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# meat_calssify_fresh_crop_fixed_epoch120_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: 0.5969
- Accuracy: 0.8165

## 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: 120

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0907        | 1.0   | 10   | 1.0937          | 0.3291   |
| 1.0777        | 2.0   | 20   | 1.0915          | 0.3354   |
| 1.0596        | 3.0   | 30   | 1.0801          | 0.3544   |
| 1.0333        | 4.0   | 40   | 1.0463          | 0.4810   |
| 0.9965        | 5.0   | 50   | 0.9945          | 0.5316   |
| 0.9588        | 6.0   | 60   | 0.9807          | 0.5127   |
| 0.9183        | 7.0   | 70   | 0.9944          | 0.5063   |
| 0.8966        | 8.0   | 80   | 1.0074          | 0.5127   |
| 0.8199        | 9.0   | 90   | 0.9472          | 0.5506   |
| 0.8089        | 10.0  | 100  | 0.9102          | 0.5886   |
| 0.7151        | 11.0  | 110  | 0.7697          | 0.6962   |
| 0.6578        | 12.0  | 120  | 0.9557          | 0.5570   |
| 0.6375        | 13.0  | 130  | 0.7262          | 0.6709   |
| 0.5994        | 14.0  | 140  | 0.7609          | 0.6709   |
| 0.5086        | 15.0  | 150  | 0.7112          | 0.7089   |
| 0.4771        | 16.0  | 160  | 0.6239          | 0.7658   |
| 0.4631        | 17.0  | 170  | 0.7229          | 0.7278   |
| 0.5158        | 18.0  | 180  | 0.8230          | 0.6835   |
| 0.4938        | 19.0  | 190  | 0.6708          | 0.7278   |
| 0.4287        | 20.0  | 200  | 0.5675          | 0.7722   |
| 0.3256        | 21.0  | 210  | 0.6100          | 0.7468   |
| 0.3555        | 22.0  | 220  | 0.6967          | 0.7342   |
| 0.3453        | 23.0  | 230  | 0.6273          | 0.7532   |
| 0.3391        | 24.0  | 240  | 0.7153          | 0.7468   |
| 0.3129        | 25.0  | 250  | 0.7745          | 0.6835   |
| 0.3296        | 26.0  | 260  | 0.7254          | 0.7089   |
| 0.2695        | 27.0  | 270  | 0.6191          | 0.7658   |
| 0.2704        | 28.0  | 280  | 0.6397          | 0.7342   |
| 0.2466        | 29.0  | 290  | 0.7419          | 0.7342   |
| 0.2655        | 30.0  | 300  | 0.6966          | 0.7532   |
| 0.2448        | 31.0  | 310  | 0.6931          | 0.7532   |
| 0.2286        | 32.0  | 320  | 0.5090          | 0.7975   |
| 0.2419        | 33.0  | 330  | 0.5987          | 0.7405   |
| 0.1965        | 34.0  | 340  | 0.7482          | 0.7405   |
| 0.197         | 35.0  | 350  | 0.8851          | 0.7089   |
| 0.1829        | 36.0  | 360  | 0.5874          | 0.7848   |
| 0.187         | 37.0  | 370  | 0.7249          | 0.7405   |
| 0.2011        | 38.0  | 380  | 0.6821          | 0.7468   |
| 0.1927        | 39.0  | 390  | 0.7133          | 0.7848   |
| 0.2043        | 40.0  | 400  | 0.6956          | 0.7468   |
| 0.1661        | 41.0  | 410  | 0.6911          | 0.7532   |
| 0.1625        | 42.0  | 420  | 0.7424          | 0.7405   |
| 0.1876        | 43.0  | 430  | 0.6222          | 0.7722   |
| 0.1865        | 44.0  | 440  | 0.6250          | 0.8101   |
| 0.1604        | 45.0  | 450  | 0.6950          | 0.7405   |
| 0.2072        | 46.0  | 460  | 0.8475          | 0.7025   |
| 0.1816        | 47.0  | 470  | 0.5856          | 0.7975   |
| 0.1683        | 48.0  | 480  | 0.6331          | 0.7911   |
| 0.1698        | 49.0  | 490  | 0.8147          | 0.7278   |
| 0.1724        | 50.0  | 500  | 0.5908          | 0.7848   |
| 0.1231        | 51.0  | 510  | 0.4522          | 0.8291   |
| 0.1121        | 52.0  | 520  | 0.5198          | 0.8291   |
| 0.1335        | 53.0  | 530  | 0.6377          | 0.7785   |
| 0.1414        | 54.0  | 540  | 0.6638          | 0.7722   |
| 0.1263        | 55.0  | 550  | 0.6548          | 0.7722   |
| 0.1427        | 56.0  | 560  | 0.6146          | 0.7785   |
| 0.1137        | 57.0  | 570  | 0.5344          | 0.8101   |
| 0.1504        | 58.0  | 580  | 0.7023          | 0.7785   |
