model_id
stringlengths 7
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|---|---|---|
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand0-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand0-aligned_unaugmentation
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: 0.0169
- Accuracy: 0.9943
- Precision: 0.9972
- Recall: 0.9965
- F1: 0.9969
- Roc Auc: 0.9993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0403 | 1.0 | 1266 | 0.0169 | 0.9943 | 0.9972 | 0.9965 | 0.9969 | 0.9993 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
pavanavn/vit-base-patch16-224-9models
|
<!-- 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-patch16-224-9models
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: 0.0167
- Accuracy: 0.9959
## 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: 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5952 | 0.9790 | 35 | 0.2206 | 0.9344 |
| 0.1228 | 1.9860 | 71 | 0.0889 | 0.9754 |
| 0.1133 | 2.9930 | 107 | 0.0701 | 0.9816 |
| 0.0877 | 4.0 | 143 | 0.0808 | 0.9754 |
| 0.0597 | 4.9790 | 178 | 0.0234 | 0.9939 |
| 0.0718 | 5.9860 | 214 | 0.0325 | 0.9898 |
| 0.0666 | 6.9930 | 250 | 0.0459 | 0.9836 |
| 0.0467 | 8.0 | 286 | 0.0162 | 0.9959 |
| 0.0446 | 8.9790 | 321 | 0.0155 | 0.9959 |
| 0.0391 | 9.7902 | 350 | 0.0167 | 0.9959 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"formula1",
"conquest",
"bigbang",
"manufacture",
"diastarsorginal",
"chronomat",
"instruments",
"epicx",
"speedmaster"
] |
universalml/gsg
|
<!-- 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. -->
# gsg
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"cat",
"dog"
] |
not-lain/cloth_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cloth_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2829
- Accuracy: 0.6395
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2765 | 1.0 | 3270 | 1.2829 | 0.6395 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"anorak",
"blazer",
"blouse",
"bomber",
"button-down",
"caftan",
"capris",
"cardigan",
"chinos",
"coat",
"coverup",
"culottes",
"cutoffs",
"dress",
"flannel",
"gauchos",
"halter",
"henley",
"hoodie",
"jacket",
"jeans",
"jeggings",
"jersey",
"jodhpurs",
"joggers",
"jumpsuit",
"kaftan",
"kimono",
"leggings",
"onesie",
"parka",
"peacoat",
"poncho",
"robe",
"romper",
"sarong",
"shorts",
"skirt",
"sweater",
"sweatpants",
"sweatshorts",
"tank",
"tee",
"top",
"trunks",
"turtleneck"
] |
jhoppanne/Dogs-Breed-Image-Classification-V0
|
<!-- 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. -->
# Dogs-Breed-Image-Classification-V0
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: 1.8210
- Accuracy: 0.7444
## Model description
This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset)
Quotes from the website:
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.
citation:
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
Secondary:
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
## Intended uses & limitations
This model is fined tune solely for classifiying 120 species of dogs.
## Training and evaluation data
75% training data, 25% testing data.
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 13.4902 | 1.0 | 515 | 4.7822 | 0.0104 |
| 4.7159 | 2.0 | 1030 | 4.6822 | 0.0323 |
| 4.6143 | 3.0 | 1545 | 4.5940 | 0.0554 |
| 4.4855 | 4.0 | 2060 | 4.5027 | 0.0935 |
| 4.36 | 5.0 | 2575 | 4.3961 | 0.1239 |
| 4.2198 | 6.0 | 3090 | 4.3112 | 0.1528 |
| 4.0882 | 7.0 | 3605 | 4.1669 | 0.1747 |
| 3.9314 | 8.0 | 4120 | 4.0775 | 0.2021 |
| 3.7863 | 9.0 | 4635 | 3.9487 | 0.2310 |
| 3.6511 | 10.0 | 5150 | 3.9028 | 0.2466 |
| 3.5168 | 11.0 | 5665 | 3.8635 | 0.2626 |
| 3.3999 | 12.0 | 6180 | 3.7550 | 0.2767 |
| 3.3037 | 13.0 | 6695 | 3.6973 | 0.2884 |
| 3.1613 | 14.0 | 7210 | 3.6315 | 0.3037 |
| 3.0754 | 15.0 | 7725 | 3.4839 | 0.3188 |
| 2.9441 | 16.0 | 8240 | 3.4406 | 0.3302 |
| 2.8579 | 17.0 | 8755 | 3.3528 | 0.3406 |
| 2.7531 | 18.0 | 9270 | 3.3132 | 0.3472 |
| 2.6477 | 19.0 | 9785 | 3.2736 | 0.3567 |
| 2.5422 | 20.0 | 10300 | 3.1950 | 0.3756 |
| 2.4629 | 21.0 | 10815 | 3.1174 | 0.4004 |
| 2.3735 | 22.0 | 11330 | 2.9916 | 0.4225 |
| 2.2436 | 23.0 | 11845 | 2.9205 | 0.4509 |
| 2.1578 | 24.0 | 12360 | 2.9197 | 0.4689 |
| 2.0671 | 25.0 | 12875 | 2.8196 | 0.4866 |
| 1.9902 | 26.0 | 13390 | 2.7117 | 0.4961 |
| 1.8737 | 27.0 | 13905 | 2.7129 | 0.5078 |
| 1.7945 | 28.0 | 14420 | 2.6654 | 0.5143 |
| 1.7092 | 29.0 | 14935 | 2.6273 | 0.5301 |
| 1.6228 | 30.0 | 15450 | 2.5407 | 0.5454 |
| 1.5744 | 31.0 | 15965 | 2.5412 | 0.5559 |
| 1.4761 | 32.0 | 16480 | 2.4658 | 0.5658 |
| 1.4084 | 33.0 | 16995 | 2.4247 | 0.5673 |
| 1.2624 | 34.0 | 17510 | 2.3766 | 0.5758 |
| 1.2066 | 35.0 | 18025 | 2.2879 | 0.5843 |
| 1.124 | 36.0 | 18540 | 2.2039 | 0.5872 |
| 1.074 | 37.0 | 19055 | 2.2469 | 0.5965 |
| 0.9937 | 38.0 | 19570 | 2.1575 | 0.6011 |
| 0.9418 | 39.0 | 20085 | 2.0854 | 0.6122 |
| 0.8812 | 40.0 | 20600 | 1.9991 | 0.6254 |
| 0.819 | 41.0 | 21115 | 2.0161 | 0.6312 |
| 0.771 | 42.0 | 21630 | 1.9253 | 0.6375 |
| 0.7128 | 43.0 | 22145 | 1.9412 | 0.6390 |
| 0.6434 | 44.0 | 22660 | 1.8463 | 0.6509 |
| 0.6138 | 45.0 | 23175 | 1.8163 | 0.6650 |
| 0.5325 | 46.0 | 23690 | 1.7881 | 0.6710 |
| 0.498 | 47.0 | 24205 | 1.7526 | 0.6744 |
| 0.4565 | 48.0 | 24720 | 1.7155 | 0.6859 |
| 0.4109 | 49.0 | 25235 | 1.6874 | 0.6946 |
| 0.3681 | 50.0 | 25750 | 1.7386 | 0.6997 |
| 0.3306 | 51.0 | 26265 | 1.6578 | 0.7104 |
| 0.2913 | 52.0 | 26780 | 1.6641 | 0.7104 |
| 0.2598 | 53.0 | 27295 | 1.6823 | 0.7162 |
| 0.2311 | 54.0 | 27810 | 1.6835 | 0.7157 |
| 0.2115 | 55.0 | 28325 | 1.6581 | 0.7206 |
| 0.1843 | 56.0 | 28840 | 1.6286 | 0.7274 |
| 0.1668 | 57.0 | 29355 | 1.6358 | 0.7225 |
| 0.1483 | 58.0 | 29870 | 1.6422 | 0.7250 |
| 0.132 | 59.0 | 30385 | 1.6618 | 0.7284 |
| 0.1164 | 60.0 | 30900 | 1.6894 | 0.7262 |
| 0.1043 | 61.0 | 31415 | 1.6923 | 0.7276 |
| 0.0937 | 62.0 | 31930 | 1.6627 | 0.7323 |
| 0.0826 | 63.0 | 32445 | 1.6280 | 0.7342 |
| 0.0743 | 64.0 | 32960 | 1.6204 | 0.7366 |
| 0.0638 | 65.0 | 33475 | 1.6890 | 0.7383 |
| 0.0603 | 66.0 | 33990 | 1.6967 | 0.7335 |
| 0.0491 | 67.0 | 34505 | 1.6975 | 0.7306 |
| 0.0459 | 68.0 | 35020 | 1.7242 | 0.7337 |
| 0.0416 | 69.0 | 35535 | 1.7019 | 0.7374 |
| 0.0382 | 70.0 | 36050 | 1.7098 | 0.7381 |
| 0.0378 | 71.0 | 36565 | 1.7188 | 0.7383 |
| 0.0326 | 72.0 | 37080 | 1.8212 | 0.7376 |
| 0.0323 | 73.0 | 37595 | 1.7965 | 0.7393 |
| 0.0299 | 74.0 | 38110 | 1.7934 | 0.7301 |
| 0.0259 | 75.0 | 38625 | 1.7799 | 0.7335 |
| 0.0276 | 76.0 | 39140 | 1.8456 | 0.7301 |
| 0.0257 | 77.0 | 39655 | 1.8551 | 0.7391 |
| 0.0234 | 78.0 | 40170 | 1.7780 | 0.7391 |
| 0.0222 | 79.0 | 40685 | 1.8216 | 0.7362 |
| 0.0195 | 80.0 | 41200 | 1.8333 | 0.7352 |
| 0.0214 | 81.0 | 41715 | 1.8526 | 0.7430 |
| 0.0207 | 82.0 | 42230 | 1.8581 | 0.7364 |
| 0.0171 | 83.0 | 42745 | 1.8329 | 0.7393 |
| 0.0175 | 84.0 | 43260 | 1.8841 | 0.7396 |
| 0.0165 | 85.0 | 43775 | 1.8381 | 0.7345 |
| 0.0152 | 86.0 | 44290 | 1.8192 | 0.7379 |
| 0.0168 | 87.0 | 44805 | 1.8538 | 0.7388 |
| 0.0158 | 88.0 | 45320 | 1.8390 | 0.7371 |
| 0.0181 | 89.0 | 45835 | 1.8555 | 0.7374 |
| 0.0142 | 90.0 | 46350 | 1.7987 | 0.7352 |
| 0.0147 | 91.0 | 46865 | 1.8446 | 0.7427 |
| 0.0142 | 92.0 | 47380 | 1.8210 | 0.7444 |
| 0.0124 | 93.0 | 47895 | 1.8233 | 0.7405 |
| 0.0128 | 94.0 | 48410 | 1.8517 | 0.7393 |
| 0.0135 | 95.0 | 48925 | 1.8408 | 0.7413 |
| 0.0122 | 96.0 | 49440 | 1.8153 | 0.7396 |
| 0.0141 | 97.0 | 49955 | 1.8645 | 0.7432 |
| 0.0121 | 98.0 | 50470 | 1.8526 | 0.7430 |
| 0.0124 | 99.0 | 50985 | 1.8693 | 0.7388 |
| 0.0113 | 100.0 | 51500 | 1.8051 | 0.7427 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1
|
[
"afghan_hound",
"african_hunting_dog",
"border_terrier",
"kuvasz",
"malamute",
"malinois",
"miniature_pinscher",
"miniature_poodle",
"miniature_schnauzer",
"otterhound",
"papillon",
"pug",
"redbone",
"boston_bull",
"schipperke",
"silky_terrier",
"soft",
"standard_poodle",
"standard_schnauzer",
"toy_poodle",
"toy_terrier",
"vizsla",
"whippet",
"wire",
"bouvier_des_flandres",
"brabancon_griffon",
"brittany_spaniel",
"cardigan",
"chesapeake_bay_retriever",
"chihuahua",
"dandie_dinmont",
"doberman",
"airedale",
"english_foxhound",
"english_setter",
"english_springer",
"entlebucher",
"eskimo_dog",
"french_bulldog",
"german_shepherd",
"german_short",
"gordon_setter",
"great_dane",
