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# meat_calssify_fresh_crop_fixed_epoch_80_V_0_1

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5098
- Accuracy: 0.8462

## 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: 1
- 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1019        | 1.0   | 10   | 1.0993          | 0.3718   |
| 1.0872        | 2.0   | 20   | 1.0762          | 0.4167   |
| 1.0617        | 3.0   | 30   | 1.0498          | 0.4808   |
| 1.0264        | 4.0   | 40   | 1.0153          | 0.4872   |
| 0.9738        | 5.0   | 50   | 0.9431          | 0.5641   |
| 0.9038        | 6.0   | 60   | 0.9137          | 0.5449   |
| 0.8405        | 7.0   | 70   | 0.8209          | 0.6538   |
| 0.8131        | 8.0   | 80   | 0.8620          | 0.5897   |
| 0.7412        | 9.0   | 90   | 0.7370          | 0.6859   |
| 0.7006        | 10.0  | 100  | 0.7230          | 0.6987   |
| 0.6531        | 11.0  | 110  | 0.7679          | 0.6923   |
| 0.6215        | 12.0  | 120  | 0.6398          | 0.7308   |
| 0.5351        | 13.0  | 130  | 0.7016          | 0.7051   |
| 0.4847        | 14.0  | 140  | 0.5606          | 0.7949   |
| 0.4677        | 15.0  | 150  | 0.8849          | 0.6410   |
| 0.6042        | 16.0  | 160  | 0.5766          | 0.7756   |
| 0.4113        | 17.0  | 170  | 0.5573          | 0.7885   |
| 0.3662        | 18.0  | 180  | 0.6451          | 0.7179   |
| 0.3899        | 19.0  | 190  | 0.5613          | 0.7692   |
| 0.3518        | 20.0  | 200  | 0.6618          | 0.7051   |
| 0.303         | 21.0  | 210  | 0.5417          | 0.7756   |
| 0.2568        | 22.0  | 220  | 0.4785          | 0.8205   |
| 0.3098        | 23.0  | 230  | 0.6330          | 0.7564   |
| 0.3299        | 24.0  | 240  | 0.4944          | 0.8077   |
| 0.2373        | 25.0  | 250  | 0.5141          | 0.8141   |
| 0.2556        | 26.0  | 260  | 0.5719          | 0.8013   |
| 0.2387        | 27.0  | 270  | 0.5495          | 0.8013   |
| 0.2651        | 28.0  | 280  | 0.7409          | 0.7436   |
| 0.2909        | 29.0  | 290  | 0.6281          | 0.7821   |
| 0.2369        | 30.0  | 300  | 0.5067          | 0.8333   |
| 0.2084        | 31.0  | 310  | 0.5297          | 0.8077   |
| 0.2506        | 32.0  | 320  | 0.6124          | 0.7756   |
| 0.2395        | 33.0  | 330  | 0.5564          | 0.7692   |
| 0.2243        | 34.0  | 340  | 0.5176          | 0.7692   |
| 0.1951        | 35.0  | 350  | 0.5289          | 0.7949   |
| 0.1967        | 36.0  | 360  | 0.4829          | 0.8333   |
| 0.1602        | 37.0  | 370  | 0.5496          | 0.8205   |
| 0.1647        | 38.0  | 380  | 0.5969          | 0.7692   |
| 0.1772        | 39.0  | 390  | 0.6299          | 0.7949   |
| 0.1595        | 40.0  | 400  | 0.6386          | 0.7756   |
| 0.1801        | 41.0  | 410  | 0.5485          | 0.7885   |
| 0.1577        | 42.0  | 420  | 0.6692          | 0.7692   |
| 0.1683        | 43.0  | 430  | 0.5639          | 0.8077   |
| 0.1677        | 44.0  | 440  | 0.4369          | 0.8462   |
| 0.1367        | 45.0  | 450  | 0.5955          | 0.7756   |
| 0.1061        | 46.0  | 460  | 0.6644          | 0.8013   |
| 0.0957        | 47.0  | 470  | 0.5834          | 0.8077   |
| 0.1341        | 48.0  | 480  | 0.5541          | 0.8077   |
| 0.1153        | 49.0  | 490  | 0.6226          | 0.7885   |
| 0.122         | 50.0  | 500  | 0.5326          | 0.8269   |
| 0.1237        | 51.0  | 510  | 0.4428          | 0.8462   |
| 0.1006        | 52.0  | 520  | 0.5562          | 0.8269   |
| 0.1256        | 53.0  | 530  | 0.5066          | 0.8333   |
| 0.0995        | 54.0  | 540  | 0.6685          | 0.8013   |
| 0.1033        | 55.0  | 550  | 0.5183          | 0.8269   |
| 0.1177        | 56.0  | 560  | 0.6426          | 0.7692   |
| 0.1033        | 57.0  | 570  | 0.5079          | 0.8141   |
| 0.1389        | 58.0  | 580  | 0.5120          | 0.8205   |
| 0.0955        | 59.0  | 590  | 0.5381          | 0.8333   |
| 0.1108        | 60.0  | 600  | 0.5210          | 0.8526   |
| 0.1355        | 61.0  | 610  | 0.5460          | 0.8205   |
| 0.0897        | 62.0  | 620  | 0.4909          | 0.8269   |
| 0.084         | 63.0  | 630  | 0.5438          | 0.8205   |
| 0.082         | 64.0  | 640  | 0.5693          | 0.8269   |
| 0.1026        | 65.0  | 650  | 0.4864          | 0.8590   |
| 0.0872        | 66.0  | 660  | 0.4856          | 0.8397   |
| 0.0966        | 67.0  | 670  | 0.4073          | 0.8590   |
| 0.097         | 68.0  | 680  | 0.5848          | 0.8269   |
| 0.1007        | 69.0  | 690  | 0.4663          | 0.8205   |
| 0.0695        | 70.0  | 700  | 0.5000          | 0.8333   |
| 0.1048        | 71.0  | 710  | 0.6038          | 0.8141   |
| 0.0715        | 72.0  | 720  | 0.6008          | 0.8269   |
| 0.1061        | 73.0  | 730  | 0.5291          | 0.8269   |
| 0.0688        | 74.0  | 740  | 0.4124          | 0.8654   |
| 0.0638        | 75.0  | 750  | 0.5575          | 0.8205   |
| 0.0785        | 76.0  | 760  | 0.5537          | 0.8141   |
| 0.0758        | 77.0  | 770  | 0.4363          | 0.8718   |
| 0.0865        | 78.0  | 780  | 0.5200          | 0.8269   |
| 0.0844        | 79.0  | 790  | 0.6848          | 0.7949   |
| 0.0776        | 80.0  | 800  | 0.5098          | 0.8462   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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