| 0.1549        | 59.0  | 590  | 0.8293          | 0.7152   |
| 0.1142        | 60.0  | 600  | 0.7865          | 0.7658   |
| 0.1271        | 61.0  | 610  | 0.6282          | 0.8038   |
| 0.1134        | 62.0  | 620  | 0.7117          | 0.7658   |
| 0.0954        | 63.0  | 630  | 0.5500          | 0.8165   |
| 0.1011        | 64.0  | 640  | 0.5801          | 0.7911   |
| 0.0878        | 65.0  | 650  | 0.4268          | 0.8418   |
| 0.1065        | 66.0  | 660  | 0.6277          | 0.8038   |
| 0.1298        | 67.0  | 670  | 0.5940          | 0.8038   |
| 0.111         | 68.0  | 680  | 0.6945          | 0.7785   |
| 0.0955        | 69.0  | 690  | 0.6320          | 0.8038   |
| 0.0728        | 70.0  | 700  | 0.6484          | 0.7975   |
| 0.0893        | 71.0  | 710  | 0.5842          | 0.8165   |
| 0.0962        | 72.0  | 720  | 0.5417          | 0.8354   |
| 0.0963        | 73.0  | 730  | 0.7366          | 0.7848   |
| 0.1103        | 74.0  | 740  | 0.5413          | 0.8354   |
| 0.1145        | 75.0  | 750  | 0.6310          | 0.7911   |
| 0.1093        | 76.0  | 760  | 0.5101          | 0.8481   |
| 0.0934        | 77.0  | 770  | 0.5049          | 0.8101   |
| 0.0914        | 78.0  | 780  | 0.6828          | 0.7975   |
| 0.0739        | 79.0  | 790  | 0.8685          | 0.7595   |
| 0.1098        | 80.0  | 800  | 0.5542          | 0.7975   |
| 0.0748        | 81.0  | 810  | 0.4864          | 0.8354   |
| 0.0763        | 82.0  | 820  | 0.6708          | 0.7785   |
| 0.0805        | 83.0  | 830  | 0.5730          | 0.8291   |
| 0.0936        | 84.0  | 840  | 0.5680          | 0.8228   |
| 0.0664        | 85.0  | 850  | 0.5645          | 0.8228   |
| 0.0932        | 86.0  | 860  | 0.4601          | 0.8608   |
| 0.0846        | 87.0  | 870  | 0.7324          | 0.7911   |
| 0.0799        | 88.0  | 880  | 0.6234          | 0.8101   |
| 0.0707        | 89.0  | 890  | 0.4808          | 0.8544   |
| 0.0626        | 90.0  | 900  | 0.6119          | 0.7848   |
| 0.066         | 91.0  | 910  | 0.5173          | 0.8228   |
| 0.0701        | 92.0  | 920  | 0.7111          | 0.7722   |
| 0.0862        | 93.0  | 930  | 0.6035          | 0.7975   |
| 0.0397        | 94.0  | 940  | 0.5329          | 0.8418   |
| 0.095         | 95.0  | 950  | 0.6635          | 0.7911   |
| 0.0688        | 96.0  | 960  | 0.4878          | 0.8734   |
| 0.07          | 97.0  | 970  | 0.5253          | 0.8608   |
| 0.0704        | 98.0  | 980  | 0.4443          | 0.8608   |
| 0.0883        | 99.0  | 990  | 0.5571          | 0.8165   |
| 0.064         | 100.0 | 1000 | 0.7047          | 0.7911   |
| 0.0547        | 101.0 | 1010 | 0.6558          | 0.8101   |
| 0.0686        | 102.0 | 1020 | 0.6330          | 0.8165   |
| 0.0806        | 103.0 | 1030 | 0.5754          | 0.8354   |
| 0.0481        | 104.0 | 1040 | 0.5074          | 0.8544   |
| 0.0499        | 105.0 | 1050 | 0.6701          | 0.8165   |
| 0.0703        | 106.0 | 1060 | 0.6151          | 0.8291   |
| 0.0921        | 107.0 | 1070 | 0.5935          | 0.8354   |
| 0.0426        | 108.0 | 1080 | 0.6534          | 0.7975   |
| 0.0618        | 109.0 | 1090 | 0.5265          | 0.8165   |
| 0.0597        | 110.0 | 1100 | 0.5604          | 0.8354   |
| 0.0471        | 111.0 | 1110 | 0.5451          | 0.8354   |
| 0.0541        | 112.0 | 1120 | 0.5182          | 0.8544   |
| 0.0369        | 113.0 | 1130 | 0.5276          | 0.8291   |
| 0.0571        | 114.0 | 1140 | 0.4766          | 0.8354   |
| 0.0469        | 115.0 | 1150 | 0.6508          | 0.8101   |
| 0.0877        | 116.0 | 1160 | 0.5894          | 0.8418   |
| 0.0681        | 117.0 | 1170 | 0.4952          | 0.8418   |
| 0.0303        | 118.0 | 1180 | 0.5804          | 0.8418   |
| 0.0536        | 119.0 | 1190 | 0.7055          | 0.8101   |
| 0.0576        | 120.0 | 1200 | 0.5969          | 0.8165   |


### Framework versions

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

fresh1 fresh2 fresh3