"american_staffordshire_terrier",
"great_pyrenees",
"greater_swiss_mountain_dog",
"ibizan_hound",
"irish_setter",
"irish_terrier",
"irish_water_spaniel",
"irish_wolfhound",
"italian_greyhound",
"japanese_spaniel",
"kerry_blue_terrier",
"appenzeller",
"labrador_retriever",
"lakeland_terrier",
"leonberg",
"lhasa",
"maltese_dog",
"mexican_hairless",
"newfoundland",
"norfolk_terrier",
"norwegian_elkhound",
"norwich_terrier",
"australian_terrier",
"old_english_sheepdog",
"pekinese",
"pembroke",
"pomeranian",
"rhodesian_ridgeback",
"rottweiler",
"saint_bernard",
"saluki",
"samoyed",
"scotch_terrier",
"bedlington_terrier",
"scottish_deerhound",
"sealyham_terrier",
"shetland_sheepdog",
"shih",
"siberian_husky",
"staffordshire_bullterrier",
"sussex_spaniel",
"tibetan_mastiff",
"tibetan_terrier",
"walker_hound",
"bernese_mountain_dog",
"weimaraner",
"welsh_springer_spaniel",
"west_highland_white_terrier",
"yorkshire_terrier",
"affenpinscher",
"basenji",
"basset",
"beagle",
"black",
"bloodhound",
"blenheim_spaniel",
"bluetick",
"borzoi",
"boxer",
"briard",
"bull_mastiff",
"cairn",
"chow",
"clumber",
"cocker_spaniel",
"collie",
"border_collie",
"curly",
"dhole",
"dingo",
"flat",
"giant_schnauzer",
"golden_retriever",
"groenendael",
"keeshond",
"kelpie",
"komondor"
] |
AZIIIIIIIIZ/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0675
- Accuracy: 0.9765
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2428 | 1.0 | 537 | 0.1768 | 0.9302 |
| 0.1877 | 2.0 | 1074 | 0.1141 | 0.9551 |
| 0.1574 | 3.0 | 1611 | 0.1359 | 0.9461 |
| 0.1412 | 4.0 | 2148 | 0.1245 | 0.9522 |
| 0.1289 | 5.0 | 2685 | 0.0774 | 0.9705 |
| 0.1116 | 6.0 | 3222 | 0.0889 | 0.9663 |
| 0.1091 | 7.0 | 3759 | 0.0800 | 0.9717 |
| 0.1096 | 8.0 | 4296 | 0.0665 | 0.9757 |
| 0.0996 | 9.0 | 4833 | 0.0708 | 0.9747 |
| 0.0992 | 10.0 | 5370 | 0.0675 | 0.9765 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"aiartdata",
"realart"
] |
anindyady/REASSTYP_CNN_Project
|
<!-- 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. -->
# REASSTYP_CNN_Project
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: 1.3870
- Accuracy: 0.895
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7917 | 0.992 | 62 | 2.6043 | 0.803 |
| 1.7522 | 2.0 | 125 | 1.6939 | 0.875 |
| 1.382 | 2.992 | 187 | 1.3878 | 0.905 |
| 1.2581 | 3.968 | 248 | 1.3110 | 0.905 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
jhoppanne/Dogs-Breed-Image-Classification-V1
|
<!-- 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. -->
# Dogs-Breed-Image-Classification-V1
This model is a fine-tuned version of [microsoft/resnet-101](https://huggingface.co/microsoft/resnet-101) on the [Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.).
It achieves the following results on the evaluation set:
- Loss: 0.4469
- Accuracy: 0.8758
## Model description
[Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0)
This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset)
Quotes from the website:
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.
citation:
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
Secondary:
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
## Intended uses & limitations
This model is fined tune solely for classifiying 120 species of dogs.
## Training and evaluation data
75% training data, 25% testing data.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 309 | 18.7685 | 0.0091 |
| 18.7211 | 2.0 | 618 | 18.5975 | 0.0091 |
| 18.7211 | 3.0 | 927 | 17.4087 | 0.0091 |
| 15.4274 | 4.0 | 1236 | 11.8712 | 0.0091 |
| 10.3252 | 5.0 | 1545 | 6.6642 | 0.0091 |
| 10.3252 | 6.0 | 1854 | 5.2754 | 0.0112 |
| 6.2268 | 7.0 | 2163 | 4.8454 | 0.0158 |
| 6.2268 | 8.0 | 2472 | 4.7658 | 0.0140 |
| 4.9682 | 9.0 | 2781 | 4.6860 | 0.0234 |
| 4.7245 | 10.0 | 3090 | 4.6165 | 0.0316 |
| 4.7245 | 11.0 | 3399 | 4.5349 | 0.0446 |
| 4.5441 | 12.0 | 3708 | 4.4555 | 0.0623 |
| 4.3912 | 13.0 | 4017 | 4.3437 | 0.0862 |
| 4.3912 | 14.0 | 4326 | 4.2182 | 0.1330 |
| 4.2211 | 15.0 | 4635 | 4.0752 | 0.2153 |
| 4.2211 | 16.0 | 4944 | 3.9803 | 0.2599 |
| 3.9762 | 17.0 | 5253 | 3.7347 | 0.3596 |
| 3.69 | 18.0 | 5562 | 3.5493 | 0.4194 |
| 3.69 | 19.0 | 5871 | 3.3404 | 0.4813 |
| 3.3803 | 20.0 | 6180 | 3.1122 | 0.5600 |
| 3.3803 | 21.0 | 6489 | 2.8656 | 0.6101 |
| 3.0345 | 22.0 | 6798 | 2.6544 | 0.6462 |
| 2.6793 | 23.0 | 7107 | 2.4178 | 0.6647 |
| 2.6793 | 24.0 | 7416 | 2.1967 | 0.7121 |
| 2.3251 | 25.0 | 7725 | 2.0091 | 0.7203 |
| 1.9975 | 26.0 | 8034 | 1.8189 | 0.7464 |
| 1.9975 | 27.0 | 8343 | 1.6537 | 0.7519 |
| 1.7009 | 28.0 | 8652 | 1.4413 | 0.7880 |
| 1.7009 | 29.0 | 8961 | 1.3137 | 0.7968 |
| 1.4494 | 30.0 | 9270 | 1.2150 | 0.7929 |
| 1.2389 | 31.0 | 9579 | 1.1238 | 0.8041 |
| 1.2389 | 32.0 | 9888 | 1.0215 | 0.8208 |
| 1.0646 | 33.0 | 10197 | 0.9637 | 0.8190 |
| 0.9319 | 34.0 | 10506 | 0.8891 | 0.8299 |
| 0.9319 | 35.0 | 10815 | 0.8520 | 0.8330 |
| 0.8297 | 36.0 | 11124 | 0.8212 | 0.8400 |
| 0.8297 | 37.0 | 11433 | 0.7579 | 0.8415 |
| 0.7293 | 38.0 | 11742 | 0.7254 | 0.8454 |
| 0.6657 | 39.0 | 12051 | 0.7019 | 0.8457 |
| 0.6657 | 40.0 | 12360 | 0.6669 | 0.8527 |
| 0.6047 | 41.0 | 12669 | 0.6510 | 0.8530 |
| 0.6047 | 42.0 | 12978 | 0.6264 | 0.8545 |
| 0.557 | 43.0 | 13287 | 0.6275 | 0.8506 |
| 0.5126 | 44.0 | 13596 | 0.5947 | 0.8536 |
| 0.5126 | 45.0 | 13905 | 0.5860 | 0.8573 |
| 0.475 | 46.0 | 14214 | 0.5745 | 0.8545 |
| 0.4406 | 47.0 | 14523 | 0.5579 | 0.8600 |
| 0.4406 | 48.0 | 14832 | 0.5386 | 0.8621 |
| 0.4086 | 49.0 | 15141 | 0.5346 | 0.8624 |
| 0.4086 | 50.0 | 15450 | 0.5200 | 0.8612 |
| 0.3882 | 51.0 | 15759 | 0.5233 | 0.8612 |
| 0.3646 | 52.0 | 16068 | 0.5148 | 0.8640 |
| 0.3646 | 53.0 | 16377 | 0.5078 | 0.8679 |
| 0.3386 | 54.0 | 16686 | 0.5067 | 0.8646 |
| 0.3386 | 55.0 | 16995 | 0.4976 | 0.8673 |
| 0.3208 | 56.0 | 17304 | 0.4934 | 0.8682 |
| 0.3039 | 57.0 | 17613 | 0.4849 | 0.8688 |
| 0.3039 | 58.0 | 17922 | 0.4930 | 0.8691 |
| 0.2915 | 59.0 | 18231 | 0.4867 | 0.8655 |
| 0.2784 | 60.0 | 18540 | 0.4832 | 0.8679 |
| 0.2784 | 61.0 | 18849 | 0.4785 | 0.8670 |
| 0.2597 | 62.0 | 19158 | 0.4753 | 0.8685 |
| 0.2597 | 63.0 | 19467 | 0.4701 | 0.8712 |
| 0.2488 | 64.0 | 19776 | 0.4766 | 0.8697 |
| 0.2426 | 65.0 | 20085 | 0.4726 | 0.8700 |
| 0.2426 | 66.0 | 20394 | 0.4670 | 0.8694 |
| 0.2261 | 67.0 | 20703 | 0.4624 | 0.8722 |
| 0.2252 | 68.0 | 21012 | 0.4631 | 0.8718 |
| 0.2252 | 69.0 | 21321 | 0.4702 | 0.8670 |
| 0.2116 | 70.0 | 21630 | 0.4629 | 0.8715 |
| 0.2116 | 71.0 | 21939 | 0.4650 | 0.8685 |
| 0.2032 | 72.0 | 22248 | 0.4670 | 0.8673 |
| 0.2035 | 73.0 | 22557 | 0.4565 | 0.8670 |
| 0.2035 | 74.0 | 22866 | 0.4550 | 0.8697 |
| 0.19 | 75.0 | 23175 | 0.4544 | 0.8706 |
| 0.19 | 76.0 | 23484 | 0.4483 | 0.8670 |
| 0.1833 | 77.0 | 23793 | 0.4650 | 0.8694 |
| 0.184 | 78.0 | 24102 | 0.4604 | 0.8709 |
| 0.184 | 79.0 | 24411 | 0.4484 | 0.8697 |
| 0.1728 | 80.0 | 24720 | 0.4469 | 0.8758 |
| 0.1688 | 81.0 | 25029 | 0.4536 | 0.8676 |
| 0.1688 | 82.0 | 25338 | 0.4450 | 0.8709 |
| 0.1674 | 83.0 | 25647 | 0.4530 | 0.8691 |
| 0.1674 | 84.0 | 25956 | 0.4532 | 0.8725 |
| 0.1632 | 85.0 | 26265 | 0.4495 | 0.8718 |
| 0.1605 | 86.0 | 26574 | 0.4440 | 0.8673 |
| 0.1605 | 87.0 | 26883 | 0.4504 | 0.8731 |
| 0.1586 | 88.0 | 27192 | 0.4551 | 0.8667 |
| 0.1558 | 89.0 | 27501 | 0.4498 | 0.8670 |
| 0.1558 | 90.0 | 27810 | 0.4516 | 0.8718 |
| 0.1587 | 91.0 | 28119 | 0.4450 | 0.8725 |
| 0.1587 | 92.0 | 28428 | 0.4435 | 0.8706 |
| 0.1505 | 93.0 | 28737 | 0.4459 | 0.8722 |
| 0.1492 | 94.0 | 29046 | 0.4578 | 0.8673 |
| 0.1492 | 95.0 | 29355 | 0.4499 | 0.8725 |
| 0.1459 | 96.0 | 29664 | 0.4494 | 0.8703 |
| 0.1459 | 97.0 | 29973 | 0.4533 | 0.8697 |
| 0.1481 | 98.0 | 30282 | 0.4524 | 0.8652 |
| 0.1477 | 99.0 | 30591 | 0.4496 | 0.8715 |
| 0.1477 | 100.0 | 30900 | 0.4523 | 0.8661 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1
|
[
"afghan_hound",
"african_hunting_dog",
"border_terrier",
"kuvasz",
"malamute",
"malinois",
"miniature_pinscher",
"miniature_poodle",
"miniature_schnauzer",
"otterhound",
"papillon",
"pug",
"redbone",
"boston_bull",
"schipperke",
"silky_terrier",
"soft",
"standard_poodle",
"standard_schnauzer",
"toy_poodle",
"toy_terrier",
"vizsla",
"whippet",
"wire",
"bouvier_des_flandres",
"brabancon_griffon",
"brittany_spaniel",
"cardigan",
"chesapeake_bay_retriever",
"chihuahua",
"dandie_dinmont",
"doberman",
"airedale",
"english_foxhound",
