8.99 | 173 | 0.1482 | 0.9501 | 0.9516 | 0.9541 |
| 0.0204 | 9.87 | 190 | 0.1474 | 0.9513 | 0.9527 | 0.9550 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
heroza/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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0557
- Accuracy: 0.9822
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2902 | 1.0 | 190 | 0.1137 | 0.9626 |
| 0.1872 | 2.0 | 380 | 0.0652 | 0.9793 |
| 0.1215 | 3.0 | 570 | 0.0557 | 0.9822 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"annualcrop",
"forest",
"herbaceousvegetation",
"highway",
"industrial",
"pasture",
"permanentcrop",
"residential",
"river",
"sealake"
] |
vhurryharry/cifar
|
<!-- 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. -->
# cifar
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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4714
- Accuracy: 0.883
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7956 | 0.99 | 62 | 1.6395 | 0.817 |
| 0.8981 | 2.0 | 125 | 0.8510 | 0.858 |
| 0.6049 | 2.99 | 187 | 0.6666 | 0.878 |
| 0.5427 | 4.0 | 250 | 0.5796 | 0.88 |
| 0.4318 | 4.99 | 312 | 0.5110 | 0.889 |
| 0.3952 | 6.0 | 375 | 0.4339 | 0.907 |
| 0.3544 | 6.99 | 437 | 0.4432 | 0.902 |
| 0.3612 | 8.0 | 500 | 0.4213 | 0.898 |
| 0.3522 | 8.99 | 562 | 0.4474 | 0.884 |
| 0.3096 | 9.92 | 620 | 0.4714 | 0.883 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
davidwtan/my_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_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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5005
- 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2078 | 0.99 | 62 | 2.9572 | 0.787 |
| 1.7221 | 2.0 | 125 | 1.6469 | 0.861 |
| 1.2109 | 2.99 | 187 | 1.1555 | 0.894 |
| 0.81 | 4.0 | 250 | 0.8631 | 0.91 |
| 0.6486 | 4.99 | 312 | 0.7190 | 0.908 |
| 0.5162 | 6.0 | 375 | 0.6194 | 0.91 |
| 0.4567 | 6.99 | 437 | 0.5399 | 0.924 |
| 0.43 | 8.0 | 500 | 0.5146 | 0.922 |
| 0.3723 | 8.99 | 562 | 0.4914 | 0.922 |
| 0.3938 | 9.92 | 620 | 0.5005 | 0.915 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- 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"
] |
vhurryharry/cifarv2
|
<!-- 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. -->
# cifarv2
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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2653
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6994 | 1.0 | 250 | 0.7132 | 0.8758 |
| 0.4271 | 2.0 | 500 | 0.4477 | 0.894 |
| 0.3112 | 3.0 | 750 | 0.3905 | 0.8942 |
| 0.3139 | 4.0 | 1000 | 0.3207 | 0.9115 |
| 0.2511 | 5.0 | 1250 | 0.3288 | 0.9048 |
| 0.2652 | 6.0 | 1500 | 0.2977 | 0.9125 |
| 0.2392 | 7.0 | 1750 | 0.2720 | 0.9187 |
| 0.1759 | 8.0 | 2000 | 0.2670 | 0.9173 |
| 0.2024 | 9.0 | 2250 | 0.2606 | 0.9193 |
| 0.1774 | 10.0 | 2500 | 0.2653 | 0.921 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
harshp8l/Fashion-Product-Images
|
<!-- 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. -->
# Fashion-Product-Images
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: 0.0354
- Accuracy: 0.9947
## 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-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0915 | 1.0 | 1135 | 0.0693 | 0.9905 |
| 0.0479 | 2.0 | 2270 | 0.0408 | 0.9938 |
| 0.0324 | 3.0 | 3405 | 0.0354 | 0.9947 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"free items",
"footwear",
"sporting goods",
"home",
"personal care",
"accessories",
"apparel"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_09
|
<!-- 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-brain-tumor-final_09
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2586
- Accuracy: 0.9077
- F1 Score: 0.9093
- Precision: 0.9184
- Sensitivity: 0.9071
- Specificity: 0.9766
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- 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 | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.4243 | 0.99 | 19 | 1.2818 | 0.4124 | 0.3570 | 0.4910 | 0.4019 | 0.8403 |
