
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_2

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: 0.2175
- Accuracy: 0.9283

## 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: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1008        | 1.0   | 11   | 1.0912          | 0.3583   |
| 1.0751        | 2.0   | 22   | 1.0569          | 0.5140   |
| 1.0562        | 3.0   | 33   | 1.0284          | 0.4891   |
| 0.9901        | 4.0   | 44   | 0.9771          | 0.5607   |
| 0.9179        | 5.0   | 55   | 0.9142          | 0.5888   |
| 0.8217        | 6.0   | 66   | 0.8546          | 0.6262   |
| 0.7811        | 7.0   | 77   | 0.7960          | 0.6791   |
| 0.8756        | 8.0   | 88   | 0.7693          | 0.6760   |
| 0.8095        | 9.0   | 99   | 0.7796          | 0.6636   |
| 0.6492        | 10.0  | 110  | 0.7908          | 0.6760   |
| 0.6357        | 11.0  | 121  | 0.7367          | 0.6885   |
| 0.6184        | 12.0  | 132  | 0.7575          | 0.6542   |
| 0.5371        | 13.0  | 143  | 0.5625          | 0.8069   |
| 0.5586        | 14.0  | 154  | 0.5400          | 0.7819   |
| 0.4235        | 15.0  | 165  | 0.5775          | 0.7664   |
| 0.5082        | 16.0  | 176  | 0.5360          | 0.7819   |
| 0.3758        | 17.0  | 187  | 0.5193          | 0.8131   |
| 0.3729        | 18.0  | 198  | 0.6018          | 0.7695   |
| 0.5911        | 19.0  | 209  | 0.4724          | 0.8224   |
| 0.3055        | 20.0  | 220  | 0.4877          | 0.8162   |
| 0.3054        | 21.0  | 231  | 0.5504          | 0.7726   |
| 0.2947        | 22.0  | 242  | 0.5059          | 0.8069   |
| 0.2336        | 23.0  | 253  | 0.4085          | 0.8598   |
| 0.2806        | 24.0  | 264  | 0.5123          | 0.8193   |
| 0.2782        | 25.0  | 275  | 0.4825          | 0.8131   |
| 0.2396        | 26.0  | 286  | 0.3329          | 0.8910   |
| 0.1937        | 27.0  | 297  | 0.3984          | 0.8816   |
| 0.5237        | 28.0  | 308  | 0.5059          | 0.8224   |
| 0.1951        | 29.0  | 319  | 0.6188          | 0.7757   |
| 0.2097        | 30.0  | 330  | 0.3235          | 0.8754   |
| 0.1443        | 31.0  | 341  | 0.4216          | 0.8567   |
| 0.1856        | 32.0  | 352  | 0.3461          | 0.8785   |
| 0.1837        | 33.0  | 363  | 0.3602          | 0.8723   |
| 0.2783        | 34.0  | 374  | 0.3804          | 0.8660   |
| 0.1553        | 35.0  | 385  | 0.3125          | 0.8879   |
| 0.1413        | 36.0  | 396  | 0.3002          | 0.8972   |
| 0.1582        | 37.0  | 407  | 0.3564          | 0.8723   |
| 0.1573        | 38.0  | 418  | 0.4468          | 0.8380   |
| 0.188         | 39.0  | 429  | 0.4019          | 0.8505   |
| 0.1562        | 40.0  | 440  | 0.2482          | 0.9221   |
| 0.1295        | 41.0  | 451  | 0.4421          | 0.8349   |
| 0.1472        | 42.0  | 462  | 0.3083          | 0.8972   |
| 0.12          | 43.0  | 473  | 0.2961          | 0.9003   |
| 0.1056        | 44.0  | 484  | 0.3540          | 0.8692   |
| 0.1121        | 45.0  | 495  | 0.3734          | 0.8692   |
| 0.1055        | 46.0  | 506  | 0.3385          | 0.8785   |
| 0.2452        | 47.0  | 517  | 0.3638          | 0.8629   |
| 0.1398        | 48.0  | 528  | 0.3100          | 0.8941   |
| 0.1255        | 49.0  | 539  | 0.2797          | 0.9034   |
