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# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_5

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: 0.2106
- Accuracy: 0.9315

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1058        | 1.0   | 21   | 1.0981          | 0.3707   |
| 1.0889        | 2.0   | 42   | 1.0703          | 0.4673   |
| 1.0551        | 3.0   | 63   | 1.0219          | 0.5265   |
| 0.9782        | 4.0   | 84   | 0.9533          | 0.5389   |
| 0.916         | 5.0   | 105  | 0.8848          | 0.6293   |
| 0.8579        | 6.0   | 126  | 0.7977          | 0.6417   |
| 0.7614        | 7.0   | 147  | 0.8384          | 0.6511   |
| 0.7643        | 8.0   | 168  | 0.6850          | 0.7632   |
| 0.6182        | 9.0   | 189  | 0.7255          | 0.7134   |
| 0.5827        | 10.0  | 210  | 0.7923          | 0.6604   |
| 0.5751        | 11.0  | 231  | 0.6706          | 0.7196   |
| 0.5045        | 12.0  | 252  | 0.5499          | 0.7726   |
| 0.473         | 13.0  | 273  | 0.7024          | 0.6885   |
| 0.4922        | 14.0  | 294  | 0.6377          | 0.7165   |
| 0.4808        | 15.0  | 315  | 0.4886          | 0.8224   |
| 0.4047        | 16.0  | 336  | 0.4426          | 0.8318   |
| 0.3982        | 17.0  | 357  | 0.5656          | 0.7508   |
| 0.3279        | 18.0  | 378  | 0.5265          | 0.7819   |
| 0.3421        | 19.0  | 399  | 0.4606          | 0.8100   |
| 0.2593        | 20.0  | 420  | 0.4638          | 0.8100   |
| 0.2542        | 21.0  | 441  | 0.4388          | 0.8474   |
| 0.2554        | 22.0  | 462  | 0.5237          | 0.8100   |
| 0.2488        | 23.0  | 483  | 0.4238          | 0.8474   |
| 0.2069        | 24.0  | 504  | 0.3515          | 0.8847   |
| 0.295         | 25.0  | 525  | 0.4371          | 0.8318   |
| 0.2213        | 26.0  | 546  | 0.3588          | 0.8847   |
| 0.1961        | 27.0  | 567  | 0.5877          | 0.7975   |
| 0.27          | 28.0  | 588  | 0.3720          | 0.8567   |
| 0.1791        | 29.0  | 609  | 0.2952          | 0.9065   |
| 0.1763        | 30.0  | 630  | 0.3312          | 0.8816   |
| 0.1664        | 31.0  | 651  | 0.3770          | 0.8754   |
| 0.2713        | 32.0  | 672  | 0.4695          | 0.8287   |
| 0.1645        | 33.0  | 693  | 0.4069          | 0.8505   |
| 0.1942        | 34.0  | 714  | 0.4516          | 0.8380   |
| 0.1435        | 35.0  | 735  | 0.2383          | 0.9252   |
| 0.1399        | 36.0  | 756  | 0.4790          | 0.8442   |
| 0.1615        | 37.0  | 777  | 0.3230          | 0.8785   |
| 0.1405        | 38.0  | 798  | 0.2635          | 0.9065   |
| 0.1569        | 39.0  | 819  | 0.4816          | 0.8598   |
| 0.1332        | 40.0  | 840  | 0.2859          | 0.8847   |
| 0.1108        | 41.0  | 861  | 0.3786          | 0.8723   |
| 0.1014        | 42.0  | 882  | 0.3215          | 0.9003   |
| 0.1186        | 43.0  | 903  | 0.3652          | 0.8785   |
| 0.1021        | 44.0  | 924  | 0.2088          | 0.9221   |
| 0.1444        | 45.0  | 945  | 0.3646          | 0.8723   |
| 0.1058        | 46.0  | 966  | 0.3530          | 0.8847   |
| 0.1297        | 47.0  | 987  | 0.4002          | 0.8629   |
| 0.0997        | 48.0  | 1008 | 0.2928          | 0.9034   |
| 0.1246        | 49.0  | 1029 | 0.2772          | 0.9065   |