| 1.1046 | 1.97 | 38 | 0.8873 | 0.6658 | 0.6584 | 0.7235 | 0.6608 | 0.9117 |
| 0.5232 | 2.96 | 57 | 0.5753 | 0.7671 | 0.7654 | 0.8063 | 0.7631 | 0.9395 |
| 0.3235 | 4.0 | 77 | 0.4476 | 0.8256 | 0.8272 | 0.8496 | 0.8228 | 0.9549 |
| 0.2586 | 4.99 | 96 | 0.3886 | 0.8590 | 0.8608 | 0.8764 | 0.8567 | 0.9638 |
| 0.1986 | 5.97 | 115 | 0.3538 | 0.8641 | 0.8663 | 0.8816 | 0.8624 | 0.9652 |
| 0.166 | 6.96 | 134 | 0.3543 | 0.8649 | 0.8668 | 0.8849 | 0.8637 | 0.9655 |
| 0.1345 | 8.0 | 154 | 0.3729 | 0.8586 | 0.8610 | 0.8837 | 0.8571 | 0.9640 |
| 0.1197 | 8.99 | 173 | 0.2879 | 0.8975 | 0.8987 | 0.9098 | 0.8961 | 0.9740 |
| 0.1033 | 9.97 | 192 | 0.2810 | 0.8998 | 0.9013 | 0.9128 | 0.8983 | 0.9746 |
| 0.0957 | 10.96 | 211 | 0.3239 | 0.8802 | 0.8818 | 0.8988 | 0.8795 | 0.9696 |
| 0.085 | 12.0 | 231 | 0.2586 | 0.9077 | 0.9093 | 0.9184 | 0.9071 | 0.9766 |
| 0.0769 | 12.99 | 250 | 0.2662 | 0.9018 | 0.9036 | 0.9149 | 0.9011 | 0.9751 |
| 0.0758 | 13.97 | 269 | 0.2830 | 0.8951 | 0.8970 | 0.9102 | 0.8945 | 0.9734 |
| 0.068 | 14.96 | 288 | 0.2757 | 0.8967 | 0.8986 | 0.9113 | 0.8960 | 0.9738 |
| 0.0641 | 16.0 | 308 | 0.2743 | 0.8991 | 0.9008 | 0.9136 | 0.8984 | 0.9744 |
| 0.0623 | 16.99 | 327 | 0.2713 | 0.8987 | 0.9001 | 0.9127 | 0.8982 | 0.9743 |
| 0.0542 | 17.97 | 346 | 0.2650 | 0.8987 | 0.9005 | 0.9128 | 0.8980 | 0.9743 |
| 0.0573 | 18.96 | 365 | 0.2709 | 0.8963 | 0.8981 | 0.9112 | 0.8957 | 0.9737 |
| 0.058 | 19.74 | 380 | 0.2778 | 0.8947 | 0.8965 | 0.9101 | 0.8942 | 0.9733 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
aalonso-developer/vit-base-clothing-leafs-example-full-simple
|
<!-- 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-clothing-leafs-example-full-simple
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9954
- Accuracy: 0.7155
## 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: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.9495 | 0.14 | 1000 | 1.4553 | 0.6307 |
| 1.3079 | 0.28 | 2000 | 1.2347 | 0.6677 |
| 1.178 | 0.41 | 3000 | 1.1607 | 0.6758 |
| 1.1324 | 0.55 | 4000 | 1.1307 | 0.6824 |
| 1.0928 | 0.69 | 5000 | 1.0956 | 0.6909 |
| 1.0679 | 0.83 | 6000 | 1.0790 | 0.6912 |
| 1.0488 | 0.97 | 7000 | 1.0486 | 0.7014 |
| 0.9548 | 1.11 | 8000 | 1.0449 | 0.7016 |
| 0.9352 | 1.24 | 9000 | 1.0348 | 0.7042 |
| 0.9164 | 1.38 | 10000 | 1.0340 | 0.7034 |
| 0.9267 | 1.52 | 11000 | 1.0178 | 0.7089 |
| 0.9058 | 1.66 | 12000 | 1.0160 | 0.7063 |
| 0.9028 | 1.8 | 13000 | 1.0084 | 0.7111 |
| 0.9093 | 1.94 | 14000 | 1.0009 | 0.7136 |
| 0.8346 | 2.07 | 15000 | 1.0152 | 0.7117 |
| 0.7897 | 2.21 | 16000 | 1.0072 | 0.7141 |
| 0.7869 | 2.35 | 17000 | 1.0088 | 0.7083 |
| 0.7853 | 2.49 | 18000 | 0.9981 | 0.7162 |
| 0.7732 | 2.63 | 19000 | 1.0030 | 0.7149 |
| 0.779 | 2.77 | 20000 | 0.9954 | 0.7155 |
| 0.7655 | 2.9 | 21000 | 0.9972 | 0.7179 |
| 0.74 | 3.04 | 22000 | 1.0114 | 0.7138 |
| 0.6824 | 3.18 | 23000 | 1.0171 | 0.7130 |
| 0.68 | 3.32 | 24000 | 1.0111 | 0.7178 |
| 0.6787 | 3.46 | 25000 | 1.0124 | 0.7151 |
| 0.6808 | 3.6 | 26000 | 1.0181 | 0.7150 |
| 0.6561 | 3.73 | 27000 | 1.0144 | 0.7168 |
| 0.6611 | 3.87 | 28000 | 1.0154 | 0.7155 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"skirt",
"jodhpurs",
"leggings",
"coverup",
"capris",
"dress",
"sweatshorts",
"tee",
"jersey",
"cutoffs",
"sweatpants",
"sarong",
"henley",
"joggers",
"tank",
"poncho",
"onesie",
"anorak",
"kimono",
"romper",
"halter",
"top",
"culottes",
"jeggings",
"robe",
"shorts",
"jeans",
"cardigan",
"jumpsuit",
"sweater",
"bomber",
"parka",
"chinos",
"turtleneck",
"flannel",
"trunks",
"blouse",
"gauchos",
"blazer",
"caftan",
"hoodie",
"coat",
"peacoat",
"jacket",
"button-down",
"kaftan"
] |
heyitskim1912/AML_A2_Q4
|
<!-- 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. -->
# AML_A2_Q4
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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0432
- Accuracy: 0.9894
## 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-05
- train_batch_size: 20
