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# meat_calssify_fresh_crop_fixed_epoch100_V_0_4

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: 0.5888
- Accuracy: 0.8418

## 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: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0919        | 1.0   | 10   | 1.0799          | 0.3481   |
| 1.0793        | 2.0   | 20   | 1.0692          | 0.4051   |
| 1.0529        | 3.0   | 30   | 1.0517          | 0.4620   |
| 1.0236        | 4.0   | 40   | 1.0159          | 0.5      |
| 0.9714        | 5.0   | 50   | 0.9742          | 0.5506   |
| 0.9373        | 6.0   | 60   | 0.9423          | 0.5949   |
| 0.8579        | 7.0   | 70   | 0.9193          | 0.5443   |
| 0.8111        | 8.0   | 80   | 0.8724          | 0.6329   |
| 0.7735        | 9.0   | 90   | 0.8477          | 0.6329   |
| 0.7259        | 10.0  | 100  | 0.8027          | 0.6329   |
| 0.6595        | 11.0  | 110  | 0.7633          | 0.6646   |
| 0.6254        | 12.0  | 120  | 0.7865          | 0.6582   |
| 0.5348        | 13.0  | 130  | 0.7159          | 0.7025   |
| 0.5617        | 14.0  | 140  | 0.8024          | 0.6519   |
| 0.577         | 15.0  | 150  | 0.8290          | 0.6582   |
| 0.48          | 16.0  | 160  | 0.8198          | 0.6139   |
| 0.4578        | 17.0  | 170  | 0.7113          | 0.7215   |
| 0.4349        | 18.0  | 180  | 0.7722          | 0.6835   |
| 0.3965        | 19.0  | 190  | 0.6600          | 0.7152   |
| 0.3775        | 20.0  | 200  | 0.7357          | 0.7089   |
| 0.3396        | 21.0  | 210  | 0.7999          | 0.6392   |
| 0.3559        | 22.0  | 220  | 0.7464          | 0.7342   |
| 0.3271        | 23.0  | 230  | 0.7684          | 0.6709   |
| 0.3522        | 24.0  | 240  | 0.6659          | 0.7025   |
| 0.2631        | 25.0  | 250  | 0.7284          | 0.6899   |
| 0.2456        | 26.0  | 260  | 0.5699          | 0.7848   |
| 0.2557        | 27.0  | 270  | 0.6237          | 0.7722   |
| 0.2807        | 28.0  | 280  | 0.7020          | 0.7152   |
| 0.2434        | 29.0  | 290  | 0.8912          | 0.6519   |
| 0.2591        | 30.0  | 300  | 0.6464          | 0.7658   |
| 0.2519        | 31.0  | 310  | 0.7927          | 0.6899   |
| 0.2253        | 32.0  | 320  | 0.6960          | 0.7468   |
| 0.1842        | 33.0  | 330  | 0.6849          | 0.7278   |
| 0.2762        | 34.0  | 340  | 0.6524          | 0.7532   |
| 0.1925        | 35.0  | 350  | 0.6154          | 0.7532   |
| 0.175         | 36.0  | 360  | 0.7486          | 0.7215   |
| 0.2422        | 37.0  | 370  | 0.7183          | 0.7342   |
| 0.1908        | 38.0  | 380  | 0.5739          | 0.7595   |
| 0.1694        | 39.0  | 390  | 0.7914          | 0.7152   |
| 0.1955        | 40.0  | 400  | 0.7209          | 0.7089   |
| 0.2208        | 41.0  | 410  | 0.9125          | 0.6709   |
| 0.2009        | 42.0  | 420  | 0.6512          | 0.7595   |
| 0.2089        | 43.0  | 430  | 0.7874          | 0.7152   |
| 0.1622        | 44.0  | 440  | 0.6903          | 0.7342   |
| 0.1685        | 45.0  | 450  | 0.6869          | 0.7658   |
| 0.1322        | 46.0  | 460  | 0.7294          | 0.7722   |
| 0.1475        | 47.0  | 470  | 0.6547          | 0.7848   |
| 0.155         | 48.0  | 480  | 0.6317          | 0.8038   |
