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# meat_calssify_fresh_crop_fixed_epoch100_V_0_7

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: 0.6404
- Accuracy: 0.8101

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.098         | 1.0   | 10   | 1.0916          | 0.4114   |
| 1.0825        | 2.0   | 20   | 1.0892          | 0.3861   |
| 1.0588        | 3.0   | 30   | 1.0802          | 0.3608   |
| 1.0298        | 4.0   | 40   | 1.0551          | 0.4430   |
| 0.981         | 5.0   | 50   | 1.0183          | 0.4620   |
| 0.9274        | 6.0   | 60   | 0.9723          | 0.5063   |
| 0.8655        | 7.0   | 70   | 0.9539          | 0.5443   |
| 0.8275        | 8.0   | 80   | 0.8944          | 0.5696   |
| 0.815         | 9.0   | 90   | 0.8859          | 0.6013   |
| 0.7543        | 10.0  | 100  | 0.9931          | 0.5253   |
| 0.7501        | 11.0  | 110  | 0.9048          | 0.5316   |
| 0.7036        | 12.0  | 120  | 0.8500          | 0.6329   |
| 0.6742        | 13.0  | 130  | 0.8228          | 0.6203   |
| 0.6331        | 14.0  | 140  | 0.8357          | 0.6076   |
| 0.5479        | 15.0  | 150  | 0.7833          | 0.6203   |
| 0.5342        | 16.0  | 160  | 0.9080          | 0.5633   |
| 0.5066        | 17.0  | 170  | 0.9448          | 0.5823   |
| 0.5077        | 18.0  | 180  | 0.7903          | 0.6772   |
| 0.3941        | 19.0  | 190  | 0.7109          | 0.7089   |
| 0.3837        | 20.0  | 200  | 0.7422          | 0.6899   |
| 0.3622        | 21.0  | 210  | 0.6693          | 0.7152   |
| 0.3725        | 22.0  | 220  | 0.7556          | 0.6709   |
| 0.3945        | 23.0  | 230  | 0.7561          | 0.6835   |
| 0.3325        | 24.0  | 240  | 0.8287          | 0.6203   |
| 0.3456        | 25.0  | 250  | 1.2373          | 0.5759   |
| 0.3829        | 26.0  | 260  | 0.8878          | 0.6013   |
| 0.3148        | 27.0  | 270  | 0.8503          | 0.7025   |
| 0.3543        | 28.0  | 280  | 0.6707          | 0.7152   |
| 0.2581        | 29.0  | 290  | 0.6273          | 0.7595   |
| 0.2237        | 30.0  | 300  | 0.5921          | 0.7722   |
| 0.1946        | 31.0  | 310  | 0.5894          | 0.7911   |
| 0.2369        | 32.0  | 320  | 0.7187          | 0.7278   |
| 0.2564        | 33.0  | 330  | 0.8258          | 0.7025   |
| 0.1967        | 34.0  | 340  | 0.5263          | 0.7911   |
| 0.1974        | 35.0  | 350  | 0.7137          | 0.7152   |
| 0.1656        | 36.0  | 360  | 0.6219          | 0.7722   |
| 0.1874        | 37.0  | 370  | 0.7103          | 0.7342   |
| 0.22          | 38.0  | 380  | 0.6303          | 0.7785   |
| 0.1705        | 39.0  | 390  | 0.6412          | 0.7658   |
| 0.1848        | 40.0  | 400  | 0.6148          | 0.7785   |
| 0.1567        | 41.0  | 410  | 0.5199          | 0.8101   |
| 0.113         | 42.0  | 420  | 0.7023          | 0.7595   |
| 0.1704        | 43.0  | 430  | 0.6339          | 0.7848   |
| 0.1829        | 44.0  | 440  | 0.5446          | 0.8165   |
| 0.1325        | 45.0  | 450  | 0.6403          | 0.7658   |
| 0.1375        | 46.0  | 460  | 0.6033          | 0.8101   |
| 0.1425        | 47.0  | 470  | 0.5715          | 0.8101   |
| 0.16          | 48.0  | 480  | 0.6529          | 0.7911   |
| 0.1862        | 49.0  | 490  | 0.7063          | 0.7468   |