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1642 | 1.0 | 2250 | 0.0572 | 0.9862 |
| 0.1503 | 2.0 | 4500 | 0.0591 | 0.9854 |
| 0.1818 | 3.0 | 6750 | 0.0432 | 0.9894 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
jayanthspratap/convnext-tiny-224-drfx-CT-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. -->
# convnext-tiny-224-drfx-CT-classifier
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6600
- Accuracy: 0.7647
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6600 | 0.7647 |
| No log | 2.0 | 8 | 0.7135 | 0.5882 |
| 0.4055 | 3.0 | 12 | 0.6851 | 0.6471 |
| 0.4055 | 4.0 | 16 | 0.6512 | 0.6471 |
| 0.3895 | 5.0 | 20 | 0.7131 | 0.6471 |
| 0.3895 | 6.0 | 24 | 0.7158 | 0.6471 |
| 0.3895 | 7.0 | 28 | 0.6533 | 0.7647 |
| 0.363 | 8.0 | 32 | 0.6211 | 0.7059 |
| 0.363 | 9.0 | 36 | 0.6222 | 0.7647 |
| 0.3207 | 10.0 | 40 | 0.6255 | 0.7647 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
jayanthspratap/resnet-50-drfx-CT-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. -->
# resnet-50-drfx-CT-classifier
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6813
- Accuracy: 0.7647
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6770 | 0.6471 |
| No log | 2.0 | 8 | 0.6813 | 0.7647 |
| 0.6847 | 3.0 | 12 | 0.6777 | 0.7059 |
| 0.6847 | 4.0 | 16 | 0.6819 | 0.7059 |
| 0.6886 | 5.0 | 20 | 0.6842 | 0.6471 |
| 0.6886 | 6.0 | 24 | 0.6806 | 0.7059 |
| 0.6886 | 7.0 | 28 | 0.6765 | 0.7059 |
| 0.6865 | 8.0 | 32 | 0.6807 | 0.7647 |
| 0.6865 | 9.0 | 36 | 0.6822 | 0.6471 |
| 0.6848 | 10.0 | 40 | 0.6832 | 0.5882 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
jayanthspratap/convnext-tiny-224-drfx-surgery-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. -->
# convnext-tiny-224-drfx-surgery-classifier
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6160
- Accuracy: 0.875
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.7140 | 0.375 |
| No log | 2.0 | 8 | 0.6876 | 0.5 |
| 0.7104 | 3.0 | 12 | 0.6666 | 0.625 |
| 0.7104 | 4.0 | 16 | 0.6495 | 0.6875 |
| 0.6567 | 5.0 | 20 | 0.6360 | 0.75 |
| 0.6567 | 6.0 | 24 | 0.6247 | 0.8125 |
| 0.6567 | 7.0 | 28 | 0.6160 | 0.875 |
| 0.6277 | 8.0 | 32 | 0.6098 | 0.875 |
| 0.6277 | 9.0 | 36 | 0.6058 | 0.875 |
| 0.6122 | 10.0 | 40 | 0.6043 | 0.875 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
jayanthspratap/resnet-50-drfx-surgery-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. -->
# resnet-50-drfx-surgery-classifier
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6399
- Accuracy: 0.875
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6591 | 0.8125 |
| No log | 2.0 | 8 | 0.6399 | 0.875 |
| 0.6638 | 3.0 | 12 | 0.6671 | 0.875 |
| 0.6638 | 4.0 | 16 | 0.6645 | 0.8125 |
| 0.6562 | 5.0 | 20 | 0.6495 | 0.875 |
| 0.6562 | 6.0 | 24 | 0.6673 | 0.875 |
| 0.6562 | 7.0 | 28 | 0.6539 | 0.875 |
| 0.6527 | 8.0 | 32 | 0.6519 | 0.875 |
| 0.6527 | 9.0 | 36 | 0.6603 | 0.875 |
| 0.6596 | 10.0 | 40 | 0.6525 | 0.875 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
jayanthspratap/vit-base-patch16-224-drfx-surgery-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. -->
# vit-base-patch16-224-drfx-surgery-classifier
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: 0.3696
- Accuracy: 0.875
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.5814 | 0.875 |
| No log | 2.0 | 8 | 0.4649 | 0.875 |
| 0.6165 | 3.0 | 12 | 0.4166 | 0.875 |
| 0.6165 | 4.0 | 16 | 0.3897 | 0.875 |
| 0.4482 | 5.0 | 20 | 0.3811 | 0.875 |
| 0.4482 | 6.0 | 24 | 0.3754 | 0.875 |
| 0.4482 | 7.0 | 28 | 0.3724 | 0.875 |
| 0.4023 | 8.0 | 32 | 0.3707 | 0.875 |
| 0.4023 | 9.0 | 36 | 0.3698 | 0.875 |
| 0.4415 | 10.0 | 40 | 0.3696 | 0.875 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
jayanthspratap/vit-base-patch16-224-drfx-CT-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. -->
# vit-base-patch16-224-drfx-CT-classifier
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: 0.6281
- Accuracy: 0.7059