| 0.1491        | 49.0  | 490  | 0.8127          | 0.7152   |
| 0.1534        | 50.0  | 500  | 0.7171          | 0.7595   |
| 0.1721        | 51.0  | 510  | 0.6840          | 0.7658   |
| 0.1408        | 52.0  | 520  | 0.6608          | 0.7785   |
| 0.1512        | 53.0  | 530  | 0.7649          | 0.7405   |
| 0.1279        | 54.0  | 540  | 0.6965          | 0.7595   |
| 0.1338        | 55.0  | 550  | 0.7497          | 0.7342   |
| 0.1249        | 56.0  | 560  | 0.6094          | 0.8038   |
| 0.1358        | 57.0  | 570  | 0.6515          | 0.7785   |
| 0.1105        | 58.0  | 580  | 0.5354          | 0.8038   |
| 0.1222        | 59.0  | 590  | 0.6427          | 0.7848   |
| 0.1137        | 60.0  | 600  | 0.5940          | 0.7975   |
| 0.0942        | 61.0  | 610  | 0.8780          | 0.7278   |
| 0.1082        | 62.0  | 620  | 0.6781          | 0.7785   |
| 0.1017        | 63.0  | 630  | 0.6201          | 0.7848   |
| 0.0803        | 64.0  | 640  | 0.6029          | 0.7785   |
| 0.111         | 65.0  | 650  | 0.7350          | 0.7532   |
| 0.1146        | 66.0  | 660  | 0.4995          | 0.8228   |
| 0.115         | 67.0  | 670  | 0.6887          | 0.7911   |
| 0.1038        | 68.0  | 680  | 0.4541          | 0.8418   |
| 0.0999        | 69.0  | 690  | 0.6961          | 0.7722   |
| 0.0891        | 70.0  | 700  | 0.4988          | 0.8228   |
| 0.0928        | 71.0  | 710  | 0.5811          | 0.7848   |
| 0.0926        | 72.0  | 720  | 0.5424          | 0.8354   |
| 0.0798        | 73.0  | 730  | 0.6707          | 0.7975   |
| 0.0724        | 74.0  | 740  | 0.5846          | 0.8038   |
| 0.0853        | 75.0  | 750  | 0.5979          | 0.8101   |
| 0.0822        | 76.0  | 760  | 0.6966          | 0.7595   |
| 0.0805        | 77.0  | 770  | 0.6735          | 0.7595   |
| 0.1131        | 78.0  | 780  | 0.7966          | 0.7532   |
| 0.0709        | 79.0  | 790  | 0.6752          | 0.7975   |
| 0.0726        | 80.0  | 800  | 0.5556          | 0.8038   |
| 0.1127        | 81.0  | 810  | 0.5609          | 0.7975   |
| 0.0558        | 82.0  | 820  | 0.6215          | 0.8101   |
| 0.1057        | 83.0  | 830  | 0.6139          | 0.7848   |
| 0.0702        | 84.0  | 840  | 0.6602          | 0.7975   |
| 0.0641        | 85.0  | 850  | 0.5445          | 0.8354   |
| 0.0511        | 86.0  | 860  | 0.6324          | 0.8228   |
| 0.0728        | 87.0  | 870  | 0.6474          | 0.8101   |
| 0.0724        | 88.0  | 880  | 0.6492          | 0.7848   |
| 0.0552        | 89.0  | 890  | 0.5549          | 0.8291   |
| 0.0587        | 90.0  | 900  | 0.5619          | 0.8228   |
| 0.0539        | 91.0  | 910  | 0.5222          | 0.8481   |
| 0.0524        | 92.0  | 920  | 0.6652          | 0.7911   |
| 0.0499        | 93.0  | 930  | 0.7568          | 0.7658   |
| 0.0717        | 94.0  | 940  | 0.6916          | 0.7911   |
| 0.0481        | 95.0  | 950  | 0.5951          | 0.8228   |
| 0.0599        | 96.0  | 960  | 0.5053          | 0.8418   |
| 0.0655        | 97.0  | 970  | 0.6685          | 0.7785   |
| 0.0621        | 98.0  | 980  | 0.5767          | 0.8291   |
| 0.0543        | 99.0  | 990  | 0.5964          | 0.8101   |
| 0.0451        | 100.0 | 1000 | 0.5888          | 0.8418   |


### Framework versions

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

fresh1 fresh2 fresh3