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# meat_calssify_fresh_crop_fixed_epoch100_V_0_8

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: 0.5407
- Accuracy: 0.8481

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0983        | 1.0   | 10   | 1.0923          | 0.3861   |
| 1.0837        | 2.0   | 20   | 1.0603          | 0.4937   |
| 1.0593        | 3.0   | 30   | 1.0236          | 0.5633   |
| 1.0323        | 4.0   | 40   | 0.9905          | 0.5696   |
| 0.991         | 5.0   | 50   | 0.9442          | 0.5759   |
| 0.9535        | 6.0   | 60   | 0.9318          | 0.5506   |
| 0.8994        | 7.0   | 70   | 0.8444          | 0.6139   |
| 0.847         | 8.0   | 80   | 0.8550          | 0.6266   |
| 0.8083        | 9.0   | 90   | 0.8252          | 0.6456   |
| 0.7465        | 10.0  | 100  | 0.7622          | 0.6646   |
| 0.6872        | 11.0  | 110  | 0.7461          | 0.6329   |
| 0.6664        | 12.0  | 120  | 0.7210          | 0.6899   |
| 0.5655        | 13.0  | 130  | 0.6896          | 0.6962   |
| 0.5541        | 14.0  | 140  | 0.6891          | 0.6962   |
| 0.5162        | 15.0  | 150  | 0.6646          | 0.7468   |
| 0.531         | 16.0  | 160  | 0.6972          | 0.6962   |
| 0.4662        | 17.0  | 170  | 0.5651          | 0.7658   |
| 0.4806        | 18.0  | 180  | 0.7426          | 0.6709   |
| 0.405         | 19.0  | 190  | 0.6764          | 0.7089   |
| 0.3897        | 20.0  | 200  | 0.5832          | 0.7658   |
| 0.327         | 21.0  | 210  | 0.5591          | 0.7532   |
| 0.3542        | 22.0  | 220  | 0.6793          | 0.7278   |
| 0.3309        | 23.0  | 230  | 0.5890          | 0.7532   |
| 0.3384        | 24.0  | 240  | 0.5595          | 0.7722   |
| 0.2867        | 25.0  | 250  | 0.5669          | 0.7975   |
| 0.2651        | 26.0  | 260  | 0.6369          | 0.7468   |
| 0.2659        | 27.0  | 270  | 0.7925          | 0.6709   |
| 0.3057        | 28.0  | 280  | 0.7006          | 0.7405   |
| 0.2606        | 29.0  | 290  | 0.5800          | 0.7658   |
| 0.2145        | 30.0  | 300  | 0.4187          | 0.8418   |
| 0.1951        | 31.0  | 310  | 0.7022          | 0.7342   |
| 0.2658        | 32.0  | 320  | 0.6902          | 0.7342   |
| 0.2329        | 33.0  | 330  | 0.5709          | 0.7595   |
| 0.1807        | 34.0  | 340  | 0.5226          | 0.7911   |
| 0.1602        | 35.0  | 350  | 0.5418          | 0.7911   |
| 0.2104        | 36.0  | 360  | 0.6453          | 0.7658   |
| 0.2009        | 37.0  | 370  | 0.4814          | 0.8291   |
| 0.2059        | 38.0  | 380  | 0.6135          | 0.7595   |
| 0.2203        | 39.0  | 390  | 0.5581          | 0.7785   |
| 0.1864        | 40.0  | 400  | 0.5939          | 0.7911   |
| 0.1564        | 41.0  | 410  | 0.6002          | 0.7848   |
| 0.1229        | 42.0  | 420  | 0.6470          | 0.7658   |
| 0.1867        | 43.0  | 430  | 0.6545          | 0.7975   |
| 0.1679        | 44.0  | 440  | 0.6079          | 0.7658   |
| 0.1752        | 45.0  | 450  | 0.6666          | 0.7468   |
| 0.1256        | 46.0  | 460  | 0.6651          | 0.7595   |
| 0.188         | 47.0  | 470  | 0.6574          | 0.7532   |
| 0.1695        | 48.0  | 480  | 0.5883          | 0.7975   |
| 0.1797        | 49.0  | 490  | 0.7344          | 0.7595   |