## 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-06
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.7020 | 0.5294 |
| No log | 2.0 | 8 | 0.6686 | 0.6471 |
| 0.7085 | 3.0 | 12 | 0.6509 | 0.5882 |
| 0.7085 | 4.0 | 16 | 0.6336 | 0.6471 |
| 0.6847 | 5.0 | 20 | 0.6281 | 0.7059 |
| 0.6847 | 6.0 | 24 | 0.6256 | 0.7059 |
| 0.6847 | 7.0 | 28 | 0.6229 | 0.7059 |
| 0.6814 | 8.0 | 32 | 0.6218 | 0.7059 |
| 0.6814 | 9.0 | 36 | 0.6214 | 0.7059 |
| 0.6717 | 10.0 | 40 | 0.6213 | 0.7059 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"no",
"yes"
] |
franco1102/human-actions-vit-model-francomedin
|
<!-- 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. -->
# human-actions-vit-model-francomedin
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: 8.2276
- Accuracy: 0.0631
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3325 | 0.32 | 500 | 4.2477 | 0.0298 |
| 0.9863 | 0.63 | 1000 | 3.6803 | 0.1269 |
| 0.8753 | 0.95 | 1500 | 4.7335 | 0.0443 |
| 0.5808 | 1.27 | 2000 | 5.3964 | 0.0752 |
| 0.5256 | 1.59 | 2500 | 5.2087 | 0.1059 |
| 0.5277 | 1.9 | 3000 | 5.7696 | 0.0783 |
| 0.326 | 2.22 | 3500 | 6.5902 | 0.0348 |
| 0.2548 | 2.54 | 4000 | 6.4781 | 0.0961 |
| 0.2708 | 2.86 | 4500 | 6.2750 | 0.0933 |
| 0.1677 | 3.17 | 5000 | 7.2354 | 0.0763 |
| 0.0796 | 3.49 | 5500 | 7.6928 | 0.0633 |
| 0.0904 | 3.81 | 6000 | 7.5144 | 0.0607 |
| 0.0561 | 4.13 | 6500 | 7.9617 | 0.0665 |
| 0.0205 | 4.44 | 7000 | 7.9271 | 0.0681 |
| 0.0204 | 4.76 | 7500 | 8.2276 | 0.0631 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"calling",
"clapping",
"running",
"sitting",
"sleeping",
"texting",
"using_laptop",
"cycling",
"dancing",
"drinking",
"eating",
"fighting",
"hugging",
"laughing",
"listening_to_music"
] |
platzi/platzi-vit-model-luis-rascon
|
<!-- 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-luis-rascon
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0563
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1447 | 3.85 | 500 | 0.0563 | 0.9850 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_10
|
<!-- 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-brain-tumor-final_10
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Accuracy: 0.9375
- F1 Score: 0.9383
- Precision: 0.9451
- Sensitivity: 0.9381
- Specificity: 0.9843
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.3428 | 0.99 | 19 | 0.7059 | 0.7467 | 0.7535 | 0.7951 | 0.7464 | 0.9332 |
| 0.3308 | 1.97 | 38 | 0.2314 | 0.9183 | 0.9194 | 0.9239 | 0.9191 | 0.9792 |
| 0.1601 | 2.96 | 57 | 0.2024 | 0.9305 | 0.9314 | 0.9349 | 0.9306 | 0.9824 |
| 0.0976 | 4.0 | 77 | 0.3376 | 0.8904 | 0.8943 | 0.9126 | 0.8930 | 0.9724 |
| 0.0585 | 4.99 | 96 | 0.3893 | 0.8830 | 0.8853 | 0.9115 | 0.8854 | 0.9706 |
| 0.0432 | 5.97 | 115 | 0.2559 | 0.9214 | 0.9239 | 0.9330 | 0.9237 | 0.9802 |
| 0.0313 | 6.96 | 134 | 0.2175 | 0.9375 | 0.9383 | 0.9451 | 0.9381 | 0.9843 |
| 0.0176 | 8.0 | 154 | 0.2309 | 0.9313 | 0.9326 | 0.9386 | 0.9320 | 0.9827 |
| 0.0152 | 8.99 | 173 | 0.2358 | 0.9328 | 0.9339 | 0.9416 | 0.9336 | 0.9831 |
| 0.0089 | 9.87 | 190 | 0.2116 | 0.9360 | 0.9374 | 0.9437 | 0.9372 | 0.9839 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
jonastokoliu/image_cls_vit-base-patch16-224-in21k_food101_finetune
|
<!-- 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_cls_vit-base-patch16-224-in21k_food101_finetune
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3653
- Accuracy: 0.838
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5065 | 0.99 | 31 | 3.2552 | 0.779 |
| 2.67 | 1.98 | 62 | 2.5466 | 0.833 |
| 2.3665 | 2.98 | 93 | 2.3653 | 0.838 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- 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"
] |
jazzmacedo/fruits-and-vegetables-detector-36
|
# fruits-and-vegetables-detector-36
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50).
It achieves the following results on the evaluation set:
- Loss: 0.0014
- Accuracy: 0.9721
## Model description
This Model is a exploration test using the base model resnet-50 from microsoft.
