
<!-- 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_13

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: 0.1337
- Accuracy: 0.9595

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0893        | 1.0   | 21   | 1.0721          | 0.4922   |
| 1.057         | 2.0   | 42   | 1.0397          | 0.5234   |
| 1.018         | 3.0   | 63   | 0.9985          | 0.5265   |
| 0.9639        | 4.0   | 84   | 0.9241          | 0.5794   |
| 0.8882        | 5.0   | 105  | 0.8763          | 0.6231   |
| 0.8154        | 6.0   | 126  | 0.8003          | 0.6542   |
| 0.6905        | 7.0   | 147  | 0.8660          | 0.5981   |
| 0.7078        | 8.0   | 168  | 0.7604          | 0.6729   |
| 0.6762        | 9.0   | 189  | 0.7024          | 0.7134   |
| 0.5977        | 10.0  | 210  | 0.7110          | 0.6854   |
| 0.516         | 11.0  | 231  | 0.6112          | 0.7383   |
| 0.4939        | 12.0  | 252  | 0.5301          | 0.7882   |
| 0.4254        | 13.0  | 273  | 0.5863          | 0.7664   |
| 0.4009        | 14.0  | 294  | 0.6802          | 0.7103   |
| 0.4477        | 15.0  | 315  | 0.6327          | 0.7508   |
| 0.3547        | 16.0  | 336  | 0.4456          | 0.8442   |
| 0.3203        | 17.0  | 357  | 0.5052          | 0.7975   |
| 0.3331        | 18.0  | 378  | 0.4561          | 0.8442   |
| 0.3304        | 19.0  | 399  | 0.5010          | 0.8131   |
| 0.3035        | 20.0  | 420  | 0.4363          | 0.8474   |
| 0.2585        | 21.0  | 441  | 0.4671          | 0.8224   |
| 0.2425        | 22.0  | 462  | 0.4404          | 0.8474   |
| 0.2911        | 23.0  | 483  | 0.4463          | 0.8442   |
| 0.2466        | 24.0  | 504  | 0.3739          | 0.8692   |
| 0.2028        | 25.0  | 525  | 0.3317          | 0.8754   |
| 0.1761        | 26.0  | 546  | 0.5032          | 0.8287   |
| 0.2257        | 27.0  | 567  | 0.4841          | 0.8567   |
| 0.2464        | 28.0  | 588  | 0.3266          | 0.8941   |
| 0.1637        | 29.0  | 609  | 0.5122          | 0.8193   |
| 0.2037        | 30.0  | 630  | 0.3683          | 0.8847   |
| 0.1592        | 31.0  | 651  | 0.3185          | 0.8785   |
| 0.1779        | 32.0  | 672  | 0.4130          | 0.8660   |
| 0.1726        | 33.0  | 693  | 0.2861          | 0.9128   |
| 0.1685        | 34.0  | 714  | 0.3174          | 0.8910   |
| 0.1571        | 35.0  | 735  | 0.3252          | 0.8941   |
| 0.1315        | 36.0  | 756  | 0.4721          | 0.8224   |
| 0.2717        | 37.0  | 777  | 0.4957          | 0.8380   |
| 0.1968        | 38.0  | 798  | 0.2139          | 0.9346   |
| 0.1257        | 39.0  | 819  | 0.2550          | 0.9003   |
| 0.1178        | 40.0  | 840  | 0.3248          | 0.8816   |
| 0.1101        | 41.0  | 861  | 0.3600          | 0.8847   |
| 0.117         | 42.0  | 882  | 0.4135          | 0.8567   |
| 0.1339        | 43.0  | 903  | 0.3311          | 0.8847   |
| 0.1098        | 44.0  | 924  | 0.4151          | 0.8660   |
| 0.0872        | 45.0  | 945  | 0.2727          | 0.9097   |
| 0.1106        | 46.0  | 966  | 0.3106          | 0.9065   |
| 0.0955        | 47.0  | 987  | 0.2232          | 0.9315   |
| 0.1308        | 48.0  | 1008 | 0.2594          | 0.9128   |
| 0.0809        | 49.0  | 1029 | 0.2846          | 0.9065   |