"english_setter",
"english_springer",
"entlebucher",
"eskimo_dog",
"french_bulldog",
"german_shepherd",
"german_short",
"gordon_setter",
"great_dane",
"american_staffordshire_terrier",
"great_pyrenees",
"greater_swiss_mountain_dog",
"ibizan_hound",
"irish_setter",
"irish_terrier",
"irish_water_spaniel",
"irish_wolfhound",
"italian_greyhound",
"japanese_spaniel",
"kerry_blue_terrier",
"appenzeller",
"labrador_retriever",
"lakeland_terrier",
"leonberg",
"lhasa",
"maltese_dog",
"mexican_hairless",
"newfoundland",
"norfolk_terrier",
"norwegian_elkhound",
"norwich_terrier",
"australian_terrier",
"old_english_sheepdog",
"pekinese",
"pembroke",
"pomeranian",
"rhodesian_ridgeback",
"rottweiler",
"saint_bernard",
"saluki",
"samoyed",
"scotch_terrier",
"bedlington_terrier",
"scottish_deerhound",
"sealyham_terrier",
"shetland_sheepdog",
"shih",
"siberian_husky",
"staffordshire_bullterrier",
"sussex_spaniel",
"tibetan_mastiff",
"tibetan_terrier",
"walker_hound",
"bernese_mountain_dog",
"weimaraner",
"welsh_springer_spaniel",
"west_highland_white_terrier",
"yorkshire_terrier",
"affenpinscher",
"basenji",
"basset",
"beagle",
"black",
"bloodhound",
"blenheim_spaniel",
"bluetick",
"borzoi",
"boxer",
"briard",
"bull_mastiff",
"cairn",
"chow",
"clumber",
"cocker_spaniel",
"collie",
"border_collie",
"curly",
"dhole",
"dingo",
"flat",
"giant_schnauzer",
"golden_retriever",
"groenendael",
"keeshond",
"kelpie",
"komondor"
] |
JuanFelCam/swin-base-patch4-window7-224-in22k-finetuned-CT-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. -->
# swin-base-patch4-window7-224-in22k-finetuned-CT-V2
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0038
- Accuracy: 0.9989
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1147 | 0.0393 | 0.9877 |
| No log | 2.0 | 2294 | 0.0201 | 0.9921 |
| No log | 3.0 | 3441 | 0.0089 | 0.9964 |
| No log | 4.0 | 4588 | 0.0071 | 0.9971 |
| 0.1453 | 5.0 | 5735 | 0.0038 | 0.9989 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.15.1
|
[
"meningioma_tumor",
"glioma_tumor",
"no_tumor",
"pituitary_tumor"
] |
AZIIIIIIIIZ/vit-base-patch16-224-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. -->
# vit-base-patch16-224-finetuned-eurosat
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:
- eval_loss: 0.1420
- eval_accuracy: 0.9472
- eval_runtime: 189.3891
- eval_samples_per_second: 80.633
- eval_steps_per_second: 2.524
- epoch: 1.2070
- step: 1296
## 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
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"aiartdata",
"realart"
] |
Salmamoori/Salmamoori-vit-fine-tune-CIFAR-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. -->
# Salmamoori-vit-fine-tune-CIFAR-10
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: 0.0811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0429 | 1.0 | 6250 | 0.0990 |
| 0.0025 | 2.0 | 12500 | 0.1146 |
| 0.0002 | 3.0 | 18750 | 0.0811 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9"
] |
jhoppanne/Dogs-Breed-Image-Classification-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. -->
# Dogs-Breed-Image-Classification-V2
This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the [Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.).
It achieves the following results on the evaluation set:
- Loss: 1.0115
- Accuracy: 0.8408
## Model description
- [Link to the fine-tuned model using resnet-50](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V0)
- [Link to the fine-tuned model using resnet-101](https://huggingface.co/jhoppanne/Dogs-Breed-Image-Classification-V1)
This model was trained using dataset from [Kaggle - Standford dogs dataset](https://www.kaggle.com/datasets/jessicali9530/stanford-dogs-dataset.)
Quotes from the website:
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It was originally collected for fine-grain image categorization, a challenging problem as certain dog breeds have near identical features or differ in colour and age.
citation:
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao and Li Fei-Fei. Novel dataset for Fine-Grained Image Categorization. First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. [pdf] [poster] [BibTex]
Secondary:
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. [pdf] [BibTex]
## Intended uses & limitations
This model is fined tune solely for classifiying 120 species of dogs.
## Training and evaluation data
75% training data, 25% testing data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 483 | 4.6525 | 0.7382 |
| 4.7329 | 2.0 | 966 | 4.3558 | 0.7298 |
| 4.5033 | 3.0 | 1449 | 3.9568 | 0.7471 |
| 4.1405 | 4.0 | 1932 | 3.5160 | 0.7782 |
| 3.7176 | 5.0 | 2415 | 3.0805 | 0.7946 |
| 3.293 | 6.0 | 2898 | 2.6907 | 0.8021 |
| 2.8898 | 7.0 | 3381 | 2.3044 | 0.8126 |
| 2.5343 | 8.0 | 3864 | 2.0091 | 0.8177 |
| 2.2188 | 9.0 | 4347 | 1.7910 | 0.8126 |
| 1.9698 | 10.0 | 4830 | 1.6015 | 0.8194 |
| 1.7532 | 11.0 | 5313 | 1.4383 | 0.8220 |
| 1.586 | 12.0 | 5796 | 1.3355 | 0.8264 |
| 1.4533 | 13.0 | 6279 | 1.2467 | 0.8260 |
| 1.336 | 14.0 | 6762 | 1.1575 | 0.8313 |
| 1.2641 | 15.0 | 7245 | 1.1038 | 0.8321 |
| 1.185 | 16.0 | 7728 | 1.0606 | 0.8395 |
| 1.1329 | 17.0 | 8211 | 1.0178 | 0.8398 |
| 1.0977 | 18.0 | 8694 | 1.0115 | 0.8408 |
| 1.0732 | 19.0 | 9177 | 0.9945 | 0.8381 |
| 1.0508 | 20.0 | 9660 | 0.9930 | 0.8393 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.15.0
- Tokenizers 0.15.1
|
[
"afghan_hound",
"african_hunting_dog",
"border_terrier",
"kuvasz",
"malamute",
"malinois",
"miniature_pinscher",
"miniature_poodle",
"miniature_schnauzer",
"otterhound",
"papillon",
"pug",
"redbone",
"boston_bull",
"schipperke",
"silky_terrier",
"soft",
"standard_poodle",
"standard_schnauzer",
"toy_poodle",
"toy_terrier",
"vizsla",
"whippet",
"wire",
"bouvier_des_flandres",
"brabancon_griffon",
"brittany_spaniel",
"cardigan",
"chesapeake_bay_retriever",
"chihuahua",
"dandie_dinmont",
"doberman",
"airedale",
"english_foxhound",
"english_setter",
"english_springer",
"entlebucher",
"eskimo_dog",
"french_bulldog",
"german_shepherd",
"german_short",
"gordon_setter",
"great_dane",
"american_staffordshire_terrier",
"great_pyrenees",
"greater_swiss_mountain_dog",
"ibizan_hound",
"irish_setter",
"irish_terrier",
"irish_water_spaniel",
"irish_wolfhound",
"italian_greyhound",
"japanese_spaniel",
"kerry_blue_terrier",
"appenzeller",
"labrador_retriever",
"lakeland_terrier",
"leonberg",
"lhasa",
"maltese_dog",
"mexican_hairless",
"newfoundland",
"norfolk_terrier",
"norwegian_elkhound",
"norwich_terrier",
"australian_terrier",
"old_english_sheepdog",
"pekinese",
"pembroke",
"pomeranian",
"rhodesian_ridgeback",
"rottweiler",
"saint_bernard",
"saluki",
"samoyed",
"scotch_terrier",
"bedlington_terrier",
"scottish_deerhound",
"sealyham_terrier",
"shetland_sheepdog",
"shih",
"siberian_husky",
"staffordshire_bullterrier",
"sussex_spaniel",
"tibetan_mastiff",
"tibetan_terrier",
"walker_hound",
"bernese_mountain_dog",
"weimaraner",
"welsh_springer_spaniel",
"west_highland_white_terrier",
"yorkshire_terrier",
"affenpinscher",
"basenji",
"basset",
"beagle",
"black",
"bloodhound",
"blenheim_spaniel",
"bluetick",
"borzoi",
"boxer",
"briard",
"bull_mastiff",
"cairn",
"chow",
"clumber",
"cocker_spaniel",
"collie",
"border_collie",
"curly",
"dhole",
"dingo",
"flat",
"giant_schnauzer",
"golden_retriever",
"groenendael",
"keeshond",
"kelpie",
"komondor"
] |
Salmamoori/vit-fine-tune-CIFAR-10-100Epochs-v1
|
<!-- 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-fine-tune-CIFAR-10-100Epochs-v1
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 CIFAR 10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0361 | 1.0 | 6250 | 0.1444 |
| 0.0075 | 2.0 | 12500 | 0.1456 |
| 0.0006 | 3.0 | 18750 | 0.1818 |
| 0.1435 | 4.0 | 25000 | 0.1572 |
| 0.0002 | 5.0 | 31250 | 0.1389 |
| 0.0004 | 6.0 | 37500 | 0.1366 |
| 0.0 | 7.0 | 43750 | 0.1531 |
| 0.0 | 8.0 | 50000 | 0.1327 |
| 0.0 | 9.0 | 56250 | 0.1483 |
| 0.0 | 10.0 | 62500 | 0.1420 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9"
] |
runaksh/chest_xray_tuberculosis_detection
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"normal",
"tuberculosis"
] |
Jl-wei/app-intro-img-classifier
|
This model is trained to classify app introduction images into three categories: `Surrounded Screenshot`, `Screenshot`, and `Irrelevant`.
