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# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_3

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: 0.1880
- Accuracy: 0.9408

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0748        | 1.0   | 21   | 1.0941          | 0.4019   |
| 1.056         | 2.0   | 42   | 1.0704          | 0.4517   |
| 1.0196        | 3.0   | 63   | 0.9922          | 0.5171   |
| 0.9144        | 4.0   | 84   | 0.9600          | 0.5140   |
| 0.9096        | 5.0   | 105  | 0.9206          | 0.5514   |
| 0.786         | 6.0   | 126  | 0.8006          | 0.6511   |
| 0.7149        | 7.0   | 147  | 0.7398          | 0.7196   |
| 0.6742        | 8.0   | 168  | 0.8100          | 0.6542   |
| 0.681         | 9.0   | 189  | 0.7297          | 0.6760   |
| 0.5929        | 10.0  | 210  | 0.7184          | 0.6854   |
| 0.5621        | 11.0  | 231  | 0.7011          | 0.7165   |
| 0.4628        | 12.0  | 252  | 0.6673          | 0.7196   |
| 0.4278        | 13.0  | 273  | 0.7029          | 0.7445   |
| 0.4525        | 14.0  | 294  | 0.6493          | 0.7477   |
| 0.3483        | 15.0  | 315  | 0.6969          | 0.7134   |
| 0.4328        | 16.0  | 336  | 0.5270          | 0.8006   |
| 0.3657        | 17.0  | 357  | 0.5653          | 0.7570   |
| 0.3047        | 18.0  | 378  | 0.4854          | 0.8131   |
| 0.2507        | 19.0  | 399  | 0.4555          | 0.8505   |
| 0.2468        | 20.0  | 420  | 0.5035          | 0.8131   |
| 0.2336        | 21.0  | 441  | 0.7171          | 0.7601   |
| 0.2954        | 22.0  | 462  | 0.4171          | 0.8536   |
| 0.2398        | 23.0  | 483  | 0.5465          | 0.7850   |
| 0.2538        | 24.0  | 504  | 0.5179          | 0.8069   |
| 0.21          | 25.0  | 525  | 0.3688          | 0.8723   |
| 0.1938        | 26.0  | 546  | 0.3997          | 0.8442   |
| 0.171         | 27.0  | 567  | 0.5068          | 0.8224   |
| 0.1983        | 28.0  | 588  | 0.4238          | 0.8380   |
| 0.1839        | 29.0  | 609  | 0.4431          | 0.8380   |
| 0.1977        | 30.0  | 630  | 0.3608          | 0.8598   |
| 0.1545        | 31.0  | 651  | 0.4898          | 0.8536   |
| 0.2214        | 32.0  | 672  | 0.5862          | 0.7850   |
| 0.185         | 33.0  | 693  | 0.3682          | 0.8785   |
| 0.1238        | 34.0  | 714  | 0.4300          | 0.8380   |
| 0.1424        | 35.0  | 735  | 0.5039          | 0.8287   |
| 0.1538        | 36.0  | 756  | 0.5649          | 0.8193   |
| 0.1806        | 37.0  | 777  | 0.3727          | 0.8505   |
| 0.1038        | 38.0  | 798  | 0.4984          | 0.8162   |
| 0.1241        | 39.0  | 819  | 0.3025          | 0.8941   |
| 0.1197        | 40.0  | 840  | 0.3038          | 0.8847   |
| 0.1288        | 41.0  | 861  | 0.5481          | 0.8100   |
| 0.1232        | 42.0  | 882  | 0.4011          | 0.8660   |
| 0.1308        | 43.0  | 903  | 0.3017          | 0.8910   |
| 0.0803        | 44.0  | 924  | 0.4368          | 0.8567   |
| 0.0893        | 45.0  | 945  | 0.3961          | 0.8660   |
| 0.1279        | 46.0  | 966  | 0.4143          | 0.8629   |
| 0.1105        | 47.0  | 987  | 0.3773          | 0.8598   |
| 0.0877        | 48.0  | 1008 | 0.3716          | 0.8816   |
| 0.0951        | 49.0  | 1029 | 0.3312          | 0.8847   |
