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# meat_calssify_fresh_crop_fixed_epoch100_V_0_5

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: 0.5296
- Accuracy: 0.8291

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0932        | 1.0   | 10   | 1.0897          | 0.4241   |
| 1.0783        | 2.0   | 20   | 1.0809          | 0.4494   |
| 1.0601        | 3.0   | 30   | 1.0457          | 0.5253   |
| 1.0246        | 4.0   | 40   | 1.0220          | 0.5316   |
| 0.9789        | 5.0   | 50   | 0.9556          | 0.5759   |
| 0.9271        | 6.0   | 60   | 0.8963          | 0.6076   |
| 0.8912        | 7.0   | 70   | 0.8881          | 0.6266   |
| 0.8307        | 8.0   | 80   | 0.7793          | 0.6646   |
| 0.7731        | 9.0   | 90   | 0.8021          | 0.6392   |
| 0.7522        | 10.0  | 100  | 0.7848          | 0.6582   |
| 0.6969        | 11.0  | 110  | 0.7099          | 0.6899   |
| 0.6233        | 12.0  | 120  | 0.6720          | 0.7532   |
| 0.5671        | 13.0  | 130  | 0.6675          | 0.7215   |
| 0.5255        | 14.0  | 140  | 0.6834          | 0.7152   |
| 0.5494        | 15.0  | 150  | 0.6656          | 0.7405   |
| 0.5031        | 16.0  | 160  | 0.5975          | 0.7532   |
| 0.4578        | 17.0  | 170  | 0.6668          | 0.7342   |
| 0.4514        | 18.0  | 180  | 0.6415          | 0.7658   |
| 0.3764        | 19.0  | 190  | 0.6383          | 0.7152   |
| 0.373         | 20.0  | 200  | 0.5907          | 0.7658   |
| 0.4071        | 21.0  | 210  | 0.5909          | 0.7532   |
| 0.3669        | 22.0  | 220  | 0.6417          | 0.7595   |
| 0.3172        | 23.0  | 230  | 0.6713          | 0.7342   |
| 0.3585        | 24.0  | 240  | 0.6214          | 0.7532   |
| 0.3713        | 25.0  | 250  | 0.7105          | 0.7152   |
| 0.3773        | 26.0  | 260  | 0.5745          | 0.7785   |
| 0.3167        | 27.0  | 270  | 0.5214          | 0.8038   |
| 0.3192        | 28.0  | 280  | 0.6045          | 0.7532   |
| 0.2735        | 29.0  | 290  | 0.5424          | 0.7975   |
| 0.2327        | 30.0  | 300  | 0.5455          | 0.7911   |
| 0.2561        | 31.0  | 310  | 0.5763          | 0.7532   |
| 0.233         | 32.0  | 320  | 0.5876          | 0.7595   |
| 0.2188        | 33.0  | 330  | 0.4700          | 0.8101   |
| 0.2528        | 34.0  | 340  | 0.5753          | 0.7848   |
| 0.2115        | 35.0  | 350  | 0.4716          | 0.8165   |
| 0.2103        | 36.0  | 360  | 0.5390          | 0.7975   |
| 0.193         | 37.0  | 370  | 0.5002          | 0.8038   |
| 0.1899        | 38.0  | 380  | 0.6283          | 0.7722   |
| 0.2473        | 39.0  | 390  | 0.5941          | 0.7911   |
| 0.177         | 40.0  | 400  | 0.4720          | 0.8544   |
| 0.1926        | 41.0  | 410  | 0.5397          | 0.8038   |
| 0.1558        | 42.0  | 420  | 0.5941          | 0.7722   |
| 0.1821        | 43.0  | 430  | 0.4703          | 0.8038   |
| 0.1507        | 44.0  | 440  | 0.5470          | 0.8228   |
| 0.1871        | 45.0  | 450  | 0.4939          | 0.8038   |
| 0.2069        | 46.0  | 460  | 0.4735          | 0.8228   |
| 0.1558        | 47.0  | 470  | 0.4094          | 0.8418   |
| 0.1604        | 48.0  | 480  | 0.5314          | 0.8038   |
| 0.168         | 49.0  | 490  | 0.5669          | 0.7975   |