## Intended uses & limitations
This Model was trained with a very small dataset
[kritikseth/fruit-and-vegetable-image-recognition](https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition) that contains only 36 labels
### How to use
Here is how to use this model to classify an image:
```python
import cv2
import torch
import torchvision.transforms as transforms
from transformers import AutoModelForImageClassification
from PIL import Image
# Load the saved model and tokenizer
model = AutoModelForImageClassification.from_pretrained("jazzmacedo/fruits-and-vegetables-detector-36")
# Get the list of labels from the model's configuration
labels = list(model.config.id2label.values())
# Define the preprocessing transformation
preprocess = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image_path = "path/to/your/image.jpg"
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(image) # Convert NumPy array to PIL image
input_tensor = preprocess(pil_image).unsqueeze(0)
# Run the image through the model
outputs = model(input_tensor)
# Get the predicted label index
predicted_idx = torch.argmax(outputs.logits, dim=1).item()
# Get the predicted label text
predicted_label = labels[predicted_idx]
# Print the predicted label
print("Detected label:", predicted_label)
```
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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
|
[
"apple",
"banana",
"beetroot",
"bell pepper",
"cabbage",
"capsicum",
"carrot",
"cauliflower",
"chilli pepper",
"corn",
"cucumber",
"eggplant",
"garlic",
"ginger",
"grapes",
"jalepeno",
"kiwi",
"lemon",
"lettuce",
"mango",
"onion",
"orange",
"paprika",
"pear",
"peas",
"pineapple",
"pomegranate",
"potato",
"raddish",
"soy beans",
"spinach",
"sweetcorn",
"sweetpotato",
"tomato",
"turnip",
"watermelon"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_11
|
<!-- 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-brain-tumor-final_11
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0783
- Accuracy: 0.9800
- F1 Score: 0.9796
- Precision: 0.9797
- Sensitivity: 0.9796
- Specificity: 0.9950
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.2367 | 0.99 | 19 | 0.3560 | 0.8649 | 0.8629 | 0.8649 | 0.8629 | 0.9648 |
| 0.2911 | 1.97 | 38 | 0.2087 | 0.9297 | 0.9290 | 0.9335 | 0.9277 | 0.9822 |
| 0.1681 | 2.96 | 57 | 0.1393 | 0.9564 | 0.9558 | 0.9558 | 0.9562 | 0.9890 |
| 0.0923 | 4.0 | 77 | 0.1106 | 0.9643 | 0.9639 | 0.9637 | 0.9647 | 0.9910 |
| 0.0602 | 4.99 | 96 | 0.1510 | 0.9505 | 0.9494 | 0.9512 | 0.9504 | 0.9875 |
| 0.0388 | 5.97 | 115 | 0.1145 | 0.9666 | 0.9667 | 0.9670 | 0.9672 | 0.9916 |
| 0.0197 | 6.96 | 134 | 0.0783 | 0.9800 | 0.9796 | 0.9797 | 0.9796 | 0.9950 |
| 0.0172 | 8.0 | 154 | 0.1032 | 0.9713 | 0.9713 | 0.9715 | 0.9718 | 0.9928 |
| 0.0169 | 8.99 | 173 | 0.0854 | 0.9776 | 0.9772 | 0.9771 | 0.9774 | 0.9944 |
| 0.0104 | 9.87 | 190 | 0.0948 | 0.9753 | 0.9750 | 0.9749 | 0.9753 | 0.9938 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
Abhilashvj/CIRCL_website_classifier_test
|
# Model Card for Model ID
<!-- This model can be used to classify website screenshots. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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 Data 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 Data 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]
|
[
"forum",
"general",
"marketplace"
] |
platzi/platzi-vit-model-joel-orellana
|
<!-- 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-joel-orellana
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0283
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1313 | 3.85 | 500 | 0.0283 | 0.9925 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
larkhoon/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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_12
|
<!-- 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-brain-tumor-final_12
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0789
- Accuracy: 0.9760
- F1 Score: 0.9761
- Precision: 0.9762
- Sensitivity: 0.9762
- Specificity: 0.9940
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 0.3644 | 1.0 | 30 | 0.2918 | 0.8955 | 0.8974 | 0.9070 | 0.8957 | 0.9734 |
| 0.2177 | 2.0 | 60 | 0.2319 | 0.9152 | 0.9155 | 0.9237 | 0.9156 | 0.9786 |
| 0.1171 | 3.0 | 90 | 0.1654 | 0.9489 | 0.9494 | 0.9532 | 0.9492 | 0.9872 |
| 0.068 | 4.0 | 120 | 0.1600 | 0.9450 | 0.9451 | 0.9466 | 0.9455 | 0.9861 |
| 0.0499 | 5.0 | 150 | 0.0947 | 0.9654 | 0.9656 | 0.9656 | 0.9657 | 0.9913 |