| 0.0989        | 50.0  | 1050 | 0.2459          | 0.9221   |
| 0.0794        | 51.0  | 1071 | 0.1970          | 0.9283   |
| 0.0698        | 52.0  | 1092 | 0.3217          | 0.8847   |
| 0.0767        | 53.0  | 1113 | 0.2706          | 0.9190   |
| 0.0966        | 54.0  | 1134 | 0.2246          | 0.9252   |
| 0.0816        | 55.0  | 1155 | 0.2585          | 0.9065   |
| 0.0732        | 56.0  | 1176 | 0.3289          | 0.8910   |
| 0.0992        | 57.0  | 1197 | 0.2790          | 0.9128   |
| 0.0684        | 58.0  | 1218 | 0.2508          | 0.9252   |
| 0.0972        | 59.0  | 1239 | 0.2558          | 0.9190   |
| 0.0702        | 60.0  | 1260 | 0.2411          | 0.9190   |
| 0.0602        | 61.0  | 1281 | 0.4097          | 0.8660   |
| 0.0912        | 62.0  | 1302 | 0.2274          | 0.9252   |
| 0.0556        | 63.0  | 1323 | 0.1940          | 0.9408   |
| 0.0727        | 64.0  | 1344 | 0.2389          | 0.9190   |
| 0.0657        | 65.0  | 1365 | 0.2964          | 0.9128   |
| 0.0486        | 66.0  | 1386 | 0.2597          | 0.9252   |
| 0.0639        | 67.0  | 1407 | 0.2272          | 0.9346   |
| 0.0614        | 68.0  | 1428 | 0.1927          | 0.9470   |
| 0.0444        | 69.0  | 1449 | 0.2771          | 0.9190   |
| 0.0648        | 70.0  | 1470 | 0.2345          | 0.9283   |
| 0.051         | 71.0  | 1491 | 0.2210          | 0.9159   |
| 0.0514        | 72.0  | 1512 | 0.2260          | 0.9346   |
| 0.0473        | 73.0  | 1533 | 0.2496          | 0.9252   |
| 0.0637        | 74.0  | 1554 | 0.3152          | 0.9128   |
| 0.0538        | 75.0  | 1575 | 0.2527          | 0.9221   |
| 0.0622        | 76.0  | 1596 | 0.2148          | 0.9408   |
| 0.0437        | 77.0  | 1617 | 0.2386          | 0.9190   |
| 0.07          | 78.0  | 1638 | 0.2013          | 0.9315   |
| 0.0599        | 79.0  | 1659 | 0.2532          | 0.9346   |
| 0.0367        | 80.0  | 1680 | 0.1835          | 0.9439   |
| 0.0386        | 81.0  | 1701 | 0.2204          | 0.9283   |
| 0.0372        | 82.0  | 1722 | 0.2417          | 0.9283   |
| 0.0516        | 83.0  | 1743 | 0.3098          | 0.9190   |
| 0.0378        | 84.0  | 1764 | 0.1587          | 0.9533   |
| 0.0371        | 85.0  | 1785 | 0.2041          | 0.9377   |
| 0.0378        | 86.0  | 1806 | 0.2343          | 0.9377   |
| 0.0288        | 87.0  | 1827 | 0.1963          | 0.9439   |
| 0.0272        | 88.0  | 1848 | 0.2122          | 0.9408   |
| 0.0293        | 89.0  | 1869 | 0.0979          | 0.9751   |
| 0.037         | 90.0  | 1890 | 0.2385          | 0.9221   |
| 0.0453        | 91.0  | 1911 | 0.2056          | 0.9439   |
| 0.0478        | 92.0  | 1932 | 0.1861          | 0.9439   |
| 0.0241        | 93.0  | 1953 | 0.2129          | 0.9470   |
| 0.0404        | 94.0  | 1974 | 0.1806          | 0.9502   |
| 0.0224        | 95.0  | 1995 | 0.1698          | 0.9377   |
| 0.0194        | 96.0  | 2016 | 0.1960          | 0.9533   |
| 0.022         | 97.0  | 2037 | 0.2140          | 0.9377   |
| 0.0328        | 98.0  | 2058 | 0.1684          | 0.9502   |
| 0.0443        | 99.0  | 2079 | 0.2536          | 0.9283   |
| 0.0386        | 100.0 | 2100 | 0.2106          | 0.9315   |


### Framework versions

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

fresh1 fresh2 fresh3