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# meat_calssify_fresh_crop_fixed_epoch100_V_0_1

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: 0.5742
- Accuracy: 0.8228

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0985        | 1.0   | 10   | 1.1041          | 0.2975   |
| 1.0824        | 2.0   | 20   | 1.0871          | 0.3544   |
| 1.0624        | 3.0   | 30   | 1.0830          | 0.3418   |
| 1.0341        | 4.0   | 40   | 1.0578          | 0.3797   |
| 0.9842        | 5.0   | 50   | 1.0021          | 0.4873   |
| 0.9433        | 6.0   | 60   | 1.0362          | 0.4557   |
| 0.8904        | 7.0   | 70   | 0.9132          | 0.5886   |
| 0.8515        | 8.0   | 80   | 0.8612          | 0.6582   |
| 0.8094        | 9.0   | 90   | 0.8405          | 0.6646   |
| 0.732         | 10.0  | 100  | 0.8722          | 0.5886   |
| 0.7408        | 11.0  | 110  | 0.7310          | 0.7215   |
| 0.6286        | 12.0  | 120  | 0.7742          | 0.6456   |
| 0.58          | 13.0  | 130  | 0.8024          | 0.6646   |
| 0.5519        | 14.0  | 140  | 0.7325          | 0.6835   |
| 0.498         | 15.0  | 150  | 0.6873          | 0.7215   |
| 0.4682        | 16.0  | 160  | 0.6757          | 0.7152   |
| 0.4201        | 17.0  | 170  | 0.7193          | 0.7025   |
| 0.4186        | 18.0  | 180  | 0.6616          | 0.7278   |
| 0.3793        | 19.0  | 190  | 0.6906          | 0.7532   |
| 0.322         | 20.0  | 200  | 0.7668          | 0.7089   |
| 0.3441        | 21.0  | 210  | 0.6939          | 0.7468   |
| 0.3146        | 22.0  | 220  | 0.6748          | 0.7342   |
| 0.3451        | 23.0  | 230  | 0.6401          | 0.7658   |
| 0.3001        | 24.0  | 240  | 0.6490          | 0.7089   |
| 0.2884        | 25.0  | 250  | 0.6640          | 0.7405   |
| 0.251         | 26.0  | 260  | 0.6769          | 0.7532   |
| 0.2386        | 27.0  | 270  | 0.6259          | 0.7595   |
| 0.236         | 28.0  | 280  | 0.7949          | 0.6962   |
| 0.2493        | 29.0  | 290  | 0.7442          | 0.7405   |
| 0.2249        | 30.0  | 300  | 0.7024          | 0.7532   |
| 0.2959        | 31.0  | 310  | 0.6887          | 0.7595   |
| 0.2601        | 32.0  | 320  | 0.7209          | 0.7025   |
| 0.2116        | 33.0  | 330  | 0.6414          | 0.7975   |
| 0.1982        | 34.0  | 340  | 0.8802          | 0.6899   |
| 0.2018        | 35.0  | 350  | 0.6697          | 0.7468   |
| 0.2038        | 36.0  | 360  | 0.6632          | 0.7532   |
| 0.2074        | 37.0  | 370  | 0.6776          | 0.7911   |
| 0.1718        | 38.0  | 380  | 0.5189          | 0.7975   |
| 0.1699        | 39.0  | 390  | 0.6332          | 0.7532   |
| 0.1563        | 40.0  | 400  | 0.5616          | 0.8038   |
| 0.1703        | 41.0  | 410  | 0.6547          | 0.7658   |
| 0.212         | 42.0  | 420  | 0.8257          | 0.7215   |
| 0.2272        | 43.0  | 430  | 0.6542          | 0.7468   |
| 0.2264        | 44.0  | 440  | 0.7265          | 0.7278   |
| 0.1506        | 45.0  | 450  | 0.7796          | 0.7468   |
| 0.1459        | 46.0  | 460  | 0.7290          | 0.7468   |
| 0.171         | 47.0  | 470  | 0.6025          | 0.7975   |
| 0.1387        | 48.0  | 480  | 0.7577          | 0.7532   |
| 0.1591        | 49.0  | 490  | 0.7600          | 0.7658   |
