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# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_11

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: 0.2049
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.089         | 1.0   | 21   | 1.0699          | 0.4642   |
| 1.0499        | 2.0   | 42   | 1.0288          | 0.5514   |
| 1.012         | 3.0   | 63   | 0.9738          | 0.5234   |
| 0.9484        | 4.0   | 84   | 0.9146          | 0.5763   |
| 0.8696        | 5.0   | 105  | 0.8897          | 0.6075   |
| 0.8194        | 6.0   | 126  | 0.8737          | 0.6168   |
| 0.7567        | 7.0   | 147  | 0.7329          | 0.6916   |
| 0.6649        | 8.0   | 168  | 0.7376          | 0.6978   |
| 0.6515        | 9.0   | 189  | 1.0486          | 0.5202   |
| 0.7191        | 10.0  | 210  | 0.7651          | 0.6636   |
| 0.573         | 11.0  | 231  | 0.7393          | 0.6885   |
| 0.4787        | 12.0  | 252  | 0.7596          | 0.6791   |
| 0.4838        | 13.0  | 273  | 0.6008          | 0.7788   |
| 0.4554        | 14.0  | 294  | 0.6622          | 0.7477   |
| 0.5433        | 15.0  | 315  | 0.6715          | 0.7196   |
| 0.4842        | 16.0  | 336  | 0.5973          | 0.7414   |
| 0.4186        | 17.0  | 357  | 0.5679          | 0.7757   |
| 0.3345        | 18.0  | 378  | 0.4770          | 0.8162   |
| 0.2651        | 19.0  | 399  | 0.4308          | 0.8442   |
| 0.2247        | 20.0  | 420  | 0.4637          | 0.8442   |
| 0.2601        | 21.0  | 441  | 0.3916          | 0.8723   |
| 0.2419        | 22.0  | 462  | 0.3525          | 0.8785   |
| 0.2626        | 23.0  | 483  | 0.4901          | 0.8380   |
| 0.2554        | 24.0  | 504  | 0.6997          | 0.7445   |
| 0.2352        | 25.0  | 525  | 0.2725          | 0.9159   |
| 0.2139        | 26.0  | 546  | 0.5544          | 0.8006   |
| 0.2456        | 27.0  | 567  | 0.3419          | 0.8785   |
| 0.2336        | 28.0  | 588  | 0.3981          | 0.8349   |
| 0.1654        | 29.0  | 609  | 0.3819          | 0.8474   |
| 0.1543        | 30.0  | 630  | 0.2538          | 0.9128   |
| 0.1744        | 31.0  | 651  | 0.4008          | 0.8536   |
| 0.1627        | 32.0  | 672  | 0.3453          | 0.8785   |
| 0.1641        | 33.0  | 693  | 0.2883          | 0.8972   |
| 0.1816        | 34.0  | 714  | 0.3159          | 0.8910   |
| 0.3087        | 35.0  | 735  | 0.5607          | 0.8131   |
| 0.1463        | 36.0  | 756  | 0.2616          | 0.9034   |
| 0.2832        | 37.0  | 777  | 0.3128          | 0.9003   |
| 0.1135        | 38.0  | 798  | 0.2374          | 0.9221   |
| 0.109         | 39.0  | 819  | 0.2972          | 0.9159   |
| 0.103         | 40.0  | 840  | 0.3414          | 0.8879   |
| 0.1084        | 41.0  | 861  | 0.5068          | 0.8318   |
| 0.1464        | 42.0  | 882  | 0.2895          | 0.9034   |
| 0.0994        | 43.0  | 903  | 0.2374          | 0.9221   |
| 0.0908        | 44.0  | 924  | 0.2381          | 0.9283   |
| 0.113         | 45.0  | 945  | 0.2854          | 0.9065   |
| 0.1415        | 46.0  | 966  | 0.2304          | 0.9283   |
| 0.0965        | 47.0  | 987  | 0.2900          | 0.9003   |
| 0.0773        | 48.0  | 1008 | 0.3234          | 0.8972   |
| 0.0749        | 49.0  | 1029 | 0.3964          | 0.8785   |