| 0.0302 | 6.0 | 180 | 0.0882 | 0.9713 | 0.9714 | 0.9715 | 0.9715 | 0.9928 |
| 0.0207 | 7.0 | 210 | 0.1002 | 0.9698 | 0.9699 | 0.9708 | 0.9699 | 0.9924 |
| 0.0205 | 8.0 | 240 | 0.1550 | 0.9525 | 0.9521 | 0.9544 | 0.9529 | 0.9881 |
| 0.0163 | 9.0 | 270 | 0.0789 | 0.9760 | 0.9761 | 0.9762 | 0.9762 | 0.9940 |
| 0.0181 | 10.0 | 300 | 0.0923 | 0.9737 | 0.9737 | 0.9740 | 0.9738 | 0.9934 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
platzi/platzi-vit-model-luis-reyes
|
<!-- 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-luis-reyes
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0661
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1322 | 3.85 | 500 | 0.0661 | 0.9850 |
### Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
edramos/swin-tiny-patch4-window7-224-finetuned-baseplates
|
<!-- 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-baseplates
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: 0.1492
- Accuracy: 0.9569
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0016 | 0.99 | 34 | 0.5869 | 0.8090 |
| 0.4455 | 1.99 | 68 | 0.2308 | 0.9405 |
| 0.321 | 2.98 | 102 | 0.1492 | 0.9569 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"class1",
"class2",
"class3",
"class4",
"class5",
"class6"
] |
amjadfqs/swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_13
|
<!-- 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-brain-tumor-final_13
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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0796
- Accuracy: 0.9784
- F1 Score: 0.9786
- Precision: 0.9787
- Sensitivity: 0.9790
- Specificity: 0.9946
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.2276 | 0.99 | 19 | 0.5721 | 0.7891 | 0.7955 | 0.8401 | 0.7933 | 0.9454 |
| 0.3873 | 1.97 | 38 | 0.2399 | 0.9195 | 0.9195 | 0.9207 | 0.9224 | 0.9796 |
| 0.1287 | 2.96 | 57 | 0.2204 | 0.9230 | 0.9237 | 0.9275 | 0.9261 | 0.9806 |
| 0.0882 | 4.0 | 77 | 0.1026 | 0.9647 | 0.9649 | 0.9647 | 0.9656 | 0.9911 |
| 0.0605 | 4.99 | 96 | 0.0898 | 0.9678 | 0.9683 | 0.9686 | 0.9685 | 0.9919 |
| 0.0439 | 5.97 | 115 | 0.0853 | 0.9741 | 0.9746 | 0.9748 | 0.9747 | 0.9935 |
| 0.0275 | 6.96 | 134 | 0.0941 | 0.9721 | 0.9724 | 0.9730 | 0.9730 | 0.9930 |
| 0.0186 | 8.0 | 154 | 0.0803 | 0.9764 | 0.9767 | 0.9770 | 0.9773 | 0.9941 |
| 0.0165 | 8.99 | 173 | 0.0740 | 0.9780 | 0.9782 | 0.9782 | 0.9786 | 0.9945 |
| 0.0106 | 9.87 | 190 | 0.0796 | 0.9784 | 0.9786 | 0.9787 | 0.9790 | 0.9946 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"glioma",
"meningioma",
"notumor",
"pituitary"
] |
platzi/platzi-vit-model-andres-arboleda
|
<!-- 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-andres-arboleda
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0152
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1312 | 3.85 | 500 | 0.0152 | 1.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
jonglet/mobile_vit
|
<!-- 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. -->
# mobile_vit
This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1128
- Accuracy: 0.7615
## 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: 200
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5859 | 0.78 | 1000 | 0.9741 | 0.7787 |
| 0.1195 | 1.56 | 2000 | 1.1128 | 0.7615 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
[
"1",
"b",
"p",
"q",
"r",
"k",
"n",
"p",
"q",
"r",
"b",
"k",
"n"
] |
hegz/autotrain-image-class-darsh-62976135295
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 62976135295
- CO2 Emissions (in grams): 0.1725
## Validation Metrics
- Loss: 0.003
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000
|
[
"abnormal",
"normal"
] |
willfox/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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
JoseVilla/cfe-telmex-classification-finetuned-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. -->
# cfe-telmex-classification-finetuned-v2
This model is a fine-tuned version of [JoseVilla/cfe-telmex-classification-finetuned-v1](https://huggingface.co/JoseVilla/cfe-telmex-classification-finetuned-v1) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1568
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.3749 | 0.7586 |
| No log | 2.0 | 4 | 0.1568 | 1.0 |
| No log | 3.0 | 6 | 0.0495 | 1.0 |
| No log | 4.0 | 8 | 0.0188 | 1.0 |
| 0.136 | 5.0 | 10 | 0.0087 | 1.0 |
| 0.136 | 6.0 | 12 | 0.0060 | 1.0 |
| 0.136 | 7.0 | 14 | 0.0063 | 1.0 |
| 0.136 | 8.0 | 16 | 0.0039 | 1.0 |