Code and dataset can be found at https://github.com/Jl-wei/guing
Using with pipeline
```py
from PIL import Image
from transformers import pipeline
classifier = pipeline("image-classification", model="Jl-wei/app-intro-img-classifier", device=0)
image = Image.open(img_path)
result = classifier(image)
```
This is the app introduction image classifier of the following paper:
```bibtex
@article{wei2024guing,
author = {Wei, Jialiang and Courbis, Anne-Lise and Lambolais, Thomas and Xu, Binbin and Bernard, Pierre Louis and Dray, G\'{e}rard and Maalej, Walid},
title = {GUing: A Mobile GUI Search Engine using a Vision-Language Model},
year = {2025},
volume = {34},
number = {4},
doi = {10.1145/3702993},
journal = {ACM Trans. Softw. Eng. Methodol.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA}
}
```
Please note that the model can only be used for academic purpose.
|
[
"0",
"1",
"2"
] |
JuIm/ViT-Breast-Cancer
|
# ViT-Breast-Cancer
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a dataset of breast cancer microscope slides.
## Model description
This is a fine-tuned ViT (Google) that serves more as an exploration of vision transformers in medicine for my learning than as anything specific. I fine-tuned this model on a dataset of ~7000 images of breast cancer slides labelled as 'benign' or 'cancerous'.
I used the Transformers library and the out-of-the-box ViTForImageClassification configuration.
Despite this being an incredibly barebones fine-tune, I hope you fine it useful! Any recommendations are welcome!
## Intended uses & limitations
This is a super basic fine tuned model. Please evaluate its performance for yourself do determine whether it can be useful for you. In a big picture sense, this model can tell apart benign and cancerous breast tissue samples.
### 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
### Training results
- training_loss = 0.007100
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"benign",
"malignant"
] |
HardlyHumans/hand-drawn-emoji-detection
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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:** Siddharth Singh,Neha Gaonkar,Aum Thaker
-
- **Model type:**
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"angry",
"confused",
"happy",
"normal",
"sad",
"surprised"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand0-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand0-aligned_unaugmentation
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: 0.1068
- Accuracy: 0.9578
- Precision: 0.9542
- Recall: 0.9974
- F1: 0.9753
- Roc Auc: 0.9904
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1402 | 0.9975 | 202 | 0.1068 | 0.9578 | 0.9542 | 0.9974 | 0.9753 | 0.9904 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
ahmedesmail16/0.50-Train-Test-vit-large
|
<!-- 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. -->
# 0.50-Train-Test-vit-large
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8804
- Accuracy: 0.8098
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.3722 | 0.9825 | 14 | 1.8140 | 0.3758 |
| 1.7117 | 1.9649 | 28 | 0.9446 | 0.7383 |
| 0.3741 | 2.9474 | 42 | 0.8083 | 0.7338 |
| 0.1709 | 4.0 | 57 | 0.7460 | 0.7562 |
| 0.0166 | 4.9825 | 71 | 0.7632 | 0.7763 |
| 0.0087 | 5.9649 | 85 | 0.9165 | 0.7629 |
| 0.013 | 6.9474 | 99 | 0.8161 | 0.7942 |
| 0.0029 | 8.0 | 114 | 0.8216 | 0.7964 |
| 0.0016 | 8.9825 | 128 | 0.8461 | 0.7919 |
| 0.0009 | 9.9649 | 142 | 0.8528 | 0.7919 |
| 0.0007 | 10.9474 | 156 | 0.8539 | 0.8031 |
| 0.0006 | 12.0 | 171 | 0.8586 | 0.8054 |
| 0.0006 | 12.9825 | 185 | 0.8622 | 0.8076 |
| 0.0005 | 13.9649 | 199 | 0.8649 | 0.8098 |
| 0.0005 | 14.9474 | 213 | 0.8677 | 0.8098 |
| 0.0005 | 16.0 | 228 | 0.8706 | 0.8098 |
| 0.0004 | 16.9825 | 242 | 0.8729 | 0.8098 |
| 0.0004 | 17.9649 | 256 | 0.8747 | 0.8098 |
| 0.0004 | 18.9474 | 270 | 0.8764 | 0.8076 |
| 0.0004 | 20.0 | 285 | 0.8776 | 0.8098 |
| 0.0004 | 20.9825 | 299 | 0.8789 | 0.8076 |
| 0.0003 | 21.9649 | 313 | 0.8794 | 0.8098 |
| 0.0003 | 22.9474 | 327 | 0.8801 | 0.8098 |
| 0.0003 | 24.0 | 342 | 0.8804 | 0.8098 |
| 0.0003 | 24.5614 | 350 | 0.8804 | 0.8098 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"abnormal",
"erythrodermic",
"guttate",
"inverse",
"nail",
"normal",
"not define",
"palm soles",
"plaque",
"psoriatic arthritis",
"pustular",
"scalp"
] |
AirellPramono/finetuned-fruit-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. -->
# finetuned-fruit-classifier
This model is a fine-tuned version of [ivandrian11/fruit-classifier](https://huggingface.co/ivandrian11/fruit-classifier) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1037
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0057 | 1.0 | 7 | 0.1005 | 0.95 |
| 0.0041 | 2.0 | 14 | 0.1033 | 0.95 |
| 0.0042 | 3.0 | 21 | 0.1037 | 0.95 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"acerolas",
"apples",
"apricots",
"avocados",
"bananas",
"blackberries",
"blueberries",
"cantaloupes",
"cherries",
"coconuts",
"figs",
"grapefruits",
"grapes",
"guava",
"kiwifruit",
"lemons",
"limes",
"mangos",
"olives",
"oranges",
"passionfruit",
"peaches",
"pears",
"pineapples",
"plums",
"pomegranates",
"raspberries",
"strawberries",
"tomatoes",
"watermelons"
] |
Countigo/vit-base-beans
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
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: 0.2258
- Accuracy: 0.9699
## 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: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9859 | 1.0 | 17 | 0.7492 | 0.9323 |
| 0.6763 | 2.0 | 34 | 0.5276 | 0.9624 |
| 0.4605 | 3.0 | 51 | 0.3726 | 0.9624 |
| 0.404 | 4.0 | 68 | 0.2965 | 0.9699 |
| 0.3169 | 5.0 | 85 | 0.2538 | 0.9699 |
| 0.2536 | 6.0 | 102 | 0.2273 | 0.9774 |
| 0.2633 | 7.0 | 119 | 0.2258 | 0.9699 |
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
bmahak2005/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 2.3.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Erik172/vit-base-patch16-224-in21k-beans-erik172
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-beans-erik172
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: 0.0524
- 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2068 | 3.8462 | 500 | 0.0524 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
mostafasmart/vit-base-patch16-224-in21k-euroSat
|
<!-- 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. -->
# vit-base-patch16-224-in21k-euroSat
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: 0.1778
- Train Accuracy: 0.9381
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.1819
- Validation Accuracy: 0.9443
- Validation Top-3-accuracy: 1.0
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 120, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.8583 | 0.6111 | 1.0 | 0.5968 | 0.7762 | 1.0 | 0 |
| 0.4764 | 0.8341 | 1.0 | 0.3488 | 0.8683 | 1.0 | 1 |
| 0.2909 | 0.8920 | 1.0 | 0.2400 | 0.9089 | 1.0 | 2 |
| 0.2079 | 0.9211 | 1.0 | 0.1928 | 0.9307 | 1.0 | 3 |
| 0.1778 | 0.9381 | 1.0 | 0.1819 | 0.9443 | 1.0 | 4 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"normaleyes",
"cataract",
"pterygium"
] |
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand10-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand10-aligned_unaugmentation
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: 0.0211
- Accuracy: 0.9928
- Precision: 0.9939
- Recall: 0.9981
- F1: 0.9960
- Roc Auc: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0526 | 0.9994 | 1264 | 0.0211 | 0.9928 | 0.9939 | 0.9981 | 0.9960 | 0.9990 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_unaugmentation
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: 0.2388
- Accuracy: 0.9033
- Precision: 0.8971
- Recall: 0.9901
- F1: 0.9413
- Roc Auc: 0.9623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.2365 | 0.9998 | 1381 | 0.2388 | 0.9033 | 0.8971 | 0.9901 | 0.9413 | 0.9623 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
smartgmin/vit-base-patch16-224-in21k-4class
|
<!-- 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. -->
# vit-base-patch16-224-in21k-4class
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: 0.1673
- Train Accuracy: 0.9240
- Train Top-3-accuracy: 0.9960
- Validation Loss: 0.2804
- Validation Accuracy: 0.9284
- Validation Top-3-accuracy: 0.9963
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 231, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.1244 | 0.5585 | 0.9362 | 0.8773 | 0.7081 | 0.9753 | 0 |
| 0.6801 | 0.7656 | 0.9822 | 0.5789 | 0.8040 | 0.9871 | 1 |
| 0.4108 | 0.8329 | 0.9897 | 0.4105 | 0.8548 | 0.9915 | 2 |
| 0.2717 | 0.8725 | 0.9927 | 0.3397 | 0.8855 | 0.9937 | 3 |
| 0.2123 | 0.8967 | 0.9944 | 0.3307 | 0.9055 | 0.9948 | 4 |
| 0.1822 | 0.9126 | 0.9953 | 0.2927 | 0.9187 | 0.9957 | 5 |
| 0.1673 | 0.9240 | 0.9960 | 0.2804 | 0.9284 | 0.9963 | 6 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"normaleyes",
"cataract",
"pterygium",
"unknown"
] |
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand20-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand20-aligned_unaugmentation
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: 0.0227
- Accuracy: 0.9921
- Precision: 0.9941
- Recall: 0.9972
- F1: 0.9956
- Roc Auc: 0.9986
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0496 | 0.9994 | 1257 | 0.0227 | 0.9921 | 0.9941 | 0.9972 | 0.9956 | 0.9986 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0982
- Accuracy: 0.9661
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0899 | 1.0 | 469 | 0.0982 | 0.9661 |
| 0.0374 | 2.0 | 938 | 0.0992 | 0.968 |
| 0.004 | 3.0 | 1407 | 0.1368 | 0.9672 |
| 0.0011 | 4.0 | 1876 | 0.1196 | 0.9749 |
| 0.0012 | 5.0 | 2345 | 0.1240 | 0.9752 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1549
- Accuracy: 0.9424
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1748 | 1.0 | 469 | 0.1549 | 0.9424 |
| 0.0338 | 2.0 | 938 | 0.2184 | 0.9301 |
| 0.0524 | 3.0 | 1407 | 0.2482 | 0.9411 |
| 0.0009 | 4.0 | 1876 | 0.2712 | 0.9467 |
| 0.0007 | 5.0 | 2345 | 0.2763 | 0.9472 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1931