| 0.1274        | 50.0  | 500  | 0.5027          | 0.8291   |
| 0.157         | 51.0  | 510  | 0.5210          | 0.8165   |
| 0.1574        | 52.0  | 520  | 0.5325          | 0.8038   |
| 0.113         | 53.0  | 530  | 0.5049          | 0.8165   |
| 0.1184        | 54.0  | 540  | 0.5178          | 0.8228   |
| 0.0908        | 55.0  | 550  | 0.6050          | 0.8038   |
| 0.1298        | 56.0  | 560  | 0.5167          | 0.8291   |
| 0.129         | 57.0  | 570  | 0.6349          | 0.7848   |
| 0.1896        | 58.0  | 580  | 0.5775          | 0.8228   |
| 0.1204        | 59.0  | 590  | 0.5537          | 0.8101   |
| 0.1285        | 60.0  | 600  | 0.6127          | 0.7722   |
| 0.1187        | 61.0  | 610  | 0.5656          | 0.8038   |
| 0.1234        | 62.0  | 620  | 0.5230          | 0.8101   |
| 0.1172        | 63.0  | 630  | 0.5435          | 0.8165   |
| 0.0906        | 64.0  | 640  | 0.4562          | 0.8734   |
| 0.0917        | 65.0  | 650  | 0.4852          | 0.8101   |
| 0.1097        | 66.0  | 660  | 0.5314          | 0.8228   |
| 0.134         | 67.0  | 670  | 0.5456          | 0.8228   |
| 0.0823        | 68.0  | 680  | 0.4863          | 0.8354   |
| 0.0997        | 69.0  | 690  | 0.5733          | 0.8228   |
| 0.1118        | 70.0  | 700  | 0.5084          | 0.8291   |
| 0.1505        | 71.0  | 710  | 0.4201          | 0.8734   |
| 0.1071        | 72.0  | 720  | 0.5167          | 0.8165   |
| 0.1006        | 73.0  | 730  | 0.4861          | 0.8101   |
| 0.0904        | 74.0  | 740  | 0.4193          | 0.8608   |
| 0.0825        | 75.0  | 750  | 0.5001          | 0.8418   |
| 0.086         | 76.0  | 760  | 0.3372          | 0.8797   |
| 0.0727        | 77.0  | 770  | 0.4712          | 0.8544   |
| 0.0779        | 78.0  | 780  | 0.5063          | 0.8418   |
| 0.0858        | 79.0  | 790  | 0.5910          | 0.8354   |
| 0.104         | 80.0  | 800  | 0.4938          | 0.8544   |
| 0.085         | 81.0  | 810  | 0.6679          | 0.7785   |
| 0.0782        | 82.0  | 820  | 0.5140          | 0.8481   |
| 0.0729        | 83.0  | 830  | 0.4257          | 0.8354   |
| 0.0712        | 84.0  | 840  | 0.5314          | 0.8101   |
| 0.0663        | 85.0  | 850  | 0.5001          | 0.8291   |
| 0.0683        | 86.0  | 860  | 0.3963          | 0.8924   |
| 0.0854        | 87.0  | 870  | 0.5039          | 0.8481   |
| 0.077         | 88.0  | 880  | 0.4131          | 0.8608   |
| 0.0683        | 89.0  | 890  | 0.4037          | 0.8671   |
| 0.0598        | 90.0  | 900  | 0.3973          | 0.8797   |
| 0.0692        | 91.0  | 910  | 0.4297          | 0.8734   |
| 0.0808        | 92.0  | 920  | 0.5091          | 0.8481   |
| 0.0529        | 93.0  | 930  | 0.4877          | 0.8418   |
| 0.0597        | 94.0  | 940  | 0.5055          | 0.8418   |
| 0.0551        | 95.0  | 950  | 0.4669          | 0.8481   |
| 0.0548        | 96.0  | 960  | 0.4825          | 0.8608   |
| 0.0922        | 97.0  | 970  | 0.4931          | 0.8481   |
| 0.0665        | 98.0  | 980  | 0.4109          | 0.8734   |
| 0.0471        | 99.0  | 990  | 0.4905          | 0.8544   |
| 0.0479        | 100.0 | 1000 | 0.5296          | 0.8291   |


### Framework versions

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

fresh1 fresh2 fresh3