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# meat_calssify_fresh_crop_fixed_epoch100_V_0_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6095
- Accuracy: 0.7975

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0941        | 1.0   | 10   | 1.0929          | 0.3418   |
| 1.0817        | 2.0   | 20   | 1.0775          | 0.4051   |
| 1.059         | 3.0   | 30   | 1.0631          | 0.4494   |
| 1.028         | 4.0   | 40   | 1.0305          | 0.4747   |
| 0.9784        | 5.0   | 50   | 1.0019          | 0.4747   |
| 0.9123        | 6.0   | 60   | 0.9389          | 0.5759   |
| 0.8789        | 7.0   | 70   | 0.8875          | 0.5949   |
| 0.816         | 8.0   | 80   | 0.8561          | 0.6203   |
| 0.7632        | 9.0   | 90   | 0.8253          | 0.6266   |
| 0.6857        | 10.0  | 100  | 0.8264          | 0.6203   |
| 0.6834        | 11.0  | 110  | 0.7056          | 0.6962   |
| 0.629         | 12.0  | 120  | 0.7708          | 0.6329   |
| 0.5744        | 13.0  | 130  | 0.6847          | 0.6962   |
| 0.5661        | 14.0  | 140  | 0.6881          | 0.7215   |
| 0.5516        | 15.0  | 150  | 0.7477          | 0.6646   |
| 0.482         | 16.0  | 160  | 0.6717          | 0.7152   |
| 0.4265        | 17.0  | 170  | 0.6200          | 0.7468   |
| 0.4074        | 18.0  | 180  | 0.6404          | 0.7278   |
| 0.3797        | 19.0  | 190  | 0.6577          | 0.7405   |
| 0.3895        | 20.0  | 200  | 0.6127          | 0.7658   |
| 0.3244        | 21.0  | 210  | 0.6776          | 0.7658   |
| 0.3764        | 22.0  | 220  | 0.8015          | 0.6899   |
| 0.3692        | 23.0  | 230  | 0.6790          | 0.7278   |
| 0.2687        | 24.0  | 240  | 0.6951          | 0.7215   |
| 0.3352        | 25.0  | 250  | 0.7140          | 0.7215   |
| 0.2734        | 26.0  | 260  | 0.6895          | 0.7152   |
| 0.2857        | 27.0  | 270  | 0.6515          | 0.7089   |
| 0.2716        | 28.0  | 280  | 0.6171          | 0.7405   |
| 0.2628        | 29.0  | 290  | 0.5954          | 0.7532   |
| 0.222         | 30.0  | 300  | 0.6447          | 0.7342   |
| 0.2458        | 31.0  | 310  | 0.6836          | 0.7532   |
| 0.2489        | 32.0  | 320  | 0.5701          | 0.7975   |
| 0.2282        | 33.0  | 330  | 0.6654          | 0.7405   |
| 0.1824        | 34.0  | 340  | 0.6552          | 0.7468   |
| 0.2261        | 35.0  | 350  | 0.7548          | 0.7342   |
| 0.2198        | 36.0  | 360  | 0.6297          | 0.7785   |
| 0.2118        | 37.0  | 370  | 0.6240          | 0.7911   |
| 0.1751        | 38.0  | 380  | 0.6787          | 0.7722   |
| 0.1507        | 39.0  | 390  | 0.5897          | 0.7911   |
| 0.1647        | 40.0  | 400  | 0.6010          | 0.7975   |
| 0.2214        | 41.0  | 410  | 0.6143          | 0.7975   |
| 0.1462        | 42.0  | 420  | 0.8883          | 0.7278   |
| 0.1841        | 43.0  | 430  | 0.7459          | 0.7532   |
| 0.2076        | 44.0  | 440  | 0.6125          | 0.8101   |
| 0.1359        | 45.0  | 450  | 0.5540          | 0.8101   |
| 0.1315        | 46.0  | 460  | 0.7218          | 0.7532   |
| 0.1658        | 47.0  | 470  | 0.7927          | 0.7278   |
| 0.1807        | 48.0  | 480  | 0.6954          | 0.7911   |
| 0.1601        | 49.0  | 490  | 0.6399          | 0.7595   |
