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# meat_calssify_fresh_crop_fixed_epoch100_V_0_6

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: 0.5432
- Accuracy: 0.8165

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1096        | 1.0   | 10   | 1.1024          | 0.3291   |
| 1.0818        | 2.0   | 20   | 1.0876          | 0.4241   |
| 1.0619        | 3.0   | 30   | 1.0912          | 0.3987   |
| 1.0289        | 4.0   | 40   | 1.0680          | 0.4494   |
| 1.0023        | 5.0   | 50   | 1.0357          | 0.4241   |
| 0.9453        | 6.0   | 60   | 1.0095          | 0.4937   |
| 0.8856        | 7.0   | 70   | 0.9563          | 0.5443   |
| 0.8301        | 8.0   | 80   | 0.9307          | 0.5506   |
| 0.7855        | 9.0   | 90   | 0.9109          | 0.5696   |
| 0.7062        | 10.0  | 100  | 0.8383          | 0.5949   |
| 0.6655        | 11.0  | 110  | 0.7534          | 0.7089   |
| 0.617         | 12.0  | 120  | 0.8160          | 0.6646   |
| 0.5821        | 13.0  | 130  | 0.7361          | 0.6962   |
| 0.5097        | 14.0  | 140  | 0.7594          | 0.6899   |
| 0.4944        | 15.0  | 150  | 0.7679          | 0.6709   |
| 0.4787        | 16.0  | 160  | 0.6902          | 0.7595   |
| 0.4234        | 17.0  | 170  | 0.8019          | 0.6835   |
| 0.451         | 18.0  | 180  | 0.6534          | 0.7532   |
| 0.4402        | 19.0  | 190  | 0.6980          | 0.7152   |
| 0.395         | 20.0  | 200  | 0.7276          | 0.6962   |
| 0.3548        | 21.0  | 210  | 0.7002          | 0.6899   |
| 0.3231        | 22.0  | 220  | 0.7080          | 0.7089   |
| 0.2844        | 23.0  | 230  | 0.6934          | 0.7152   |
| 0.2909        | 24.0  | 240  | 0.6465          | 0.7468   |
| 0.3433        | 25.0  | 250  | 0.5959          | 0.7595   |
| 0.2718        | 26.0  | 260  | 0.6787          | 0.7405   |
| 0.2516        | 27.0  | 270  | 0.5951          | 0.8165   |
| 0.2717        | 28.0  | 280  | 0.6355          | 0.7405   |
| 0.2509        | 29.0  | 290  | 0.6980          | 0.6899   |
| 0.2407        | 30.0  | 300  | 0.5824          | 0.7722   |
| 0.2444        | 31.0  | 310  | 0.6241          | 0.7342   |
| 0.2153        | 32.0  | 320  | 0.5982          | 0.7658   |
| 0.2294        | 33.0  | 330  | 0.6701          | 0.7532   |
| 0.1901        | 34.0  | 340  | 0.8116          | 0.6772   |
| 0.252         | 35.0  | 350  | 0.5960          | 0.7848   |
| 0.1761        | 36.0  | 360  | 0.5993          | 0.7785   |
| 0.1892        | 37.0  | 370  | 0.6046          | 0.7785   |
| 0.15          | 38.0  | 380  | 0.6442          | 0.8038   |
| 0.1705        | 39.0  | 390  | 0.7802          | 0.7152   |
| 0.2171        | 40.0  | 400  | 0.7078          | 0.7278   |
| 0.2233        | 41.0  | 410  | 0.5835          | 0.7785   |
| 0.2252        | 42.0  | 420  | 0.7923          | 0.7089   |
| 0.2304        | 43.0  | 430  | 0.6414          | 0.7342   |
| 0.231         | 44.0  | 440  | 0.5405          | 0.7911   |
| 0.1573        | 45.0  | 450  | 0.7065          | 0.7468   |
| 0.1203        | 46.0  | 460  | 0.7642          | 0.7278   |
| 0.1712        | 47.0  | 470  | 0.6204          | 0.7658   |
| 0.1406        | 48.0  | 480  | 0.6591          | 0.7658   |
| 0.1877        | 49.0  | 490  | 0.7968          | 0.7152   |