- Accuracy: 0.9251
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1592 | 1.0 | 469 | 0.1931 | 0.9251 |
| 0.053 | 2.0 | 938 | 0.2442 | 0.9248 |
| 0.0249 | 3.0 | 1407 | 0.3192 | 0.9269 |
| 0.0085 | 4.0 | 1876 | 0.3501 | 0.9336 |
| 0.0008 | 5.0 | 2345 | 0.3632 | 0.9339 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0788
- Accuracy: 0.9739
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0357 | 1.0 | 469 | 0.0788 | 0.9739 |
| 0.0029 | 2.0 | 938 | 0.1357 | 0.9659 |
| 0.0008 | 3.0 | 1407 | 0.0954 | 0.9779 |
| 0.0005 | 4.0 | 1876 | 0.1017 | 0.9789 |
| 0.0004 | 5.0 | 2345 | 0.0953 | 0.9803 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed2_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles10_seed2_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1683
- Accuracy: 0.9429
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1064 | 1.0 | 469 | 0.1683 | 0.9429 |
| 0.0383 | 2.0 | 938 | 0.2168 | 0.944 |
| 0.0354 | 3.0 | 1407 | 0.2290 | 0.9523 |
| 0.0005 | 4.0 | 1876 | 0.2511 | 0.9541 |
| 0.0005 | 5.0 | 2345 | 0.2560 | 0.9536 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand30-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand30-aligned_unaugmentation
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: 0.0198
- Accuracy: 0.9931
- Precision: 0.9948
- Recall: 0.9976
- F1: 0.9962
- Roc Auc: 0.9991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0338 | 0.9994 | 1243 | 0.0198 | 0.9931 | 0.9948 | 0.9976 | 0.9962 | 0.9991 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand40-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand40-aligned_unaugmentation
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: 0.0217
- Accuracy: 0.9923
- Precision: 0.9963
- Recall: 0.9952
- F1: 0.9957
- Roc Auc: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0388 | 1.0 | 1220 | 0.0217 | 0.9923 | 0.9963 | 0.9952 | 0.9957 | 0.9990 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed2_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles12_seed2_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1908
- Accuracy: 0.9341
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1941 | 1.0 | 469 | 0.1908 | 0.9341 |
| 0.1098 | 2.0 | 938 | 0.2508 | 0.9312 |
| 0.0352 | 3.0 | 1407 | 0.3487 | 0.9323 |
| 0.0006 | 4.0 | 1876 | 0.3715 | 0.9328 |
| 0.0004 | 5.0 | 2345 | 0.3757 | 0.9331 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
hchcsuim/batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand50-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand50-aligned_unaugmentation
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: 0.0227
- Accuracy: 0.9923
- Precision: 0.9939
- Recall: 0.9976
- F1: 0.9958
- Roc Auc: 0.9987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1033 | 0.9994 | 1178 | 0.0227 | 0.9923 | 0.9939 | 0.9976 | 0.9958 | 0.9987 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand10-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand10-aligned_unaugmentation
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: 0.1239
- Accuracy: 0.9529
- Precision: 0.9541
- Recall: 0.9914
- F1: 0.9724
- Roc Auc: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1313 | 1.0 | 202 | 0.1239 | 0.9529 | 0.9541 | 0.9914 | 0.9724 | 0.9834 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed3_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles7_seed3_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1020
- Accuracy: 0.9803
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0623 | 1.0 | 469 | 0.1099 | 0.9669 |
| 0.009 | 2.0 | 938 | 0.1434 | 0.9709 |
| 0.0004 | 3.0 | 1407 | 0.1069 | 0.9781 |
| 0.0003 | 4.0 | 1876 | 0.1020 | 0.9803 |
| 0.0002 | 5.0 | 2345 | 0.1060 | 0.9792 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand20-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand20-aligned_unaugmentation
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: 0.1108
- Accuracy: 0.9615
- Precision: 0.9650
- Recall: 0.9899
- F1: 0.9773
- Roc Auc: 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1781 | 0.9975 | 201 | 0.1108 | 0.9615 | 0.9650 | 0.9899 | 0.9773 | 0.9856 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed3_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles10_seed3_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1493
- Accuracy: 0.9464
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1249 | 1.0 | 469 | 0.1493 | 0.9464 |
| 0.0475 | 2.0 | 938 | 0.2181 | 0.9483 |
| 0.001 | 3.0 | 1407 | 0.2409 | 0.9552 |
| 0.0002 | 4.0 | 1876 | 0.2419 | 0.956 |
| 0.0002 | 5.0 | 2345 | 0.2469 | 0.9568 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
its1nonly/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. -->
# its1nonly/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: 2.8165
- Validation Loss: 1.6500
- Train Accuracy: 0.84
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8165 | 1.6500 | 0.84 | 0 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.16.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand30-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand30-aligned_unaugmentation
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: 0.1173
- Accuracy: 0.9559
- Precision: 0.9555
- Recall: 0.9937
- F1: 0.9742
- Roc Auc: 0.9848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1447 | 1.0 | 201 | 0.1173 | 0.9559 | 0.9555 | 0.9937 | 0.9742 | 0.9848 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed3_q1
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles12_seed3_q1
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1793
- Accuracy: 0.9344
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1109 | 1.0 | 469 | 0.1793 | 0.9344 |
| 0.0471 | 2.0 | 938 | 0.3186 | 0.9248 |
| 0.0023 | 3.0 | 1407 | 0.3203 | 0.9392 |
| 0.0002 | 4.0 | 1876 | 0.3376 | 0.9405 |
| 0.0002 | 5.0 | 2345 | 0.3396 | 0.9403 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand50-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_faces-expand50-aligned_unaugmentation
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: 0.1154
- Accuracy: 0.9550
- Precision: 0.9581
- Recall: 0.9856
- F1: 0.9717
- Roc Auc: 0.9902
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.168 | 0.9996 | 1332 | 0.1154 | 0.9550 | 0.9581 | 0.9856 | 0.9717 | 0.9902 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand40-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand40-aligned_unaugmentation
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: 0.1504
- Accuracy: 0.9439
- Precision: 0.9453
- Recall: 0.9904
- F1: 0.9673
- Roc Auc: 0.9728
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.2003 | 0.9962 | 199 | 0.1504 | 0.9439 | 0.9453 | 0.9904 | 0.9673 | 0.9728 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_unaugmentation
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: 0.3378
- Accuracy: 0.8468
- Precision: 0.8510
- Recall: 0.9750
- F1: 0.9088
- Roc Auc: 0.8879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.4067 | 0.9998 | 1381 | 0.3378 | 0.8468 | 0.8510 | 0.9750 | 0.9088 | 0.8879 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_Celeb-DF_opencv-1FPS_faces-expand50-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_Celeb-DF_opencv-1FPS_faces-expand50-aligned_unaugmentation
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: 0.1258
- Accuracy: 0.9556
- Precision: 0.9569
- Recall: 0.9918
- F1: 0.9740
- Roc Auc: 0.9813
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.2155 | 0.9974 | 195 | 0.1258 | 0.9556 | 0.9569 | 0.9918 | 0.9740 | 0.9813 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
platzi/platzi-vit-model-wgcv
|
<!-- 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. -->
# platzi-vit-model-wgcv
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:
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4136, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand10-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_faces-expand10-aligned_unaugmentation
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: 0.0861
- Accuracy: 0.9668
- Precision: 0.9737
- Recall: 0.9841
- F1: 0.9789
- Roc Auc: 0.9937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1164 | 1.0 | 1374 | 0.0861 | 0.9668 | 0.9737 | 0.9841 | 0.9789 | 0.9937 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand30-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_faces-expand30-aligned_unaugmentation
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: 0.1043
- Accuracy: 0.9593
- Precision: 0.9586
- Recall: 0.9907
- F1: 0.9744
- Roc Auc: 0.9935
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0935 | 0.9994 | 1359 | 0.1043 | 0.9593 | 0.9586 | 0.9907 | 0.9744 | 0.9935 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_faces-expand20-aligned_unaugmentation
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: 0.0892
- Accuracy: 0.9652
- Precision: 0.9761
- Recall: 0.9796
- F1: 0.9778
- Roc Auc: 0.9929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1093 | 0.9996 | 1368 | 0.0892 | 0.9652 | 0.9761 | 0.9796 | 0.9778 | 0.9929 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand40-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-raw_opencv-1FPS_faces-expand40-aligned_unaugmentation
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: 0.0934
- Accuracy: 0.9640
- Precision: 0.9690
- Recall: 0.9855
- F1: 0.9772
- Roc Auc: 0.9930
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0939 | 1.0 | 1348 | 0.0934 | 0.9640 | 0.9690 | 0.9855 | 0.9772 | 0.9930 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand50-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand50-aligned_unaugmentation
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: 0.2819
- Accuracy: 0.8766
- Precision: 0.9057
- Recall: 0.9402
- F1: 0.9226
- Roc Auc: 0.9232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3591 | 1.0 | 1381 | 0.2819 | 0.8766 | 0.9057 | 0.9402 | 0.9226 | 0.9232 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand10-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand10-aligned_unaugmentation