| 0.1385        | 50.0  | 500  | 0.6353          | 0.7532   |
| 0.1387        | 51.0  | 510  | 0.6596          | 0.7658   |
| 0.1435        | 52.0  | 520  | 0.5697          | 0.8165   |
| 0.1116        | 53.0  | 530  | 0.6201          | 0.8165   |
| 0.0899        | 54.0  | 540  | 0.5805          | 0.8101   |
| 0.1245        | 55.0  | 550  | 0.7132          | 0.7785   |
| 0.1309        | 56.0  | 560  | 0.6173          | 0.7911   |
| 0.1176        | 57.0  | 570  | 0.6650          | 0.8038   |
| 0.1516        | 58.0  | 580  | 0.7006          | 0.7342   |
| 0.1359        | 59.0  | 590  | 0.7015          | 0.7785   |
| 0.134         | 60.0  | 600  | 0.6239          | 0.7975   |
| 0.1167        | 61.0  | 610  | 0.5665          | 0.7848   |
| 0.127         | 62.0  | 620  | 0.5368          | 0.8038   |
| 0.1306        | 63.0  | 630  | 0.4862          | 0.8544   |
| 0.0919        | 64.0  | 640  | 0.6305          | 0.7595   |
| 0.1082        | 65.0  | 650  | 0.6479          | 0.7848   |
| 0.1484        | 66.0  | 660  | 0.6687          | 0.7785   |
| 0.1066        | 67.0  | 670  | 0.5404          | 0.8101   |
| 0.1011        | 68.0  | 680  | 0.4724          | 0.8797   |
| 0.0891        | 69.0  | 690  | 0.5482          | 0.8354   |
| 0.1011        | 70.0  | 700  | 0.7259          | 0.7975   |
| 0.0819        | 71.0  | 710  | 0.6372          | 0.7911   |
| 0.0943        | 72.0  | 720  | 0.5851          | 0.7975   |
| 0.0638        | 73.0  | 730  | 0.5816          | 0.8101   |
| 0.0875        | 74.0  | 740  | 0.7538          | 0.7595   |
| 0.1146        | 75.0  | 750  | 0.5902          | 0.8165   |
| 0.0861        | 76.0  | 760  | 0.5353          | 0.8354   |
| 0.1031        | 77.0  | 770  | 0.5022          | 0.8101   |
| 0.0721        | 78.0  | 780  | 0.5100          | 0.8544   |
| 0.0752        | 79.0  | 790  | 0.6330          | 0.7785   |
| 0.0753        | 80.0  | 800  | 0.5908          | 0.7848   |
| 0.0602        | 81.0  | 810  | 0.6954          | 0.7658   |
| 0.082         | 82.0  | 820  | 0.4405          | 0.8671   |
| 0.0905        | 83.0  | 830  | 0.5115          | 0.8481   |
| 0.0597        | 84.0  | 840  | 0.5156          | 0.8608   |
| 0.0716        | 85.0  | 850  | 0.5273          | 0.8228   |
| 0.0606        | 86.0  | 860  | 0.6440          | 0.8354   |
| 0.0736        | 87.0  | 870  | 0.5842          | 0.8354   |
| 0.0614        | 88.0  | 880  | 0.5470          | 0.8354   |
| 0.0496        | 89.0  | 890  | 0.5201          | 0.8228   |
| 0.067         | 90.0  | 900  | 0.5866          | 0.8228   |
| 0.059         | 91.0  | 910  | 0.5842          | 0.8354   |
| 0.0525        | 92.0  | 920  | 0.5256          | 0.8418   |
| 0.0928        | 93.0  | 930  | 0.6557          | 0.8101   |
| 0.0736        | 94.0  | 940  | 0.6496          | 0.8101   |
| 0.064         | 95.0  | 950  | 0.5068          | 0.8418   |
| 0.0654        | 96.0  | 960  | 0.4680          | 0.8291   |
| 0.0426        | 97.0  | 970  | 0.5116          | 0.8608   |
| 0.0515        | 98.0  | 980  | 0.4887          | 0.8608   |
| 0.0466        | 99.0  | 990  | 0.5188          | 0.8228   |
| 0.0746        | 100.0 | 1000 | 0.6095          | 0.7975   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1

fresh1 fresh2 fresh3