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# meat_calssify_fresh_crop_fixed_epoch100_V_0_3

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: 0.8015
- Accuracy: 0.7595

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1038        | 1.0   | 10   | 1.1003          | 0.3165   |
| 1.0874        | 2.0   | 20   | 1.0824          | 0.4367   |
| 1.0699        | 3.0   | 30   | 1.0695          | 0.4051   |
| 1.0363        | 4.0   | 40   | 1.0423          | 0.4620   |
| 0.9912        | 5.0   | 50   | 0.9911          | 0.5253   |
| 0.9378        | 6.0   | 60   | 0.9630          | 0.5380   |
| 0.8829        | 7.0   | 70   | 0.9239          | 0.5696   |
| 0.8168        | 8.0   | 80   | 0.9127          | 0.5759   |
| 0.758         | 9.0   | 90   | 0.8583          | 0.6392   |
| 0.7032        | 10.0  | 100  | 0.7911          | 0.6772   |
| 0.6348        | 11.0  | 110  | 0.8342          | 0.5949   |
| 0.6082        | 12.0  | 120  | 0.8187          | 0.6203   |
| 0.5936        | 13.0  | 130  | 0.6830          | 0.7468   |
| 0.5726        | 14.0  | 140  | 0.8194          | 0.6962   |
| 0.5774        | 15.0  | 150  | 0.7164          | 0.6709   |
| 0.4617        | 16.0  | 160  | 0.8145          | 0.6456   |
| 0.4399        | 17.0  | 170  | 0.6810          | 0.7215   |
| 0.4065        | 18.0  | 180  | 0.7049          | 0.7089   |
| 0.4012        | 19.0  | 190  | 0.7462          | 0.6962   |
| 0.3553        | 20.0  | 200  | 0.7550          | 0.6835   |
| 0.419         | 21.0  | 210  | 0.9119          | 0.5886   |
| 0.3862        | 22.0  | 220  | 0.7467          | 0.6646   |
| 0.3218        | 23.0  | 230  | 0.7734          | 0.7025   |
| 0.2856        | 24.0  | 240  | 0.7714          | 0.6772   |
| 0.2853        | 25.0  | 250  | 0.7505          | 0.7215   |
| 0.3005        | 26.0  | 260  | 0.8128          | 0.7152   |
| 0.3128        | 27.0  | 270  | 0.8520          | 0.6456   |
| 0.3146        | 28.0  | 280  | 0.8034          | 0.6962   |
| 0.3091        | 29.0  | 290  | 0.8961          | 0.6835   |
| 0.3128        | 30.0  | 300  | 0.8976          | 0.6772   |
| 0.2696        | 31.0  | 310  | 0.7489          | 0.7089   |
| 0.286         | 32.0  | 320  | 0.6697          | 0.7532   |
| 0.3084        | 33.0  | 330  | 0.8061          | 0.7089   |
| 0.2527        | 34.0  | 340  | 0.6714          | 0.7658   |
| 0.2239        | 35.0  | 350  | 0.6858          | 0.7595   |
| 0.2251        | 36.0  | 360  | 0.7142          | 0.7405   |
| 0.2049        | 37.0  | 370  | 0.6644          | 0.7785   |
| 0.2141        | 38.0  | 380  | 0.6722          | 0.7532   |
| 0.2265        | 39.0  | 390  | 0.8172          | 0.7089   |
| 0.1847        | 40.0  | 400  | 0.5876          | 0.7848   |
| 0.1692        | 41.0  | 410  | 0.7485          | 0.7468   |
| 0.1759        | 42.0  | 420  | 0.6973          | 0.7468   |
| 0.2126        | 43.0  | 430  | 0.7180          | 0.7468   |
| 0.2217        | 44.0  | 440  | 0.8617          | 0.6772   |
| 0.1662        | 45.0  | 450  | 0.7264          | 0.7468   |
| 0.1168        | 46.0  | 460  | 0.6226          | 0.7848   |
| 0.1737        | 47.0  | 470  | 0.7201          | 0.7658   |
| 0.1673        | 48.0  | 480  | 0.7411          | 0.7658   |
| 0.1992        | 49.0  | 490  | 0.6667          | 0.7722   |