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: 0.2914
- Accuracy: 0.8733
- Precision: 0.9096
- Recall: 0.9306
- F1: 0.9200
- Roc Auc: 0.9185
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3689 | 1.0 | 1381 | 0.2914 | 0.8733 | 0.9096 | 0.9306 | 0.9200 | 0.9185 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand0-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand0-aligned_unaugmentation
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: 0.3336
- Accuracy: 0.8527
- Precision: 0.9117
- Recall: 0.8989
- F1: 0.9053
- Roc Auc: 0.8991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3834 | 1.0 | 1381 | 0.3336 | 0.8527 | 0.9117 | 0.8989 | 0.9053 | 0.8991 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
wgcv/platzi-vit-model-wgcv
|
<!-- 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. -->
# platzi-vit-model-wgcv
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:
## Testing
Bean Rust
Healthy
## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 4136, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand20-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand20-aligned_unaugmentation
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: 0.2936
- Accuracy: 0.8726
- Precision: 0.9128
- Recall: 0.9256
- F1: 0.9192
- Roc Auc: 0.9186
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3713 | 1.0 | 1381 | 0.2936 | 0.8726 | 0.9128 | 0.9256 | 0.9192 | 0.9186 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand30-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand30-aligned_unaugmentation
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: 0.2878
- Accuracy: 0.8762
- Precision: 0.9024
- Recall: 0.9440
- F1: 0.9227
- Roc Auc: 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3772 | 1.0 | 1381 | 0.2878 | 0.8762 | 0.9024 | 0.9440 | 0.9227 | 0.9189 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_FFPP-c40_opencv-1FPS_faces-expand40-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_FFPP-c40_opencv-1FPS_faces-expand40-aligned_unaugmentation
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: 0.2789
- Accuracy: 0.8802
- Precision: 0.9079
- Recall: 0.9426
- F1: 0.9249
- Roc Auc: 0.9258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.3781 | 1.0 | 1381 | 0.2789 | 0.8802 | 0.9079 | 0.9426 | 0.9249 | 0.9258 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
Mithun162001/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. -->
# Mithun162001/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: 0.3749
- Validation Loss: 0.3678
- Train Accuracy: 0.912
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7571 | 1.6496 | 0.814 | 0 |
| 1.2022 | 0.8020 | 0.909 | 1 |
| 0.7036 | 0.5592 | 0.895 | 2 |
| 0.4919 | 0.4119 | 0.911 | 3 |
| 0.3749 | 0.3678 | 0.912 | 4 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Augusto777/swinv2-finetuned-ve-Ub200
|
<!-- 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. -->
# swinv2-finetuned-ve-Ub200
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5977
- Accuracy: 0.4706
## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 7.9891 | 0.0980 |
| No log | 2.0 | 13 | 7.4848 | 0.0980 |
| No log | 2.92 | 19 | 6.2378 | 0.0980 |
| No log | 4.0 | 26 | 4.8900 | 0.0980 |
| No log | 4.92 | 32 | 3.8155 | 0.0980 |
| No log | 6.0 | 39 | 2.7342 | 0.0980 |
| No log | 6.92 | 45 | 2.0612 | 0.0980 |
| No log | 8.0 | 52 | 1.5977 | 0.4706 |
| No log | 8.92 | 58 | 1.3671 | 0.4706 |
| No log | 10.0 | 65 | 1.2122 | 0.4706 |
| No log | 10.92 | 71 | 1.1823 | 0.4706 |
| No log | 12.0 | 78 | 1.1835 | 0.4706 |
| No log | 12.92 | 84 | 1.1838 | 0.4706 |
| No log | 14.0 | 91 | 1.1778 | 0.4706 |
| No log | 14.92 | 97 | 1.1769 | 0.4706 |
| 3.2267 | 16.0 | 104 | 1.1762 | 0.4706 |
| 3.2267 | 16.92 | 110 | 1.1758 | 0.4706 |
| 3.2267 | 18.0 | 117 | 1.1770 | 0.4706 |
| 3.2267 | 18.46 | 120 | 1.1771 | 0.4706 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"avanzada",
"leve",
"moderada",
"no dmae"
] |
Augusto777/vit-base-patch16-224-ve-U13b-R
|
<!-- 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-ve-U13b-R
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: 0.3534
- Accuracy: 0.9348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3157 | 0.99 | 51 | 1.2967 | 0.3478 |
| 0.9801 | 2.0 | 103 | 0.9966 | 0.5870 |
| 0.7385 | 2.99 | 154 | 0.7600 | 0.7174 |
| 0.572 | 4.0 | 206 | 0.6425 | 0.7826 |
| 0.3646 | 4.99 | 257 | 0.7687 | 0.6957 |
| 0.3033 | 6.0 | 309 | 0.6336 | 0.7391 |
| 0.3073 | 6.99 | 360 | 0.3534 | 0.9348 |
| 0.1623 | 8.0 | 412 | 0.8559 | 0.6739 |
| 0.1079 | 8.99 | 463 | 0.9730 | 0.7391 |
| 0.2703 | 10.0 | 515 | 0.7768 | 0.8043 |
| 0.178 | 10.99 | 566 | 0.8520 | 0.7826 |
| 0.2191 | 12.0 | 618 | 1.0049 | 0.7391 |
| 0.0597 | 12.99 | 669 | 0.8334 | 0.7609 |
| 0.0881 | 14.0 | 721 | 0.9985 | 0.7609 |
| 0.1265 | 14.99 | 772 | 0.9443 | 0.8043 |
| 0.0696 | 16.0 | 824 | 0.9878 | 0.8261 |
| 0.1198 | 16.99 | 875 | 0.8784 | 0.8043 |
| 0.1484 | 18.0 | 927 | 0.9595 | 0.7609 |
| 0.2887 | 18.99 | 978 | 1.0563 | 0.8043 |
| 0.1423 | 20.0 | 1030 | 0.8550 | 0.8043 |
| 0.083 | 20.99 | 1081 | 0.9093 | 0.7826 |
| 0.0695 | 22.0 | 1133 | 1.2758 | 0.6739 |
| 0.0285 | 22.99 | 1184 | 1.0852 | 0.7609 |
| 0.0132 | 24.0 | 1236 | 1.3341 | 0.6957 |
| 0.0957 | 24.99 | 1287 | 1.1965 | 0.7391 |
| 0.0633 | 26.0 | 1339 | 1.1199 | 0.7609 |
| 0.0705 | 26.99 | 1390 | 1.0551 | 0.8043 |
| 0.0564 | 28.0 | 1442 | 1.4332 | 0.7391 |
| 0.0798 | 28.99 | 1493 | 1.3855 | 0.7391 |
| 0.0326 | 30.0 | 1545 | 1.0534 | 0.8043 |
| 0.092 | 30.99 | 1596 | 1.1745 | 0.7609 |
| 0.1243 | 32.0 | 1648 | 1.1341 | 0.8043 |
| 0.062 | 32.99 | 1699 | 1.2648 | 0.7826 |
| 0.0941 | 34.0 | 1751 | 1.1236 | 0.7826 |
| 0.0119 | 34.99 | 1802 | 1.1303 | 0.8043 |
| 0.044 | 36.0 | 1854 | 1.1848 | 0.7826 |
| 0.0073 | 36.99 | 1905 | 1.1796 | 0.7609 |
| 0.0149 | 38.0 | 1957 | 1.2491 | 0.7826 |
| 0.0194 | 38.99 | 2008 | 1.1812 | 0.7826 |
| 0.0577 | 39.61 | 2040 | 1.1777 | 0.7609 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"avanzada",
"leve",
"moderada",
"no dmae"
] |
Augusto777/vit-base-patch16-224-ve-U13b-80R
|
<!-- 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-ve-U13b-80R
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: 0.4109
- Accuracy: 0.8913
## 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: 5.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3158 | 0.99 | 51 | 1.2967 | 0.3478 |
| 1.0472 | 2.0 | 103 | 0.9694 | 0.5 |
| 0.6641 | 2.99 | 154 | 0.7911 | 0.7391 |
| 0.5615 | 4.0 | 206 | 0.6850 | 0.7391 |
| 0.3458 | 4.99 | 257 | 0.4109 | 0.8913 |
| 0.3156 | 6.0 | 309 | 0.5213 | 0.8043 |
| 0.141 | 6.99 | 360 | 0.4793 | 0.8478 |
| 0.2016 | 8.0 | 412 | 0.6031 | 0.7826 |
| 0.2444 | 8.99 | 463 | 0.7324 | 0.8043 |
| 0.1501 | 10.0 | 515 | 0.6392 | 0.8043 |
| 0.1256 | 10.99 | 566 | 0.9706 | 0.7826 |
| 0.2421 | 12.0 | 618 | 0.8059 | 0.7826 |
| 0.103 | 12.99 | 669 | 0.7601 | 0.8478 |
| 0.1353 | 14.0 | 721 | 1.1986 | 0.7391 |
| 0.1095 | 14.99 | 772 | 1.0279 | 0.7609 |
| 0.065 | 16.0 | 824 | 1.2043 | 0.6957 |
| 0.1777 | 16.99 | 875 | 0.9779 | 0.8043 |
| 0.0813 | 18.0 | 927 | 1.3356 | 0.7391 |
| 0.2552 | 18.99 | 978 | 0.8483 | 0.8261 |
| 0.0941 | 20.0 | 1030 | 0.7106 | 0.8696 |
| 0.0486 | 20.99 | 1081 | 0.8359 | 0.8261 |
| 0.0361 | 22.0 | 1133 | 0.8710 | 0.8261 |
| 0.0361 | 22.99 | 1184 | 1.0301 | 0.8043 |
| 0.0136 | 24.0 | 1236 | 0.9015 | 0.8261 |
| 0.1441 | 24.99 | 1287 | 0.9958 | 0.8043 |
| 0.0181 | 26.0 | 1339 | 1.0793 | 0.7826 |
| 0.0612 | 26.99 | 1390 | 0.9678 | 0.8043 |
| 0.0814 | 28.0 | 1442 | 1.0320 | 0.7826 |
| 0.0479 | 28.99 | 1493 | 1.1845 | 0.7826 |
| 0.06 | 30.0 | 1545 | 1.2026 | 0.7826 |
| 0.0777 | 30.99 | 1596 | 1.1574 | 0.7826 |
| 0.0747 | 32.0 | 1648 | 1.3104 | 0.7609 |
| 0.0181 | 32.99 | 1699 | 1.1145 | 0.8043 |
| 0.0652 | 34.0 | 1751 | 1.1691 | 0.8043 |
| 0.0242 | 34.99 | 1802 | 1.2415 | 0.8043 |
| 0.0043 | 36.0 | 1854 | 1.1841 | 0.7826 |
| 0.0318 | 36.99 | 1905 | 1.2475 | 0.8043 |
| 0.0092 | 38.0 | 1957 | 1.2452 | 0.8043 |
| 0.0194 | 38.99 | 2008 | 1.2395 | 0.8043 |
| 0.0376 | 39.61 | 2040 | 1.2345 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"avanzada",
"leve",
"moderada",
"no dmae"
] |
Augusto777/vit-base-patch16-224-ve-U13b-80RX
|
<!-- 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-ve-U13b-80RX
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: 0.6099
- Accuracy: 0.8478
## 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: 5.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3857 | 0.99 | 17 | 1.3703 | 0.5652 |
| 1.3134 | 1.98 | 34 | 1.2235 | 0.4565 |
| 1.0384 | 2.97 | 51 | 1.0173 | 0.5435 |
| 0.908 | 3.96 | 68 | 0.8346 | 0.7826 |
| 0.75 | 4.95 | 85 | 0.7343 | 0.7826 |
| 0.5131 | 6.0 | 103 | 0.6099 | 0.8478 |
| 0.395 | 6.99 | 120 | 0.5932 | 0.7826 |
| 0.355 | 7.98 | 137 | 0.7209 | 0.7391 |
| 0.2658 | 8.97 | 154 | 0.5652 | 0.8043 |
| 0.248 | 9.96 | 171 | 0.7103 | 0.7826 |
| 0.2086 | 10.95 | 188 | 0.6788 | 0.7609 |
| 0.1532 | 12.0 | 206 | 0.5725 | 0.7826 |
| 0.147 | 12.99 | 223 | 0.6130 | 0.8043 |
| 0.1145 | 13.98 | 240 | 0.6563 | 0.8043 |
| 0.1053 | 14.97 | 257 | 0.5993 | 0.8043 |
| 0.0971 | 15.96 | 274 | 0.8840 | 0.7391 |
| 0.0947 | 16.95 | 291 | 0.6256 | 0.8043 |
| 0.1055 | 18.0 | 309 | 0.8406 | 0.7609 |
| 0.0974 | 18.99 | 326 | 0.6355 | 0.8478 |
| 0.1215 | 19.98 | 343 | 0.6651 | 0.8043 |
| 0.108 | 20.97 | 360 | 0.8301 | 0.7826 |
| 0.0784 | 21.96 | 377 | 0.8837 | 0.7609 |
| 0.0919 | 22.95 | 394 | 0.6985 | 0.8043 |
| 0.064 | 24.0 | 412 | 0.6426 | 0.8043 |
| 0.0669 | 24.99 | 429 | 0.8102 | 0.7826 |
| 0.0878 | 25.98 | 446 | 0.7863 | 0.7391 |
| 0.0875 | 26.97 | 463 | 0.8777 | 0.7609 |
| 0.0441 | 27.96 | 480 | 0.7324 | 0.8043 |
| 0.088 | 28.95 | 497 | 0.8099 | 0.7826 |
| 0.0739 | 30.0 | 515 | 0.7776 | 0.8043 |
| 0.0598 | 30.99 | 532 | 0.8188 | 0.7826 |
| 0.0443 | 31.98 | 549 | 0.8549 | 0.8043 |
| 0.0376 | 32.97 | 566 | 0.8049 | 0.7826 |
| 0.0375 | 33.96 | 583 | 0.8037 | 0.8043 |
| 0.0346 | 34.95 | 600 | 0.8255 | 0.8261 |
| 0.0471 | 36.0 | 618 | 0.8239 | 0.8043 |
| 0.0669 | 36.99 | 635 | 0.8188 | 0.8043 |