| 0.1457        | 50.0  | 500  | 0.6790          | 0.7658   |
| 0.1391        | 51.0  | 510  | 0.6116          | 0.7658   |
| 0.1508        | 52.0  | 520  | 0.7331          | 0.7722   |
| 0.1468        | 53.0  | 530  | 0.6900          | 0.7722   |
| 0.13          | 54.0  | 540  | 0.5799          | 0.7975   |
| 0.1078        | 55.0  | 550  | 0.6568          | 0.7785   |
| 0.0945        | 56.0  | 560  | 0.6073          | 0.8101   |
| 0.1358        | 57.0  | 570  | 0.4966          | 0.8291   |
| 0.1605        | 58.0  | 580  | 0.6678          | 0.7722   |
| 0.1673        | 59.0  | 590  | 0.5742          | 0.7975   |
| 0.1112        | 60.0  | 600  | 0.6181          | 0.8038   |
| 0.1144        | 61.0  | 610  | 0.5031          | 0.8354   |
| 0.1154        | 62.0  | 620  | 0.6085          | 0.8038   |
| 0.1141        | 63.0  | 630  | 0.4470          | 0.8734   |
| 0.1201        | 64.0  | 640  | 0.5089          | 0.8291   |
| 0.0871        | 65.0  | 650  | 0.5447          | 0.8481   |
| 0.0939        | 66.0  | 660  | 0.5763          | 0.7911   |
| 0.1098        | 67.0  | 670  | 0.6186          | 0.7911   |
| 0.1062        | 68.0  | 680  | 0.6349          | 0.8038   |
| 0.0755        | 69.0  | 690  | 0.8513          | 0.7278   |
| 0.1257        | 70.0  | 700  | 0.6852          | 0.7911   |
| 0.0651        | 71.0  | 710  | 0.7073          | 0.7722   |
| 0.1024        | 72.0  | 720  | 0.5794          | 0.8354   |
| 0.0887        | 73.0  | 730  | 0.7889          | 0.7278   |
| 0.1014        | 74.0  | 740  | 0.5774          | 0.8228   |
| 0.0986        | 75.0  | 750  | 0.6864          | 0.8038   |
| 0.098         | 76.0  | 760  | 0.4825          | 0.8354   |
| 0.1049        | 77.0  | 770  | 0.7881          | 0.7658   |
| 0.0997        | 78.0  | 780  | 0.5239          | 0.8418   |
| 0.0939        | 79.0  | 790  | 0.6434          | 0.8228   |
| 0.0851        | 80.0  | 800  | 0.5087          | 0.8291   |
| 0.0683        | 81.0  | 810  | 0.6410          | 0.7658   |
| 0.084         | 82.0  | 820  | 0.6120          | 0.8101   |
| 0.0717        | 83.0  | 830  | 0.6231          | 0.8038   |
| 0.0811        | 84.0  | 840  | 0.4338          | 0.8544   |
| 0.066         | 85.0  | 850  | 0.4633          | 0.8544   |
| 0.0746        | 86.0  | 860  | 0.6960          | 0.7595   |
| 0.0864        | 87.0  | 870  | 0.6154          | 0.8101   |
| 0.0432        | 88.0  | 880  | 0.5864          | 0.8228   |
| 0.0644        | 89.0  | 890  | 0.6045          | 0.7911   |
| 0.0644        | 90.0  | 900  | 0.5924          | 0.8101   |
| 0.0976        | 91.0  | 910  | 0.6515          | 0.8165   |
| 0.0593        | 92.0  | 920  | 0.5491          | 0.8101   |
| 0.0884        | 93.0  | 930  | 0.6618          | 0.8101   |
| 0.0752        | 94.0  | 940  | 0.5612          | 0.8165   |
| 0.0423        | 95.0  | 950  | 0.5914          | 0.8101   |
| 0.0685        | 96.0  | 960  | 0.5502          | 0.8291   |
| 0.0425        | 97.0  | 970  | 0.6455          | 0.7975   |
| 0.0639        | 98.0  | 980  | 0.5402          | 0.8291   |
| 0.056         | 99.0  | 990  | 0.5159          | 0.8481   |
| 0.0663        | 100.0 | 1000 | 0.5432          | 0.8165   |


### Framework versions

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

fresh1 fresh2 fresh3