| 0.1327        | 50.0  | 500  | 0.8436          | 0.6962   |
| 0.1409        | 51.0  | 510  | 0.8467          | 0.6899   |
| 0.1325        | 52.0  | 520  | 0.8331          | 0.7278   |
| 0.1247        | 53.0  | 530  | 0.6017          | 0.7722   |
| 0.1215        | 54.0  | 540  | 0.5934          | 0.8038   |
| 0.1556        | 55.0  | 550  | 0.8121          | 0.7215   |
| 0.1615        | 56.0  | 560  | 0.5814          | 0.7911   |
| 0.1268        | 57.0  | 570  | 0.6809          | 0.7468   |
| 0.1258        | 58.0  | 580  | 0.5749          | 0.7975   |
| 0.1128        | 59.0  | 590  | 0.6332          | 0.8101   |
| 0.1519        | 60.0  | 600  | 0.7176          | 0.7785   |
| 0.1303        | 61.0  | 610  | 0.6800          | 0.7405   |
| 0.1256        | 62.0  | 620  | 0.7101          | 0.7468   |
| 0.1139        | 63.0  | 630  | 0.7587          | 0.7532   |
| 0.0914        | 64.0  | 640  | 0.6320          | 0.8038   |
| 0.1314        | 65.0  | 650  | 0.7287          | 0.7658   |
| 0.1402        | 66.0  | 660  | 0.9050          | 0.7025   |
| 0.0947        | 67.0  | 670  | 0.5996          | 0.7785   |
| 0.0902        | 68.0  | 680  | 0.6142          | 0.8038   |
| 0.1101        | 69.0  | 690  | 0.8431          | 0.7405   |
| 0.1138        | 70.0  | 700  | 0.6796          | 0.7658   |
| 0.0875        | 71.0  | 710  | 0.8089          | 0.7405   |
| 0.1006        | 72.0  | 720  | 0.6522          | 0.7532   |
| 0.0811        | 73.0  | 730  | 0.7060          | 0.7975   |
| 0.0919        | 74.0  | 740  | 0.7761          | 0.7658   |
| 0.0717        | 75.0  | 750  | 0.8626          | 0.7089   |
| 0.0961        | 76.0  | 760  | 0.6235          | 0.7975   |
| 0.0869        | 77.0  | 770  | 0.6554          | 0.7975   |
| 0.096         | 78.0  | 780  | 0.6839          | 0.7658   |
| 0.0926        | 79.0  | 790  | 0.6482          | 0.8038   |
| 0.0702        | 80.0  | 800  | 0.8970          | 0.7405   |
| 0.0802        | 81.0  | 810  | 0.7076          | 0.7785   |
| 0.0631        | 82.0  | 820  | 0.5784          | 0.8228   |
| 0.0874        | 83.0  | 830  | 0.6042          | 0.8038   |
| 0.092         | 84.0  | 840  | 0.6569          | 0.8038   |
| 0.0827        | 85.0  | 850  | 0.7801          | 0.7848   |
| 0.0796        | 86.0  | 860  | 0.7321          | 0.7975   |
| 0.0731        | 87.0  | 870  | 0.6231          | 0.7911   |
| 0.0731        | 88.0  | 880  | 0.6244          | 0.8038   |
| 0.0694        | 89.0  | 890  | 0.6433          | 0.7975   |
| 0.0601        | 90.0  | 900  | 0.7026          | 0.7785   |
| 0.0715        | 91.0  | 910  | 0.5609          | 0.8165   |
| 0.0782        | 92.0  | 920  | 0.5387          | 0.8481   |
| 0.0685        | 93.0  | 930  | 0.5740          | 0.8165   |
| 0.0508        | 94.0  | 940  | 0.6352          | 0.8291   |
| 0.0871        | 95.0  | 950  | 0.6687          | 0.8038   |
| 0.0533        | 96.0  | 960  | 0.5791          | 0.8165   |
| 0.0525        | 97.0  | 970  | 0.8043          | 0.7532   |
| 0.0884        | 98.0  | 980  | 0.7164          | 0.7911   |
| 0.0619        | 99.0  | 990  | 0.7417          | 0.7785   |
| 0.0703        | 100.0 | 1000 | 0.8015          | 0.7595   |


### Framework versions

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

fresh1 fresh2 fresh3