| 0.0438 | 37.98 | 652 | 0.8443 | 0.8043 |
| 0.0549 | 38.97 | 669 | 0.8551 | 0.8043 |
| 0.0622 | 39.61 | 680 | 0.8551 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"avanzada",
"leve",
"moderada",
"no dmae"
] |
Augusto777/vit-base-patch16-224-ve-U13b-80RX1
|
<!-- 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-ve-U13b-80RX1
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: 0.7770
- Accuracy: 0.8478
## 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: 5.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.05
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3157 | 0.99 | 51 | 1.2968 | 0.3478 |
| 1.0334 | 2.0 | 103 | 1.0060 | 0.5217 |
| 0.691 | 2.99 | 154 | 0.7506 | 0.7609 |
| 0.5005 | 4.0 | 206 | 0.6433 | 0.7826 |
| 0.3478 | 4.99 | 257 | 0.5674 | 0.7609 |
| 0.3339 | 6.0 | 309 | 0.6623 | 0.7609 |
| 0.2533 | 6.99 | 360 | 0.6905 | 0.7391 |
| 0.138 | 8.0 | 412 | 0.7251 | 0.7826 |
| 0.1289 | 8.99 | 463 | 0.7467 | 0.7391 |
| 0.152 | 10.0 | 515 | 0.9011 | 0.7174 |
| 0.2609 | 10.99 | 566 | 1.0150 | 0.7174 |
| 0.2202 | 12.0 | 618 | 0.9713 | 0.7826 |
| 0.1083 | 12.99 | 669 | 1.1106 | 0.6739 |
| 0.07 | 14.0 | 721 | 1.1211 | 0.7174 |
| 0.0791 | 14.99 | 772 | 1.1830 | 0.7609 |
| 0.0427 | 16.0 | 824 | 0.7770 | 0.8478 |
| 0.1219 | 16.99 | 875 | 1.0962 | 0.7391 |
| 0.0739 | 18.0 | 927 | 0.9447 | 0.7609 |
| 0.1989 | 18.99 | 978 | 1.1543 | 0.7391 |
| 0.1097 | 20.0 | 1030 | 1.1795 | 0.7609 |
| 0.1204 | 20.99 | 1081 | 1.2679 | 0.6739 |
| 0.0514 | 22.0 | 1133 | 1.0646 | 0.7174 |
| 0.0612 | 22.99 | 1184 | 1.1413 | 0.6957 |
| 0.0207 | 24.0 | 1236 | 0.8928 | 0.7826 |
| 0.1063 | 24.99 | 1287 | 1.1186 | 0.7609 |
| 0.1076 | 26.0 | 1339 | 1.1741 | 0.7609 |
| 0.0714 | 26.99 | 1390 | 1.0977 | 0.8043 |
| 0.062 | 28.0 | 1442 | 1.3965 | 0.7174 |
| 0.0617 | 28.99 | 1493 | 1.1849 | 0.7609 |
| 0.0536 | 30.0 | 1545 | 1.0865 | 0.7826 |
| 0.0707 | 30.99 | 1596 | 1.2081 | 0.7609 |
| 0.0967 | 32.0 | 1648 | 1.3300 | 0.7391 |
| 0.0564 | 32.99 | 1699 | 1.2240 | 0.7826 |
| 0.0435 | 34.0 | 1751 | 1.2391 | 0.7609 |
| 0.043 | 34.99 | 1802 | 1.1813 | 0.7609 |
| 0.0218 | 36.0 | 1854 | 1.2496 | 0.7826 |
| 0.0043 | 36.99 | 1905 | 1.2797 | 0.7174 |
| 0.0051 | 38.0 | 1957 | 1.2493 | 0.7391 |
| 0.0123 | 38.99 | 2008 | 1.2538 | 0.7391 |
| 0.0546 | 39.61 | 2040 | 1.2530 | 0.7609 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
[
"avanzada",
"leve",
"moderada",
"no dmae"
] |
mostafasmart/vit-base-patch16-224-5class224
|
<!-- 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. -->
# vit-base-patch16-224-5class224
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:
- Train Loss: 0.0115
- Train Accuracy: 0.9460
- Train Top-3-accuracy: 0.9911
- Validation Loss: 0.1621
- Validation Accuracy: 0.9490
- Validation Top-3-accuracy: 0.9916
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 574, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.7725 | 0.6414 | 0.8898 | 0.3755 | 0.7636 | 0.9478 | 0 |
| 0.2160 | 0.8219 | 0.9635 | 0.2372 | 0.8557 | 0.9726 | 1 |
| 0.0696 | 0.8812 | 0.9780 | 0.2035 | 0.8989 | 0.9818 | 2 |
| 0.0344 | 0.9108 | 0.9842 | 0.1715 | 0.9203 | 0.9860 | 3 |
| 0.0194 | 0.9278 | 0.9875 | 0.1911 | 0.9337 | 0.9888 | 4 |
| 0.0147 | 0.9381 | 0.9897 | 0.1651 | 0.9425 | 0.9904 | 5 |
| 0.0115 | 0.9460 | 0.9911 | 0.1621 | 0.9490 | 0.9916 | 6 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"normaleyes",
"blepharitis",
"cataract",
"pterygium",
"unknown"
] |
alanAIKit/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 2.3.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
mostafasmart/vit-base-patch16-224-7class224
|
<!-- 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. -->
# vit-base-patch16-224-7class224
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:
- Train Loss: 0.0078
- Train Accuracy: 0.9540
- Train Top-3-accuracy: 0.9960
- Validation Loss: 0.1065
- Validation Accuracy: 0.9569
- Validation Top-3-accuracy: 0.9963
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 630, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.6954 | 0.6170 | 0.9295 | 0.3090 | 0.7653 | 0.9734 | 0 |
| 0.1603 | 0.8272 | 0.9819 | 0.1722 | 0.8640 | 0.9865 | 1 |
| 0.0448 | 0.8890 | 0.9892 | 0.1220 | 0.9071 | 0.9912 | 2 |
| 0.0201 | 0.9192 | 0.9924 | 0.1171 | 0.9289 | 0.9934 | 3 |
| 0.0132 | 0.9359 | 0.9942 | 0.1132 | 0.9416 | 0.9948 | 4 |
| 0.0089 | 0.9466 | 0.9952 | 0.1095 | 0.9506 | 0.9957 | 5 |
| 0.0078 | 0.9540 | 0.9960 | 0.1065 | 0.9569 | 0.9963 | 6 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"normaleyes",
"cataract",
"diabetic_retinopathy",
"normal",
"pterygium",
"unknown"
] |
vishnun0027/Crop_Disease_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. -->
# Crop_Disease_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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2482
- Accuracy: 0.7
## 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: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.974 | 0.9787 | 23 | 2.9288 | 0.1573 |
| 2.8301 | 2.0 | 47 | 2.6713 | 0.5173 |
| 2.3995 | 2.9787 | 70 | 2.3223 | 0.5707 |
| 2.112 | 4.0 | 94 | 2.0321 | 0.604 |
| 1.8965 | 4.9787 | 117 | 1.8377 | 0.6133 |
| 1.6807 | 6.0 | 141 | 1.6895 | 0.6307 |
| 1.4942 | 6.9787 | 164 | 1.5807 | 0.6693 |
| 1.3849 | 8.0 | 188 | 1.5080 | 0.664 |
| 1.2975 | 8.9787 | 211 | 1.4605 | 0.6613 |
| 1.1747 | 10.0 | 235 | 1.3888 | 0.692 |
| 1.1457 | 10.9787 | 258 | 1.3622 | 0.692 |
| 1.0602 | 12.0 | 282 | 1.3318 | 0.6893 |
| 1.0296 | 12.9787 | 305 | 1.2968 | 0.7133 |
| 0.9556 | 14.0 | 329 | 1.2999 | 0.676 |
| 0.9317 | 14.9787 | 352 | 1.2625 | 0.7053 |
| 0.9134 | 16.0 | 376 | 1.2656 | 0.696 |
| 0.914 | 16.9787 | 399 | 1.2593 | 0.7013 |
| 0.9013 | 17.6170 | 414 | 1.2482 | 0.7 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"anthracnose",
"apple scab",
"crown gall",
"downy mildew",
"fire blight",
"fusarium",
"gray mold",
"leaf spots",
"mosaic virus",
"nematodes",
"powdery mildew",
"verticillium",
"black spot",
"blight",
"blossom end rot",
"botrytis",
"brown rot",
"canker",
"cedar apple rust",
"clubroot"
] |
th041/vit-weldclassifyv4
|
<!-- 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-weldclassifyv4
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: 0.5265
- Accuracy: 0.8094
## 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: 13
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1126 | 0.6410 | 100 | 1.0171 | 0.5504 |
| 0.8229 | 1.2821 | 200 | 0.7307 | 0.6942 |
| 0.7224 | 1.9231 | 300 | 0.6399 | 0.7122 |
| 0.3909 | 2.5641 | 400 | 0.5400 | 0.7734 |
| 0.237 | 3.2051 | 500 | 0.6716 | 0.7626 |
| 0.4056 | 3.8462 | 600 | 0.5265 | 0.8094 |
| 0.1764 | 4.4872 | 700 | 0.9174 | 0.7446 |
| 0.0546 | 5.1282 | 800 | 0.6644 | 0.8237 |
| 0.0436 | 5.7692 | 900 | 0.6923 | 0.8345 |
| 0.0661 | 6.4103 | 1000 | 0.6784 | 0.8345 |
| 0.0167 | 7.0513 | 1100 | 0.7115 | 0.8309 |
| 0.0744 | 7.6923 | 1200 | 0.6341 | 0.8525 |
| 0.0047 | 8.3333 | 1300 | 0.6402 | 0.8597 |
| 0.0039 | 8.9744 | 1400 | 0.5958 | 0.8849 |
| 0.0029 | 9.6154 | 1500 | 0.6158 | 0.8885 |
| 0.0027 | 10.2564 | 1600 | 0.6189 | 0.8885 |
| 0.0025 | 10.8974 | 1700 | 0.6309 | 0.8885 |
| 0.0024 | 11.5385 | 1800 | 0.6356 | 0.8885 |
| 0.0023 | 12.1795 | 1900 | 0.6382 | 0.8885 |
| 0.0023 | 12.8205 | 2000 | 0.6399 | 0.8885 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"a",
"b",
"c",
"d"
] |
emirie/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 2.3.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Luuu01/RESNET
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Luuu01/RESNETDONE
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Seh83/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 2.3.0+cu121
- Datasets 2.7.1
- Tokenizers 0.13.3
|
[
"daisy",
"dandelion",
"roses",
"sunflowers",
"tulips"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles4_seed1_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles4_seed1_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0595
- Accuracy: 0.9813
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0341 | 1.0 | 469 | 0.0602 | 0.9805 |
| 0.008 | 2.0 | 938 | 0.0595 | 0.9813 |
| 0.0196 | 3.0 | 1407 | 0.0648 | 0.9845 |
| 0.0011 | 4.0 | 1876 | 0.0672 | 0.9848 |
| 0.0015 | 5.0 | 2345 | 0.0675 | 0.9851 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0879
- Accuracy: 0.9669
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.108 | 1.0 | 469 | 0.0879 | 0.9669 |
| 0.0508 | 2.0 | 938 | 0.0957 | 0.9688 |
| 0.0174 | 3.0 | 1407 | 0.0956 | 0.9792 |
| 0.0012 | 4.0 | 1876 | 0.1015 | 0.9797 |
| 0.0013 | 5.0 | 2345 | 0.1044 | 0.9795 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1562
- Accuracy: 0.9413
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0722 | 1.0 | 469 | 0.1562 | 0.9413 |
| 0.0062 | 2.0 | 938 | 0.2430 | 0.9371 |
| 0.0043 | 3.0 | 1407 | 0.2117 | 0.948 |
| 0.0025 | 4.0 | 1876 | 0.2428 | 0.9501 |
| 0.0014 | 5.0 | 2345 | 0.2693 | 0.9475 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
sunnyrp21/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5916
- Accuracy: 0.894
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6279 | 0.992 | 62 | 2.4589 | 0.829 |
| 1.781 | 2.0 | 125 | 1.7553 | 0.876 |
| 1.5678 | 2.976 | 186 | 1.5916 | 0.894 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1990
- Accuracy: 0.9235
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1395 | 1.0 | 469 | 0.1990 | 0.9235 |
| 0.072 | 2.0 | 938 | 0.2463 | 0.9272 |
| 0.0085 | 3.0 | 1407 | 0.2956 | 0.9312 |
| 0.0016 | 4.0 | 1876 | 0.3366 | 0.9325 |
| 0.0015 | 5.0 | 2345 | 0.3437 | 0.9349 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles4_seed2_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles4_seed2_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0480
- Accuracy: 0.9848
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0433 | 1.0 | 469 | 0.0480 | 0.9848 |
| 0.0012 | 2.0 | 938 | 0.0592 | 0.9837 |
| 0.0009 | 3.0 | 1407 | 0.0801 | 0.9829 |
| 0.0007 | 4.0 | 1876 | 0.0696 | 0.9851 |
| 0.0006 | 5.0 | 2345 | 0.0701 | 0.9853 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0742
