model_id stringlengths 7 105 | model_card stringlengths 1 130k | model_labels listlengths 2 80k |
|---|---|---|
TamalSarker777/my_bean_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_bean_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2302
- Accuracy: 0.9517
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0364 | 1.0 | 13 | 0.7647 | 0.8744 |
| 0.6946 | 2.0 | 26 | 0.4217 | 0.9130 |
| 0.4294 | 3.0 | 39 | 0.2887 | 0.9517 |
| 0.2405 | 4.0 | 52 | 0.2417 | 0.9372 |
| 0.2118 | 5.0 | 65 | 0.2281 | 0.9614 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
| [
"angular_leaf_spot",
"bean_rust",
"healthy"
] |
gspeech/workout-hk3 |
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deepmaster/72_0 |
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plumpyfield/natix_v2-020 |
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plumpyfield/natix_v2-021 |
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plumpyfield/natix_v2-022 |
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plumpyfield/natix_v2-023 |
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plumpyfield/natix_v2-024 |
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plumpyfield/natix_v2-025 |
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gspeech/workout-hk3-uid4 |
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love-mimi/sn72-model-34 |
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love-mimi/sn72-model-7 |
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love-mimi/sn72-model-9 |
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love-mimi/sn72-model-19 |
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love-mimi/sn72-model-68 |
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deepmaster/72_3 |
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deepmaster/72_4 |
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deepmaster/72_5 |
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deepmaster/72_6 |
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deepmaster/72_7 |
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deepmaster/72_8 |
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deepmaster/72_9 |
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deepmaster/72_10 |
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deepmaster/72_11 |
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deepmaster/72_12 |
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deepmaster/72_13 |
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deepmaster/72_14 |
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legalaspro/vit-base-oxford-iiit-pets |
<!-- 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-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- Accuracy: 0.9486
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3577 | 1.0 | 370 | 0.3177 | 0.9161 |
| 0.2052 | 2.0 | 740 | 0.2434 | 0.9364 |
| 0.1704 | 3.0 | 1110 | 0.2237 | 0.9472 |
| 0.1322 | 4.0 | 1480 | 0.2176 | 0.9418 |
| 0.1309 | 5.0 | 1850 | 0.2134 | 0.9418 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
| [
"siamese",
"birman",
"shiba inu",
"staffordshire bull terrier",
"basset hound",
"bombay",
"japanese chin",
"chihuahua",
"german shorthaired",
"pomeranian",
"beagle",
"english cocker spaniel",
"american pit bull terrier",
"ragdoll",
"persian",
"egyptian mau",
"miniature pinscher",
"... |
love-mimi/sn72-model-79 |
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love-mimi/sn72-model-174 |
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love-mimi/sn72-model-125 |
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love-mimi/sn72-model-179 |
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eiitndidkwh/roadwork-2 |
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orgart/nat4 |
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orgart/nat5 |
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orgart/nat6 |
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James4u/5EvEkpTzU3bER8hrGkZrk97edd5LJxJBA77pPvJ8LGGFnBGi |
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legalaspro/oxford-pets-vit-from-scratch |
<!-- 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. -->
# oxford-pets-vit-from-scratch
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2803
- Accuracy: 0.1231
## 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: 0.0003
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6195 | 1.0 | 108 | 3.6024 | 0.0352 |
| 3.5561 | 2.0 | 216 | 3.5537 | 0.0656 |
| 3.5081 | 3.0 | 324 | 3.5253 | 0.0622 |
| 3.4711 | 4.0 | 432 | 3.4822 | 0.0812 |
| 3.3975 | 5.0 | 540 | 3.4285 | 0.0825 |
| 3.358 | 6.0 | 648 | 3.3771 | 0.0907 |
| 3.2989 | 7.0 | 756 | 3.3508 | 0.1035 |
| 3.2387 | 8.0 | 864 | 3.3233 | 0.1049 |
| 3.1679 | 9.0 | 972 | 3.3016 | 0.1150 |
| 3.1138 | 10.0 | 1080 | 3.2803 | 0.1231 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
| [
"abyssinian",
"bengal",
"birman",
"bombay",
"british shorthair",
"egyptian mau",
"maine coon",
"persian",
"ragdoll",
"russian blue",
"siamese",
"sphynx",
"american bulldog",
"american pit bull terrier",
"basset hound",
"beagle",
"boxer",
"chihuahua",
"english cocker spaniel",
"... |
legalaspro/oxford-pets-vit-with-kd |
<!-- 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. -->
# oxford-pets-vit-with-kd
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1434
- Accuracy: 0.1407
## 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: 0.0003
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.5042 | 1.0 | 108 | 4.4437 | 0.0474 |
| 4.4359 | 2.0 | 216 | 4.4182 | 0.0535 |
| 4.4074 | 3.0 | 324 | 4.3997 | 0.0704 |
| 4.3821 | 4.0 | 432 | 4.3765 | 0.0724 |
| 4.3157 | 5.0 | 540 | 4.3176 | 0.0853 |
| 4.2545 | 6.0 | 648 | 4.2902 | 0.0839 |
| 4.1764 | 7.0 | 756 | 4.2478 | 0.1089 |
| 4.1407 | 8.0 | 864 | 4.1937 | 0.1184 |
| 4.0407 | 9.0 | 972 | 4.1630 | 0.1313 |
| 4.0023 | 10.0 | 1080 | 4.1434 | 0.1407 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
| [
"abyssinian",
"bengal",
"birman",
"bombay",
"british shorthair",
"egyptian mau",
"maine coon",
"persian",
"ragdoll",
"russian blue",
"siamese",
"sphynx",
"american bulldog",
"american pit bull terrier",
"basset hound",
"beagle",
"boxer",
"chihuahua",
"english cocker spaniel",
"... |
Starcorsair/toothpick-classifier |
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"whole",
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