| 0.1378        | 50.0  | 500  | 0.7683          | 0.7468   |
| 0.1364        | 51.0  | 510  | 0.7009          | 0.7658   |
| 0.1514        | 52.0  | 520  | 0.6590          | 0.7722   |
| 0.1638        | 53.0  | 530  | 0.6948          | 0.7785   |
| 0.1263        | 54.0  | 540  | 0.6051          | 0.7785   |
| 0.1391        | 55.0  | 550  | 0.7105          | 0.7785   |
| 0.1449        | 56.0  | 560  | 0.6240          | 0.7785   |
| 0.1065        | 57.0  | 570  | 0.6473          | 0.7911   |
| 0.1704        | 58.0  | 580  | 0.7020          | 0.7848   |
| 0.1323        | 59.0  | 590  | 0.7223          | 0.7468   |
| 0.1574        | 60.0  | 600  | 0.7592          | 0.7658   |
| 0.0914        | 61.0  | 610  | 0.6220          | 0.8038   |
| 0.1664        | 62.0  | 620  | 0.7561          | 0.7658   |
| 0.1293        | 63.0  | 630  | 0.6690          | 0.7848   |
| 0.0981        | 64.0  | 640  | 0.7014          | 0.7722   |
| 0.1098        | 65.0  | 650  | 0.6289          | 0.8038   |
| 0.1615        | 66.0  | 660  | 0.7278          | 0.7532   |
| 0.1164        | 67.0  | 670  | 0.7004          | 0.7658   |
| 0.1279        | 68.0  | 680  | 0.7258          | 0.7911   |
| 0.133         | 69.0  | 690  | 0.5725          | 0.8291   |
| 0.0848        | 70.0  | 700  | 0.4775          | 0.8544   |
| 0.1125        | 71.0  | 710  | 0.5514          | 0.8165   |
| 0.0869        | 72.0  | 720  | 0.5685          | 0.7848   |
| 0.0801        | 73.0  | 730  | 0.6424          | 0.7975   |
| 0.0954        | 74.0  | 740  | 0.6587          | 0.7848   |
| 0.078         | 75.0  | 750  | 0.5911          | 0.7975   |
| 0.0913        | 76.0  | 760  | 0.6705          | 0.7911   |
| 0.0712        | 77.0  | 770  | 0.7348          | 0.8038   |
| 0.0781        | 78.0  | 780  | 0.7332          | 0.7658   |
| 0.0898        | 79.0  | 790  | 0.6067          | 0.8101   |
| 0.0715        | 80.0  | 800  | 0.5991          | 0.8038   |
| 0.0713        | 81.0  | 810  | 0.6865          | 0.7975   |
| 0.0899        | 82.0  | 820  | 0.6586          | 0.7975   |
| 0.0739        | 83.0  | 830  | 0.7000          | 0.7975   |
| 0.0861        | 84.0  | 840  | 0.6194          | 0.8101   |
| 0.0752        | 85.0  | 850  | 0.6037          | 0.7911   |
| 0.0665        | 86.0  | 860  | 0.5696          | 0.8228   |
| 0.0693        | 87.0  | 870  | 0.5897          | 0.8291   |
| 0.0736        | 88.0  | 880  | 0.6536          | 0.8228   |
| 0.0618        | 89.0  | 890  | 0.6509          | 0.8038   |
| 0.0835        | 90.0  | 900  | 0.6343          | 0.8038   |
| 0.0826        | 91.0  | 910  | 0.7298          | 0.7722   |
| 0.0736        | 92.0  | 920  | 0.5925          | 0.8165   |
| 0.0656        | 93.0  | 930  | 0.6565          | 0.8228   |
| 0.0605        | 94.0  | 940  | 0.5389          | 0.8481   |
| 0.0451        | 95.0  | 950  | 0.4811          | 0.8481   |
| 0.0807        | 96.0  | 960  | 0.6439          | 0.7848   |
| 0.0579        | 97.0  | 970  | 0.7767          | 0.7468   |
| 0.0639        | 98.0  | 980  | 0.5558          | 0.8038   |
| 0.051         | 99.0  | 990  | 0.6174          | 0.8038   |
| 0.044         | 100.0 | 1000 | 0.5742          | 0.8228   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1

fresh1 fresh2 fresh3