| 0.0972        | 50.0  | 550  | 0.2636          | 0.9034   |
| 0.1057        | 51.0  | 561  | 0.2505          | 0.9003   |
| 0.0929        | 52.0  | 572  | 0.3668          | 0.8816   |
| 0.0991        | 53.0  | 583  | 0.2946          | 0.8972   |
| 0.0994        | 54.0  | 594  | 0.2765          | 0.9065   |
| 0.0949        | 55.0  | 605  | 0.2876          | 0.9097   |
| 0.2796        | 56.0  | 616  | 0.2407          | 0.9221   |
| 0.071         | 57.0  | 627  | 0.3321          | 0.8941   |
| 0.1163        | 58.0  | 638  | 0.2527          | 0.9315   |
| 0.0966        | 59.0  | 649  | 0.2549          | 0.9252   |
| 0.0871        | 60.0  | 660  | 0.3171          | 0.8879   |
| 0.216         | 61.0  | 671  | 0.2085          | 0.9283   |
| 0.0556        | 62.0  | 682  | 0.2115          | 0.9190   |
| 0.0842        | 63.0  | 693  | 0.2602          | 0.9097   |
| 0.0824        | 64.0  | 704  | 0.3565          | 0.8723   |
| 0.0765        | 65.0  | 715  | 0.2983          | 0.9003   |
| 0.3268        | 66.0  | 726  | 0.2924          | 0.8972   |
| 0.0881        | 67.0  | 737  | 0.2990          | 0.8941   |
| 0.0656        | 68.0  | 748  | 0.2518          | 0.9128   |
| 0.0707        | 69.0  | 759  | 0.2702          | 0.9003   |
| 0.0609        | 70.0  | 770  | 0.2493          | 0.9190   |
| 0.0882        | 71.0  | 781  | 0.2210          | 0.9252   |
| 0.0706        | 72.0  | 792  | 0.2242          | 0.9252   |
| 0.0569        | 73.0  | 803  | 0.2450          | 0.9097   |
| 0.0476        | 74.0  | 814  | 0.1686          | 0.9408   |
| 0.0587        | 75.0  | 825  | 0.2537          | 0.9159   |
| 0.056         | 76.0  | 836  | 0.2437          | 0.9190   |
| 0.0613        | 77.0  | 847  | 0.2664          | 0.9128   |
| 0.0554        | 78.0  | 858  | 0.2851          | 0.9003   |
| 0.0522        | 79.0  | 869  | 0.2326          | 0.9221   |
| 0.0564        | 80.0  | 880  | 0.2392          | 0.9283   |
| 0.052         | 81.0  | 891  | 0.2298          | 0.9252   |
| 0.0489        | 82.0  | 902  | 0.2626          | 0.9190   |
| 0.0545        | 83.0  | 913  | 0.2442          | 0.9159   |
| 0.054         | 84.0  | 924  | 0.1613          | 0.9439   |
| 0.0481        | 85.0  | 935  | 0.2730          | 0.9190   |
| 0.0541        | 86.0  | 946  | 0.2194          | 0.9315   |
| 0.0489        | 87.0  | 957  | 0.1749          | 0.9470   |
| 0.0515        | 88.0  | 968  | 0.1577          | 0.9502   |
| 0.05          | 89.0  | 979  | 0.2191          | 0.9252   |
| 0.0484        | 90.0  | 990  | 0.2574          | 0.9252   |
| 0.0503        | 91.0  | 1001 | 0.1792          | 0.9408   |
| 0.0434        | 92.0  | 1012 | 0.2147          | 0.9377   |
| 0.0449        | 93.0  | 1023 | 0.2430          | 0.9159   |
| 0.0464        | 94.0  | 1034 | 0.2486          | 0.9159   |
| 0.0469        | 95.0  | 1045 | 0.1922          | 0.9408   |
| 0.0449        | 96.0  | 1056 | 0.2005          | 0.9283   |
| 0.0456        | 97.0  | 1067 | 0.2175          | 0.9346   |
| 0.0425        | 98.0  | 1078 | 0.1975          | 0.9346   |
| 0.0419        | 99.0  | 1089 | 0.2070          | 0.9283   |
| 0.0363        | 100.0 | 1100 | 0.2175          | 0.9283   |


### Framework versions

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

fresh1 fresh2 fresh3