- Accuracy: 0.9739
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0079 | 1.0 | 469 | 0.0742 | 0.9739 |
| 0.0016 | 2.0 | 938 | 0.0763 | 0.9787 |
| 0.0009 | 3.0 | 1407 | 0.0937 | 0.9795 |
| 0.0007 | 4.0 | 1876 | 0.1036 | 0.9784 |
| 0.0006 | 5.0 | 2345 | 0.0894 | 0.9816 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed2_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles10_seed2_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2174
- Accuracy: 0.9131
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1147 | 1.0 | 469 | 0.2174 | 0.9131 |
| 0.1135 | 2.0 | 938 | 0.2590 | 0.9061 |
| 0.0194 | 3.0 | 1407 | 0.2780 | 0.9264 |
| 0.0053 | 4.0 | 1876 | 0.2965 | 0.9347 |
| 0.0026 | 5.0 | 2345 | 0.3056 | 0.9347 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed2_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles12_seed2_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2206
- Accuracy: 0.9131
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1274 | 1.0 | 469 | 0.2206 | 0.9131 |
| 0.1174 | 2.0 | 938 | 0.2408 | 0.9163 |
| 0.0722 | 3.0 | 1407 | 0.3461 | 0.9144 |
| 0.0025 | 4.0 | 1876 | 0.3689 | 0.9227 |
| 0.0018 | 5.0 | 2345 | 0.3660 | 0.9269 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles4_seed3_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles4_seed3_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
- Accuracy: 0.9813
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0557 | 1.0 | 469 | 0.0603 | 0.9776 |
| 0.0195 | 2.0 | 938 | 0.0602 | 0.9813 |
| 0.0039 | 3.0 | 1407 | 0.0863 | 0.9805 |
| 0.0012 | 4.0 | 1876 | 0.0866 | 0.9813 |
| 0.0008 | 5.0 | 2345 | 0.0897 | 0.9816 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed3_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles7_seed3_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0909
- Accuracy: 0.9653
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0583 | 1.0 | 469 | 0.0909 | 0.9653 |
| 0.0278 | 2.0 | 938 | 0.1095 | 0.972 |
| 0.002 | 3.0 | 1407 | 0.1121 | 0.9747 |
| 0.0007 | 4.0 | 1876 | 0.1056 | 0.9773 |
| 0.0006 | 5.0 | 2345 | 0.1085 | 0.9784 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed3_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles10_seed3_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1746
- Accuracy: 0.9384
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0726 | 1.0 | 469 | 0.1746 | 0.9384 |
| 0.0066 | 2.0 | 938 | 0.2144 | 0.9467 |
| 0.0045 | 3.0 | 1407 | 0.2160 | 0.9509 |
| 0.0012 | 4.0 | 1876 | 0.2315 | 0.9515 |
| 0.001 | 5.0 | 2345 | 0.2408 | 0.9515 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed3_q3_dropout_v2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_epochs5_batch32_lr5e-05_size224_tiles12_seed3_q3_dropout_v2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2352
- Accuracy: 0.912
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1698 | 1.0 | 469 | 0.2352 | 0.912 |
| 0.0749 | 2.0 | 938 | 0.2504 | 0.9285 |
| 0.0024 | 3.0 | 1407 | 0.3106 | 0.9307 |
| 0.0014 | 4.0 | 1876 | 0.3357 | 0.9339 |
| 0.001 | 5.0 | 2345 | 0.3471 | 0.9333 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
funkepal/vit-medicinal-plant-finetune
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## 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]
|
[
"aloevera",
"amla",
"amruta_balli",
"arali",
"ashoka",
"ashwagandha",
"avacado",
"bamboo",
"basale",
"betel",
"betel_nut",
"brahmi",
"castor",
"curry_leaf",
"doddapatre",
"ekka",
"ganike",
"gauva",
"geranium",
"henna",
"hibiscus",
"honge",
"insulin",
"jasmine",
"lemon",
"lemon_grass",
"mango",
"mint",
"nagadali",
"neem",
"nithyapushpa",
"nooni",
"not_a_plant",
"pappaya",
"pepper",
"pomegranate",
"raktachandini",
"rose",
"sapota",
"tulasi",
"wood_sorel"
] |
hchcsuim/batch-size16_DFDC_opencv-1FPS_unaugmentation
|
<!-- 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. -->
# batch-size16_DFDC_opencv-1FPS_unaugmentation
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: 0.0674
- Accuracy: 0.9754
- Precision: 0.9893
- Recall: 0.9814
- F1: 0.9853
- Roc Auc: 0.9951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0756 | 1.0000 | 18848 | 0.0674 | 0.9754 | 0.9893 | 0.9814 | 0.9853 | 0.9951 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
hchcsuim/batch-size16_DFDC_opencv-1FPS_faces-expand50-aligned_unaugmentation
|
<!-- 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. -->
# batch-size16_DFDC_opencv-1FPS_faces-expand50-aligned_unaugmentation
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: 0.0529
- Accuracy: 0.9796
- Precision: 0.9855
- Recall: 0.9903
- F1: 0.9879
- Roc Auc: 0.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.0568 | 1.0 | 18831 | 0.0529 | 0.9796 | 0.9855 | 0.9903 | 0.9879 | 0.9965 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"fake",
"real"
] |
djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2418
- Accuracy: 0.9148
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4375 | 0.9836 | 30 | 0.4385 | 0.8564 |
| 0.3408 | 2.0 | 61 | 0.2872 | 0.8978 |
| 0.3106 | 2.9836 | 91 | 0.2598 | 0.9100 |
| 0.3167 | 4.0 | 122 | 0.2609 | 0.9124 |
| 0.2533 | 4.9836 | 152 | 0.2426 | 0.9075 |
| 0.256 | 6.0 | 183 | 0.2372 | 0.9075 |
| 0.2492 | 6.9836 | 213 | 0.2418 | 0.9148 |
| 0.2364 | 8.0 | 244 | 0.2352 | 0.9051 |
| 0.2301 | 8.9836 | 274 | 0.2348 | 0.9075 |
| 0.2255 | 9.8361 | 300 | 0.2350 | 0.8978 |
### Framework versions
- Transformers 4.42.3
- Pytorch 1.12.1+cu113
- Datasets 2.19.2
- Tokenizers 0.19.1
|
[
"invalid",
"mid market",
"non mid market"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles4_seed1_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles4_seed1_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0429
- Accuracy: 0.9853
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1508 | 1.0 | 469 | 0.0772 | 0.9747 |
| 0.1087 | 2.0 | 938 | 0.0651 | 0.9755 |
| 0.1245 | 3.0 | 1407 | 0.0429 | 0.9861 |
| 0.1423 | 4.0 | 1876 | 0.0599 | 0.9808 |
| 0.0791 | 5.0 | 2345 | 0.0429 | 0.9853 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
JacobJan/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 None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0069
- eval_accuracy: 0.9967
- eval_runtime: 136.0728
- eval_samples_per_second: 8.819
- eval_steps_per_second: 0.279
- epoch: 1.1953
- step: 101
## 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
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"0",
"1"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles7_seed1_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0875
- Accuracy: 0.9733
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0461 | 1.0 | 469 | 0.0954 | 0.964 |
| 0.1075 | 2.0 | 938 | 0.0919 | 0.9675 |
| 0.0729 | 3.0 | 1407 | 0.0986 | 0.9688 |
| 0.0534 | 4.0 | 1876 | 0.0925 | 0.9696 |
| 0.0555 | 5.0 | 2345 | 0.0875 | 0.9733 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles10_seed1_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1506
- Accuracy: 0.9445
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1011 | 1.0 | 469 | 0.1869 | 0.9261 |
| 0.0877 | 2.0 | 938 | 0.1684 | 0.9376 |
| 0.0897 | 3.0 | 1407 | 0.1506 | 0.9445 |
| 0.0925 | 4.0 | 1876 | 0.1784 | 0.9459 |
| 0.0336 | 5.0 | 2345 | 0.1561 | 0.9512 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles12_seed1_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2382
- Accuracy: 0.9272
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1052 | 1.0 | 469 | 0.2414 | 0.9045 |
| 0.1223 | 2.0 | 938 | 0.3206 | 0.8925 |
| 0.0798 | 3.0 | 1407 | 0.2540 | 0.916 |
| 0.0959 | 4.0 | 1876 | 0.2636 | 0.9224 |
| 0.0781 | 5.0 | 2345 | 0.2382 | 0.9272 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles4_seed2_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles4_seed2_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
- Accuracy: 0.9848
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0517 | 1.0 | 469 | 0.0511 | 0.9827 |
| 0.048 | 2.0 | 938 | 0.0483 | 0.9848 |
| 0.0113 | 3.0 | 1407 | 0.0497 | 0.9845 |
| 0.0503 | 4.0 | 1876 | 0.0577 | 0.9853 |
| 0.0657 | 5.0 | 2345 | 0.0585 | 0.9851 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
Floriankidev/deit-small-patch16-224-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. -->
# deit-small-patch16-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6817
- Accuracy: 0.7978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5174 | 0.9966 | 218 | 1.3672 | 0.5855 |
| 1.282 | 1.9977 | 437 | 1.1843 | 0.6260 |
| 1.117 | 2.9989 | 656 | 1.0301 | 0.6845 |
| 1.0176 | 4.0 | 875 | 0.9670 | 0.7070 |
| 0.9912 | 4.9966 | 1093 | 0.8551 | 0.7477 |
| 0.9458 | 5.9977 | 1312 | 0.8534 | 0.7392 |
| 0.8502 | 6.9989 | 1531 | 0.8049 | 0.7600 |
| 0.8954 | 8.0 | 1750 | 0.7716 | 0.7683 |
| 0.872 | 8.9966 | 1968 | 0.7443 | 0.7779 |
| 0.8186 | 9.9977 | 2187 | 0.7304 | 0.7835 |
| 0.747 | 10.9989 | 2406 | 0.7178 | 0.7911 |
| 0.6843 | 12.0 | 2625 | 0.7062 | 0.7925 |
| 0.7453 | 12.9966 | 2843 | 0.7031 | 0.7939 |
| 0.7472 | 13.9977 | 3062 | 0.6891 | 0.7965 |
| 0.7067 | 14.9486 | 3270 | 0.6817 | 0.7978 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
[
"advertisement",
"budget",
"email",
"file_folder",
"form",
"handwritten",
"invoice",
"letter",
"memo",
"news_article",
"presentation",
"questionnaire",
"resume",
"scientific_publication",
"scientific_report",
"specification"
] |
Omriy123/vit_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q3_DA
|
<!-- 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_epochs5_batch32_lr5e-05_size224_tiles7_seed2_q3_DA
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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0877
- Accuracy: 0.9747
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0154 | 1.0 | 469 | 0.1104 | 0.9677 |
| 0.0674 | 2.0 | 938 | 0.1104 | 0.9723 |
| 0.0875 | 3.0 | 1407 | 0.0877 | 0.9747 |
| 0.0243 | 4.0 | 1876 | 0.0949 | 0.9773 |
| 0.0852 | 5.0 | 2345 | 0.0935 | 0.9787 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
[
"cat",
"dog"
] |
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