| 0.136 | 9.0 | 18 | 0.0018 | 1.0 |
| 0.0129 | 10.0 | 20 | 0.0015 | 1.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"cfe",
"telmex"
] |
Richardol1219/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 food101 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 4.28.0
- Pytorch 1.13.1+cpu
- Datasets 2.12.0
- 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"
] |
jjanixe/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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1332
- Accuracy: 0.9572
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6031 | 1.0 | 88 | 0.2208 | 0.9282 |
| 0.4569 | 2.0 | 176 | 0.1426 | 0.953 |
| 0.3886 | 3.0 | 264 | 0.1332 | 0.9572 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
jagriti/swin-tiny-patch4-window7-224
|
<!-- 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
This model is a fine-tuned version of [jagriti/swin-tiny-patch4-window7-224](https://huggingface.co/jagriti/swin-tiny-patch4-window7-224) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0907
- Accuracy: 0.9694
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4991 | 1.0 | 351 | 0.1543 | 0.947 |
| 0.346 | 2.0 | 703 | 0.1023 | 0.964 |
| 0.3329 | 2.99 | 1053 | 0.0907 | 0.9694 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck"
] |
Soulaimen/convnext-large-224-22k-1k-BottomSportsCasual
|
<!-- 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-large-224-22k-1k-BottomSportsCasual
This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0051
- 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0732 | 1.0 | 141 | 0.0117 | 0.9943 |
| 0.0664 | 2.0 | 283 | 0.0051 | 1.0 |
| 0.065 | 2.99 | 424 | 0.0047 | 0.9989 |
| 0.0635 | 4.0 | 566 | 0.0029 | 1.0 |
| 0.0357 | 4.99 | 707 | 0.0048 | 0.9966 |
| 0.0826 | 6.0 | 849 | 0.0029 | 0.9989 |
| 0.0177 | 7.0 | 991 | 0.0018 | 1.0 |
| 0.0353 | 8.0 | 1132 | 0.0007 | 1.0 |
| 0.0243 | 9.0 | 1274 | 0.0003 | 1.0 |
| 0.0259 | 9.96 | 1410 | 0.0004 | 1.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"casual",
"sport"
] |
platzi/platzi-vit-model-miguel-solarte
|
<!-- 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-miguel-solarte
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0585
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.14 | 3.85 | 500 | 0.0585 | 0.9850 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
cvint91/swin-tiny-patch4-window7-224-attempt-1
|
<!-- 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-attempt-1
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: 0.0248
- F1: 0.9863
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0762 | 0.99 | 33 | 0.0468 | 0.9807 |
| 0.0915 | 1.98 | 66 | 0.0309 | 0.9836 |
| 0.0707 | 2.98 | 99 | 0.0248 | 0.9863 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"0",
"1"
] |
platzi/platzi-vit-model-rafa-rivera
|
<!-- 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-rafa-rivera
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0750
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0014 | 3.85 | 500 | 0.0750 | 0.9850 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
kpbth/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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6393
- Accuracy: 0.901
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7424 | 0.99 | 62 | 2.5822 | 0.829 |
| 1.8776 | 2.0 | 125 | 1.8066 | 0.873 |
| 1.6558 | 2.98 | 186 | 1.6393 | 0.901 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- 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"
] |
SaeedAnas/vit-base-trees
|
<!-- 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-trees
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 trees dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0054
- 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: 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
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"pepper tree",
"weeping willow"
] |
jkrperson/Beit-for-rice-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. -->
# Beit-for-rice-disease
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1687
- Accuracy: 0.95
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4305 | 0.16 | 10 | 2.0142 | 0.5 |
| 1.804 | 0.32 | 20 | 1.5097 | 0.5446 |
| 1.2088 | 0.48 | 30 | 1.0185 | 0.6786 |
| 0.9168 | 0.63 | 40 | 0.8841 | 0.7768 |
| 0.7893 | 0.79 | 50 | 0.7512 | 0.7768 |
| 0.6253 | 0.95 | 60 | 0.9253 | 0.7411 |
| 0.4949 | 1.11 | 70 | 0.5854 | 0.8214 |
