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# meat_calssify_fresh_crop_fixed_epoch100_V_0_9

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: 0.5517
- Accuracy: 0.8038

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1101        | 1.0   | 10   | 1.1019          | 0.3038   |
| 1.087         | 2.0   | 20   | 1.0748          | 0.4557   |
| 1.0593        | 3.0   | 30   | 1.0543          | 0.5      |
| 1.0235        | 4.0   | 40   | 1.0289          | 0.4873   |
| 0.9755        | 5.0   | 50   | 1.0048          | 0.4873   |
| 0.9116        | 6.0   | 60   | 0.9857          | 0.5      |
| 0.9154        | 7.0   | 70   | 0.9614          | 0.4937   |
| 0.8318        | 8.0   | 80   | 0.9839          | 0.5506   |
| 0.795         | 9.0   | 90   | 0.9393          | 0.5570   |
| 0.7544        | 10.0  | 100  | 0.9061          | 0.5886   |
| 0.6596        | 11.0  | 110  | 0.8780          | 0.6392   |
| 0.6111        | 12.0  | 120  | 0.8170          | 0.6392   |
| 0.5791        | 13.0  | 130  | 0.8801          | 0.6329   |
| 0.5287        | 14.0  | 140  | 0.8099          | 0.6709   |
| 0.4966        | 15.0  | 150  | 0.7827          | 0.6582   |
| 0.4842        | 16.0  | 160  | 0.8007          | 0.6709   |
| 0.4059        | 17.0  | 170  | 0.7380          | 0.6582   |
| 0.3905        | 18.0  | 180  | 0.6695          | 0.7278   |
| 0.3681        | 19.0  | 190  | 0.7496          | 0.6899   |
| 0.3618        | 20.0  | 200  | 0.7554          | 0.7089   |
| 0.3446        | 21.0  | 210  | 0.7603          | 0.6962   |
| 0.3625        | 22.0  | 220  | 0.7402          | 0.6772   |
| 0.3356        | 23.0  | 230  | 0.7598          | 0.6582   |
| 0.2758        | 24.0  | 240  | 0.7952          | 0.6899   |
| 0.287         | 25.0  | 250  | 0.8296          | 0.6772   |
| 0.3334        | 26.0  | 260  | 0.9352          | 0.6456   |
| 0.2925        | 27.0  | 270  | 0.8240          | 0.6772   |
| 0.2732        | 28.0  | 280  | 0.7479          | 0.7278   |
| 0.2816        | 29.0  | 290  | 0.7068          | 0.7152   |
| 0.2349        | 30.0  | 300  | 0.6218          | 0.7658   |
| 0.2282        | 31.0  | 310  | 0.6681          | 0.7342   |
| 0.2297        | 32.0  | 320  | 0.9084          | 0.6709   |
| 0.2316        | 33.0  | 330  | 0.8716          | 0.6772   |
| 0.2182        | 34.0  | 340  | 0.7289          | 0.7342   |
| 0.2159        | 35.0  | 350  | 0.6567          | 0.7405   |
| 0.2329        | 36.0  | 360  | 0.6947          | 0.7468   |
| 0.155         | 37.0  | 370  | 0.6736          | 0.7532   |
| 0.1901        | 38.0  | 380  | 0.8000          | 0.7025   |
| 0.1767        | 39.0  | 390  | 0.7780          | 0.7342   |
| 0.1718        | 40.0  | 400  | 0.6616          | 0.7595   |
| 0.1558        | 41.0  | 410  | 0.7514          | 0.7025   |
| 0.1564        | 42.0  | 420  | 0.7801          | 0.7278   |
| 0.2172        | 43.0  | 430  | 0.7421          | 0.7342   |
| 0.1703        | 44.0  | 440  | 0.7043          | 0.7595   |
| 0.1475        | 45.0  | 450  | 0.6865          | 0.7658   |
| 0.1174        | 46.0  | 460  | 0.5958          | 0.7975   |
| 0.1586        | 47.0  | 470  | 0.6927          | 0.7785   |
| 0.1515        | 48.0  | 480  | 0.8407          | 0.7089   |
| 0.1593        | 49.0  | 490  | 0.6465          | 0.7658   |