| 0.0941        | 50.0  | 1050 | 0.2714          | 0.8910   |
| 0.073         | 51.0  | 1071 | 0.3475          | 0.8910   |
| 0.0878        | 52.0  | 1092 | 0.3918          | 0.8847   |
| 0.0898        | 53.0  | 1113 | 0.4729          | 0.8442   |
| 0.0849        | 54.0  | 1134 | 0.4245          | 0.8692   |
| 0.1619        | 55.0  | 1155 | 0.3289          | 0.9065   |
| 0.0838        | 56.0  | 1176 | 0.2787          | 0.9159   |
| 0.0767        | 57.0  | 1197 | 0.2738          | 0.9128   |
| 0.0815        | 58.0  | 1218 | 0.2729          | 0.9128   |
| 0.0747        | 59.0  | 1239 | 0.2036          | 0.9377   |
| 0.0629        | 60.0  | 1260 | 0.2615          | 0.9221   |
| 0.0561        | 61.0  | 1281 | 0.3424          | 0.8910   |
| 0.0666        | 62.0  | 1302 | 0.3222          | 0.8941   |
| 0.0759        | 63.0  | 1323 | 0.3462          | 0.9065   |
| 0.0548        | 64.0  | 1344 | 0.3463          | 0.8972   |
| 0.0607        | 65.0  | 1365 | 0.2171          | 0.9283   |
| 0.0796        | 66.0  | 1386 | 0.3879          | 0.8847   |
| 0.0651        | 67.0  | 1407 | 0.2649          | 0.9159   |
| 0.0615        | 68.0  | 1428 | 0.2469          | 0.9221   |
| 0.0495        | 69.0  | 1449 | 0.2899          | 0.9252   |
| 0.0511        | 70.0  | 1470 | 0.2891          | 0.9065   |
| 0.0487        | 71.0  | 1491 | 0.2990          | 0.9159   |
| 0.0593        | 72.0  | 1512 | 0.3046          | 0.9128   |
| 0.0484        | 73.0  | 1533 | 0.2865          | 0.9065   |
| 0.0534        | 74.0  | 1554 | 0.2614          | 0.9128   |
| 0.0446        | 75.0  | 1575 | 0.3311          | 0.8972   |
| 0.0478        | 76.0  | 1596 | 0.2580          | 0.9159   |
| 0.0335        | 77.0  | 1617 | 0.3392          | 0.9159   |
| 0.0436        | 78.0  | 1638 | 0.3400          | 0.9034   |
| 0.07          | 79.0  | 1659 | 0.3434          | 0.9034   |
| 0.0536        | 80.0  | 1680 | 0.3456          | 0.8972   |
| 0.0431        | 81.0  | 1701 | 0.2386          | 0.9408   |
| 0.0381        | 82.0  | 1722 | 0.2401          | 0.9346   |
| 0.0423        | 83.0  | 1743 | 0.2421          | 0.9346   |
| 0.0393        | 84.0  | 1764 | 0.1979          | 0.9439   |
| 0.0393        | 85.0  | 1785 | 0.2756          | 0.9190   |
| 0.0395        | 86.0  | 1806 | 0.3339          | 0.8972   |
| 0.031         | 87.0  | 1827 | 0.2471          | 0.9252   |
| 0.0227        | 88.0  | 1848 | 0.2182          | 0.9346   |
| 0.0392        | 89.0  | 1869 | 0.2732          | 0.9221   |
| 0.0536        | 90.0  | 1890 | 0.2579          | 0.9283   |
| 0.0426        | 91.0  | 1911 | 0.2062          | 0.9315   |
| 0.0344        | 92.0  | 1932 | 0.2209          | 0.9252   |
| 0.0333        | 93.0  | 1953 | 0.1584          | 0.9564   |
| 0.0338        | 94.0  | 1974 | 0.2976          | 0.9128   |
| 0.0391        | 95.0  | 1995 | 0.2420          | 0.9377   |
| 0.0302        | 96.0  | 2016 | 0.2694          | 0.9159   |
| 0.0268        | 97.0  | 2037 | 0.2610          | 0.9221   |
| 0.0402        | 98.0  | 2058 | 0.2952          | 0.9159   |
| 0.0172        | 99.0  | 2079 | 0.1870          | 0.9470   |
| 0.0241        | 100.0 | 2100 | 0.1880          | 0.9408   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1

fresh1 fresh2 fresh3