| 0.1913        | 50.0  | 500  | 0.5662          | 0.8101   |
| 0.1483        | 51.0  | 510  | 0.5385          | 0.8038   |
| 0.1502        | 52.0  | 520  | 0.5101          | 0.8165   |
| 0.1142        | 53.0  | 530  | 0.5263          | 0.8228   |
| 0.0839        | 54.0  | 540  | 0.4852          | 0.8038   |
| 0.1432        | 55.0  | 550  | 0.5651          | 0.8101   |
| 0.1327        | 56.0  | 560  | 0.6218          | 0.7911   |
| 0.0948        | 57.0  | 570  | 0.6101          | 0.7722   |
| 0.1387        | 58.0  | 580  | 0.5350          | 0.8101   |
| 0.0957        | 59.0  | 590  | 0.7503          | 0.7722   |
| 0.1243        | 60.0  | 600  | 0.5468          | 0.7911   |
| 0.1179        | 61.0  | 610  | 0.5851          | 0.8038   |
| 0.128         | 62.0  | 620  | 0.5167          | 0.8291   |
| 0.1018        | 63.0  | 630  | 0.5119          | 0.8481   |
| 0.0987        | 64.0  | 640  | 0.6415          | 0.7911   |
| 0.0901        | 65.0  | 650  | 0.6031          | 0.8038   |
| 0.1457        | 66.0  | 660  | 0.6773          | 0.7848   |
| 0.1247        | 67.0  | 670  | 0.5563          | 0.7975   |
| 0.127         | 68.0  | 680  | 0.7763          | 0.7595   |
| 0.0841        | 69.0  | 690  | 0.4934          | 0.8544   |
| 0.0914        | 70.0  | 700  | 0.6510          | 0.8228   |
| 0.0982        | 71.0  | 710  | 0.5742          | 0.8101   |
| 0.0945        | 72.0  | 720  | 0.4954          | 0.8481   |
| 0.077         | 73.0  | 730  | 0.6194          | 0.8101   |
| 0.0936        | 74.0  | 740  | 0.5301          | 0.8228   |
| 0.0641        | 75.0  | 750  | 0.5673          | 0.8165   |
| 0.0646        | 76.0  | 760  | 0.5055          | 0.8291   |
| 0.0794        | 77.0  | 770  | 0.5444          | 0.8228   |
| 0.0774        | 78.0  | 780  | 0.5511          | 0.8228   |
| 0.0674        | 79.0  | 790  | 0.5688          | 0.8354   |
| 0.0731        | 80.0  | 800  | 0.5594          | 0.8291   |
| 0.0839        | 81.0  | 810  | 0.6970          | 0.7785   |
| 0.0857        | 82.0  | 820  | 0.5651          | 0.7975   |
| 0.0729        | 83.0  | 830  | 0.7003          | 0.7848   |
| 0.074         | 84.0  | 840  | 0.5293          | 0.8165   |
| 0.0505        | 85.0  | 850  | 0.5051          | 0.8544   |
| 0.0669        | 86.0  | 860  | 0.6459          | 0.8101   |
| 0.0614        | 87.0  | 870  | 0.5474          | 0.8291   |
| 0.0659        | 88.0  | 880  | 0.4981          | 0.8291   |
| 0.0702        | 89.0  | 890  | 0.5611          | 0.8291   |
| 0.0635        | 90.0  | 900  | 0.6273          | 0.7975   |
| 0.0698        | 91.0  | 910  | 0.4314          | 0.8734   |
| 0.0671        | 92.0  | 920  | 0.5471          | 0.8291   |
| 0.057         | 93.0  | 930  | 0.4922          | 0.8481   |
| 0.0563        | 94.0  | 940  | 0.5463          | 0.8418   |
| 0.0638        | 95.0  | 950  | 0.5177          | 0.8291   |
| 0.0545        | 96.0  | 960  | 0.6183          | 0.8038   |
| 0.0534        | 97.0  | 970  | 0.5460          | 0.8165   |
| 0.0655        | 98.0  | 980  | 0.4196          | 0.8861   |
| 0.0775        | 99.0  | 990  | 0.5088          | 0.8354   |
| 0.0519        | 100.0 | 1000 | 0.5407          | 0.8481   |


### Framework versions

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

fresh1 fresh2 fresh3