| 0.1777        | 50.0  | 500  | 0.7899          | 0.7215   |
| 0.1205        | 51.0  | 510  | 0.5897          | 0.7722   |
| 0.1375        | 52.0  | 520  | 0.6837          | 0.7785   |
| 0.1564        | 53.0  | 530  | 0.7868          | 0.7152   |
| 0.1481        | 54.0  | 540  | 0.7252          | 0.7722   |
| 0.1073        | 55.0  | 550  | 0.6796          | 0.7658   |
| 0.1549        | 56.0  | 560  | 0.7610          | 0.7152   |
| 0.1351        | 57.0  | 570  | 0.7985          | 0.7342   |
| 0.1235        | 58.0  | 580  | 0.6534          | 0.7595   |
| 0.1306        | 59.0  | 590  | 0.7046          | 0.7975   |
| 0.1464        | 60.0  | 600  | 0.7280          | 0.7595   |
| 0.1724        | 61.0  | 610  | 0.7066          | 0.7848   |
| 0.115         | 62.0  | 620  | 0.7080          | 0.7532   |
| 0.0842        | 63.0  | 630  | 0.6463          | 0.7848   |
| 0.0883        | 64.0  | 640  | 0.8290          | 0.7342   |
| 0.0901        | 65.0  | 650  | 0.7097          | 0.7595   |
| 0.1174        | 66.0  | 660  | 0.6627          | 0.7658   |
| 0.1167        | 67.0  | 670  | 0.7519          | 0.7722   |
| 0.0795        | 68.0  | 680  | 0.6104          | 0.7975   |
| 0.0583        | 69.0  | 690  | 0.7621          | 0.7848   |
| 0.0973        | 70.0  | 700  | 0.7309          | 0.7658   |
| 0.0909        | 71.0  | 710  | 0.9068          | 0.7215   |
| 0.0931        | 72.0  | 720  | 0.7453          | 0.7658   |
| 0.1101        | 73.0  | 730  | 0.8395          | 0.7089   |
| 0.0867        | 74.0  | 740  | 0.6816          | 0.7722   |
| 0.1154        | 75.0  | 750  | 0.7723          | 0.7405   |
| 0.1016        | 76.0  | 760  | 0.7334          | 0.7785   |
| 0.0821        | 77.0  | 770  | 0.7354          | 0.7722   |
| 0.0624        | 78.0  | 780  | 0.5303          | 0.8544   |
| 0.0698        | 79.0  | 790  | 0.7409          | 0.7658   |
| 0.086         | 80.0  | 800  | 0.6524          | 0.8038   |
| 0.072         | 81.0  | 810  | 0.7530          | 0.7848   |
| 0.0656        | 82.0  | 820  | 0.7409          | 0.7785   |
| 0.0909        | 83.0  | 830  | 0.7190          | 0.7848   |
| 0.0821        | 84.0  | 840  | 0.7085          | 0.7848   |
| 0.0618        | 85.0  | 850  | 0.6801          | 0.7658   |
| 0.0943        | 86.0  | 860  | 0.6859          | 0.7595   |
| 0.0787        | 87.0  | 870  | 0.6259          | 0.7975   |
| 0.0691        | 88.0  | 880  | 0.7148          | 0.7911   |
| 0.0494        | 89.0  | 890  | 0.7675          | 0.7785   |
| 0.0767        | 90.0  | 900  | 0.7293          | 0.7911   |
| 0.0861        | 91.0  | 910  | 0.6653          | 0.7975   |
| 0.0535        | 92.0  | 920  | 0.6421          | 0.8038   |
| 0.0574        | 93.0  | 930  | 0.7444          | 0.7911   |
| 0.0567        | 94.0  | 940  | 0.4409          | 0.8671   |
| 0.0759        | 95.0  | 950  | 0.5884          | 0.7975   |
| 0.0407        | 96.0  | 960  | 0.6606          | 0.7848   |
| 0.0624        | 97.0  | 970  | 0.5409          | 0.8354   |
| 0.0586        | 98.0  | 980  | 0.5585          | 0.7975   |
| 0.0413        | 99.0  | 990  | 0.6347          | 0.7911   |
| 0.0597        | 100.0 | 1000 | 0.5517          | 0.8038   |


### Framework versions

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

fresh1 fresh2 fresh3