| 0.3272 | 1.27 | 80 | 0.5379 | 0.8393 |
| 0.3753 | 1.43 | 90 | 0.5194 | 0.8571 |
| 0.3378 | 1.59 | 100 | 0.3831 | 0.8661 |
| 0.3643 | 1.75 | 110 | 0.4066 | 0.8661 |
| 0.3413 | 1.9 | 120 | 0.4268 | 0.875 |
| 0.3278 | 2.06 | 130 | 0.4754 | 0.8482 |
| 0.1784 | 2.22 | 140 | 0.4979 | 0.8839 |
| 0.2002 | 2.38 | 150 | 0.3606 | 0.8929 |
| 0.1224 | 2.54 | 160 | 0.3852 | 0.8839 |
| 0.2458 | 2.7 | 170 | 0.3492 | 0.9107 |
| 0.1816 | 2.86 | 180 | 0.3506 | 0.8839 |
| 0.2194 | 3.02 | 190 | 0.3255 | 0.9018 |
| 0.1582 | 3.17 | 200 | 0.4486 | 0.875 |
| 0.2072 | 3.33 | 210 | 0.3760 | 0.8929 |
| 0.1317 | 3.49 | 220 | 0.3021 | 0.9286 |
| 0.1223 | 3.65 | 230 | 0.3867 | 0.9018 |
| 0.1543 | 3.81 | 240 | 0.2640 | 0.9286 |
| 0.1256 | 3.97 | 250 | 0.2925 | 0.9196 |
| 0.095 | 4.13 | 260 | 0.2909 | 0.9107 |
| 0.0578 | 4.29 | 270 | 0.2825 | 0.9196 |
| 0.0428 | 4.44 | 280 | 0.2826 | 0.9196 |
| 0.1743 | 4.6 | 290 | 0.2746 | 0.9196 |
| 0.0659 | 4.76 | 300 | 0.3457 | 0.8929 |
| 0.0871 | 4.92 | 310 | 0.2902 | 0.9196 |
| 0.0726 | 5.08 | 320 | 0.2810 | 0.9196 |
| 0.0556 | 5.24 | 330 | 0.3244 | 0.9107 |
| 0.0629 | 5.4 | 340 | 0.2812 | 0.9286 |
| 0.0517 | 5.56 | 350 | 0.2638 | 0.9464 |
| 0.078 | 5.71 | 360 | 0.2580 | 0.9554 |
| 0.0376 | 5.87 | 370 | 0.2375 | 0.9464 |
| 0.0262 | 6.03 | 380 | 0.2274 | 0.9464 |
| 0.0582 | 6.19 | 390 | 0.2254 | 0.9643 |
| 0.0215 | 6.35 | 400 | 0.2545 | 0.9375 |
| 0.0379 | 6.51 | 410 | 0.2925 | 0.9286 |
| 0.0664 | 6.67 | 420 | 0.2842 | 0.9196 |
| 0.0375 | 6.83 | 430 | 0.2643 | 0.9375 |
| 0.0088 | 6.98 | 440 | 0.2401 | 0.9375 |
| 0.0299 | 7.14 | 450 | 0.2634 | 0.9286 |
| 0.0199 | 7.3 | 460 | 0.2800 | 0.9286 |
| 0.0182 | 7.46 | 470 | 0.2996 | 0.9107 |
| 0.0199 | 7.62 | 480 | 0.2813 | 0.9286 |
| 0.0189 | 7.78 | 490 | 0.2571 | 0.9286 |
| 0.053 | 7.94 | 500 | 0.2260 | 0.9196 |
| 0.0448 | 8.1 | 510 | 0.2112 | 0.9464 |
| 0.0072 | 8.25 | 520 | 0.2133 | 0.9286 |
| 0.0112 | 8.41 | 530 | 0.2149 | 0.9464 |
| 0.021 | 8.57 | 540 | 0.2189 | 0.9286 |
| 0.0073 | 8.73 | 550 | 0.2397 | 0.9375 |
| 0.0128 | 8.89 | 560 | 0.2578 | 0.9375 |
| 0.005 | 9.05 | 570 | 0.2578 | 0.9375 |
| 0.0246 | 9.21 | 580 | 0.2485 | 0.9286 |
| 0.0073 | 9.37 | 590 | 0.2460 | 0.9286 |
| 0.005 | 9.52 | 600 | 0.2429 | 0.9286 |
| 0.0083 | 9.68 | 610 | 0.2362 | 0.9286 |
| 0.0114 | 9.84 | 620 | 0.2338 | 0.9286 |
| 0.0031 | 10.0 | 630 | 0.2327 | 0.9286 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
[
"bacterial_leaf_blight",
"bacterial_leaf_streak",
"bakanae",
"brown_spot",
"grassy_stunt_virus",
"healthy_rice_plant",
"narrow_brown_spot",
"ragged_stunt_virus",
"rice_blast",
"rice_false_smut",
"sheath_blight",
"sheath_rot",
"stem_rot",
"tungro_virus"
] |
InklingSutra/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 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
platzi/platzi-vit-model-santiago
|
<!-- 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-santiago
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0180
- Accuracy: 0.4812
## 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: 0.002
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0972 | 3.85 | 500 | 1.0180 | 0.4812 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
Carina124/plant-vit-model-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. -->
# plant-vit-model-2
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: 0.0402
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2898 | 1.0 | 83 | 0.2431 | 0.9973 | 0.9973 | 0.9973 | 0.9973 |
| 0.2255 | 2.0 | 166 | 0.1596 | 0.9963 | 0.9963 | 0.9963 | 0.9963 |
| 0.1622 | 3.0 | 249 | 0.1105 | 0.9973 | 0.9973 | 0.9973 | 0.9973 |
| 0.1261 | 4.0 | 332 | 0.0887 | 0.9963 | 0.9963 | 0.9963 | 0.9963 |
| 0.1009 | 5.0 | 415 | 0.0645 | 0.9989 | 0.9989 | 0.9989 | 0.9989 |
| 0.0884 | 6.0 | 498 | 0.0554 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0776 | 7.0 | 581 | 0.0489 | 0.9995 | 0.9995 | 0.9995 | 0.9995 |
| 0.0682 | 8.0 | 664 | 0.0447 | 0.9995 | 0.9995 | 0.9995 | 0.9995 |
| 0.0664 | 9.0 | 747 | 0.0411 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0532 | 10.0 | 830 | 0.0402 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
[
"apple___apple_scab",
"apple___black_rot",
"apple___cedar_apple_rust",
"apple___healthy",
"blueberry___healthy",
"grape___black_rot",
"grape___healthy",
"peach___bacterial_spot",
"peach___healthy",
"potato___early_blight",
"potato___late_blight",
"potato___healthy",
"raspberry___healthy",
"soybean___healthy"
] |
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