| 0.1583        | 50.0  | 500  | 0.4872          | 0.7975   |
| 0.1141        | 51.0  | 510  | 0.7283          | 0.7089   |
| 0.1333        | 52.0  | 520  | 0.6197          | 0.8101   |
| 0.1062        | 53.0  | 530  | 0.5728          | 0.8291   |
| 0.1159        | 54.0  | 540  | 0.7551          | 0.7532   |
| 0.1152        | 55.0  | 550  | 0.7598          | 0.7532   |
| 0.1339        | 56.0  | 560  | 0.7102          | 0.7658   |
| 0.1244        | 57.0  | 570  | 0.5283          | 0.8038   |
| 0.1247        | 58.0  | 580  | 0.6756          | 0.7658   |
| 0.1269        | 59.0  | 590  | 0.7887          | 0.7468   |
| 0.1321        | 60.0  | 600  | 0.6724          | 0.7658   |
| 0.1267        | 61.0  | 610  | 0.6647          | 0.7911   |
| 0.1066        | 62.0  | 620  | 0.5684          | 0.8038   |
| 0.1058        | 63.0  | 630  | 0.6389          | 0.7848   |
| 0.0944        | 64.0  | 640  | 0.5810          | 0.7975   |
| 0.0751        | 65.0  | 650  | 0.8577          | 0.7215   |
| 0.1129        | 66.0  | 660  | 0.5848          | 0.8038   |
| 0.1448        | 67.0  | 670  | 0.5494          | 0.7911   |
| 0.0962        | 68.0  | 680  | 0.6846          | 0.7722   |
| 0.0766        | 69.0  | 690  | 0.5374          | 0.8101   |
| 0.0955        | 70.0  | 700  | 0.6121          | 0.7848   |
| 0.0917        | 71.0  | 710  | 0.6612          | 0.7848   |
| 0.0832        | 72.0  | 720  | 0.6200          | 0.7911   |
| 0.0686        | 73.0  | 730  | 0.6439          | 0.8038   |
| 0.082         | 74.0  | 740  | 0.5290          | 0.8291   |
| 0.0853        | 75.0  | 750  | 0.7542          | 0.7595   |
| 0.0789        | 76.0  | 760  | 0.6179          | 0.8165   |
| 0.1031        | 77.0  | 770  | 0.5439          | 0.8354   |
| 0.0724        | 78.0  | 780  | 0.6302          | 0.8165   |
| 0.0695        | 79.0  | 790  | 0.6113          | 0.7975   |
| 0.1089        | 80.0  | 800  | 0.7490          | 0.7532   |
| 0.0714        | 81.0  | 810  | 0.6824          | 0.8038   |
| 0.09          | 82.0  | 820  | 0.5732          | 0.8165   |
| 0.0962        | 83.0  | 830  | 0.6818          | 0.7785   |
| 0.0614        | 84.0  | 840  | 0.5182          | 0.8165   |
| 0.0685        | 85.0  | 850  | 0.6753          | 0.7532   |
| 0.0861        | 86.0  | 860  | 0.5541          | 0.8228   |
| 0.09          | 87.0  | 870  | 0.7829          | 0.7658   |
| 0.0565        | 88.0  | 880  | 0.7735          | 0.7595   |
| 0.0574        | 89.0  | 890  | 0.6467          | 0.8038   |
| 0.0431        | 90.0  | 900  | 0.6314          | 0.8038   |
| 0.091         | 91.0  | 910  | 0.6226          | 0.8038   |
| 0.055         | 92.0  | 920  | 0.7533          | 0.7785   |
| 0.0776        | 93.0  | 930  | 0.6564          | 0.7975   |
| 0.056         | 94.0  | 940  | 0.6182          | 0.8038   |
| 0.0683        | 95.0  | 950  | 0.5490          | 0.8228   |
| 0.0695        | 96.0  | 960  | 0.6460          | 0.7911   |
| 0.0464        | 97.0  | 970  | 0.6381          | 0.7975   |
| 0.0483        | 98.0  | 980  | 0.5261          | 0.8608   |
| 0.0487        | 99.0  | 990  | 0.5322          | 0.8291   |
| 0.0537        | 100.0 | 1000 | 0.6404          | 0.8101   |


### Framework versions

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

fresh1 fresh2 fresh3