| 0.1094        | 50.0  | 1050 | 0.4835          | 0.8536   |
| 0.1152        | 51.0  | 1071 | 0.2459          | 0.9159   |
| 0.1123        | 52.0  | 1092 | 0.2469          | 0.9190   |
| 0.0837        | 53.0  | 1113 | 0.2169          | 0.9252   |
| 0.0944        | 54.0  | 1134 | 0.2855          | 0.9003   |
| 0.0975        | 55.0  | 1155 | 0.2581          | 0.9065   |
| 0.0738        | 56.0  | 1176 | 0.2912          | 0.8972   |
| 0.0735        | 57.0  | 1197 | 0.2847          | 0.9003   |
| 0.0773        | 58.0  | 1218 | 0.2194          | 0.9252   |
| 0.0917        | 59.0  | 1239 | 0.2202          | 0.9159   |
| 0.0843        | 60.0  | 1260 | 0.4062          | 0.8629   |
| 0.0796        | 61.0  | 1281 | 0.2564          | 0.9190   |
| 0.0592        | 62.0  | 1302 | 0.2795          | 0.9097   |
| 0.0526        | 63.0  | 1323 | 0.2589          | 0.9252   |
| 0.072         | 64.0  | 1344 | 0.1720          | 0.9470   |
| 0.0721        | 65.0  | 1365 | 0.3482          | 0.8972   |
| 0.0643        | 66.0  | 1386 | 0.2056          | 0.9315   |
| 0.0632        | 67.0  | 1407 | 0.2368          | 0.9377   |
| 0.0656        | 68.0  | 1428 | 0.1891          | 0.9346   |
| 0.0547        | 69.0  | 1449 | 0.2592          | 0.9315   |
| 0.0613        | 70.0  | 1470 | 0.2446          | 0.9221   |
| 0.0572        | 71.0  | 1491 | 0.1700          | 0.9439   |
| 0.0707        | 72.0  | 1512 | 0.1974          | 0.9377   |
| 0.0462        | 73.0  | 1533 | 0.3013          | 0.9221   |
| 0.045         | 74.0  | 1554 | 0.2223          | 0.9252   |
| 0.0729        | 75.0  | 1575 | 0.2085          | 0.9346   |
| 0.049         | 76.0  | 1596 | 0.2198          | 0.9470   |
| 0.0531        | 77.0  | 1617 | 0.2064          | 0.9439   |
| 0.047         | 78.0  | 1638 | 0.3139          | 0.9065   |
| 0.0484        | 79.0  | 1659 | 0.3167          | 0.9190   |
| 0.0572        | 80.0  | 1680 | 0.2002          | 0.9408   |
| 0.0356        | 81.0  | 1701 | 0.2248          | 0.9283   |
| 0.0405        | 82.0  | 1722 | 0.2738          | 0.9283   |
| 0.0502        | 83.0  | 1743 | 0.1940          | 0.9315   |
| 0.0403        | 84.0  | 1764 | 0.2541          | 0.9252   |
| 0.0334        | 85.0  | 1785 | 0.2284          | 0.9439   |
| 0.0395        | 86.0  | 1806 | 0.2369          | 0.9315   |
| 0.0359        | 87.0  | 1827 | 0.1361          | 0.9688   |
| 0.0412        | 88.0  | 1848 | 0.2190          | 0.9408   |
| 0.0399        | 89.0  | 1869 | 0.2068          | 0.9408   |
| 0.047         | 90.0  | 1890 | 0.2655          | 0.9159   |
| 0.0377        | 91.0  | 1911 | 0.1519          | 0.9377   |
| 0.0246        | 92.0  | 1932 | 0.2156          | 0.9377   |
| 0.0285        | 93.0  | 1953 | 0.2732          | 0.9315   |
| 0.0447        | 94.0  | 1974 | 0.2069          | 0.9315   |
| 0.0271        | 95.0  | 1995 | 0.2119          | 0.9377   |
| 0.0316        | 96.0  | 2016 | 0.2199          | 0.9377   |
| 0.0335        | 97.0  | 2037 | 0.1942          | 0.9439   |
| 0.0285        | 98.0  | 2058 | 0.1771          | 0.9439   |
| 0.0262        | 99.0  | 2079 | 0.1745          | 0.9470   |
| 0.0276        | 100.0 | 2100 | 0.2049          | 0.9315   |


### Framework versions

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

fresh1 fresh2 fresh3