| 0.1123        | 50.0  | 1050 | 0.2310          | 0.9221   |
| 0.0971        | 51.0  | 1071 | 0.3536          | 0.8879   |
| 0.1126        | 52.0  | 1092 | 0.3048          | 0.8972   |
| 0.0909        | 53.0  | 1113 | 0.2762          | 0.9097   |
| 0.089         | 54.0  | 1134 | 0.2672          | 0.9065   |
| 0.0881        | 55.0  | 1155 | 0.3479          | 0.8972   |
| 0.0852        | 56.0  | 1176 | 0.3397          | 0.9003   |
| 0.0712        | 57.0  | 1197 | 0.2242          | 0.9252   |
| 0.0844        | 58.0  | 1218 | 0.2430          | 0.9221   |
| 0.0619        | 59.0  | 1239 | 0.3453          | 0.8785   |
| 0.0904        | 60.0  | 1260 | 0.2579          | 0.9190   |
| 0.0704        | 61.0  | 1281 | 0.2337          | 0.9252   |
| 0.0637        | 62.0  | 1302 | 0.2778          | 0.9128   |
| 0.0752        | 63.0  | 1323 | 0.2019          | 0.9315   |
| 0.0759        | 64.0  | 1344 | 0.2226          | 0.9221   |
| 0.048         | 65.0  | 1365 | 0.3095          | 0.9003   |
| 0.0546        | 66.0  | 1386 | 0.3657          | 0.8972   |
| 0.0664        | 67.0  | 1407 | 0.3862          | 0.8972   |
| 0.0584        | 68.0  | 1428 | 0.2183          | 0.9408   |
| 0.0704        | 69.0  | 1449 | 0.2288          | 0.9283   |
| 0.0444        | 70.0  | 1470 | 0.2355          | 0.9252   |
| 0.0475        | 71.0  | 1491 | 0.1171          | 0.9626   |
| 0.0594        | 72.0  | 1512 | 0.2632          | 0.9252   |
| 0.0428        | 73.0  | 1533 | 0.2323          | 0.9346   |
| 0.0501        | 74.0  | 1554 | 0.2586          | 0.9221   |
| 0.0556        | 75.0  | 1575 | 0.2172          | 0.9252   |
| 0.0427        | 76.0  | 1596 | 0.2898          | 0.9097   |
| 0.0572        | 77.0  | 1617 | 0.1617          | 0.9502   |
| 0.038         | 78.0  | 1638 | 0.2294          | 0.9221   |
| 0.0453        | 79.0  | 1659 | 0.1670          | 0.9502   |
| 0.0378        | 80.0  | 1680 | 0.1205          | 0.9595   |
| 0.0444        | 81.0  | 1701 | 0.1833          | 0.9470   |
| 0.065         | 82.0  | 1722 | 0.2581          | 0.9252   |
| 0.0498        | 83.0  | 1743 | 0.2651          | 0.9315   |
| 0.0607        | 84.0  | 1764 | 0.2678          | 0.9221   |
| 0.0554        | 85.0  | 1785 | 0.1547          | 0.9470   |
| 0.0313        | 86.0  | 1806 | 0.1567          | 0.9533   |
| 0.0267        | 87.0  | 1827 | 0.1955          | 0.9346   |
| 0.0377        | 88.0  | 1848 | 0.1900          | 0.9346   |
| 0.0388        | 89.0  | 1869 | 0.1831          | 0.9377   |
| 0.0297        | 90.0  | 1890 | 0.1823          | 0.9470   |
| 0.0424        | 91.0  | 1911 | 0.2606          | 0.9315   |
| 0.0459        | 92.0  | 1932 | 0.1478          | 0.9502   |
| 0.0308        | 93.0  | 1953 | 0.1695          | 0.9439   |
| 0.0415        | 94.0  | 1974 | 0.1325          | 0.9564   |
| 0.0387        | 95.0  | 1995 | 0.0877          | 0.9751   |
| 0.0318        | 96.0  | 2016 | 0.1765          | 0.9408   |
| 0.0317        | 97.0  | 2037 | 0.1650          | 0.9564   |
| 0.0198        | 98.0  | 2058 | 0.2043          | 0.9439   |
| 0.0422        | 99.0  | 2079 | 0.1777          | 0.9377   |
| 0.0335        | 100.0 | 2100 | 0.1337          | 0.9595   |


### Framework versions

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

fresh1 fresh2 fresh3