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# meat_calssify_fresh_crop_fixed_epoch100_V_0_2

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: 0.7219
- Accuracy: 0.7975

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0968        | 1.0   | 10   | 1.0907          | 0.3797   |
| 1.0804        | 2.0   | 20   | 1.0759          | 0.3924   |
| 1.0578        | 3.0   | 30   | 1.0750          | 0.4241   |
| 1.0273        | 4.0   | 40   | 1.0443          | 0.4684   |
| 0.9866        | 5.0   | 50   | 1.0325          | 0.4747   |
| 0.9234        | 6.0   | 60   | 0.9837          | 0.5886   |
| 0.8597        | 7.0   | 70   | 0.9564          | 0.5443   |
| 0.8042        | 8.0   | 80   | 0.9315          | 0.5633   |
| 0.8463        | 9.0   | 90   | 0.9334          | 0.5380   |
| 0.7795        | 10.0  | 100  | 0.9305          | 0.5443   |
| 0.7375        | 11.0  | 110  | 0.8787          | 0.6076   |
| 0.6489        | 12.0  | 120  | 0.8685          | 0.6392   |
| 0.5958        | 13.0  | 130  | 0.8133          | 0.6582   |
| 0.5308        | 14.0  | 140  | 0.8563          | 0.6519   |
| 0.5206        | 15.0  | 150  | 0.7902          | 0.6709   |
| 0.4617        | 16.0  | 160  | 0.8114          | 0.6456   |
| 0.4338        | 17.0  | 170  | 0.8134          | 0.6646   |
| 0.454         | 18.0  | 180  | 0.7283          | 0.6772   |
| 0.5094        | 19.0  | 190  | 0.7035          | 0.6962   |
| 0.4133        | 20.0  | 200  | 0.7652          | 0.6835   |
| 0.3504        | 21.0  | 210  | 0.7225          | 0.7089   |
| 0.3602        | 22.0  | 220  | 0.8140          | 0.6582   |
| 0.32          | 23.0  | 230  | 0.7057          | 0.7278   |
| 0.2849        | 24.0  | 240  | 0.7051          | 0.6899   |
| 0.3051        | 25.0  | 250  | 0.7805          | 0.7025   |
| 0.3099        | 26.0  | 260  | 0.7456          | 0.6772   |
| 0.3305        | 27.0  | 270  | 0.7802          | 0.6646   |
| 0.2508        | 28.0  | 280  | 0.7222          | 0.7152   |
| 0.2842        | 29.0  | 290  | 0.6745          | 0.7278   |
| 0.2584        | 30.0  | 300  | 0.6029          | 0.7658   |
| 0.2324        | 31.0  | 310  | 0.6066          | 0.7911   |
| 0.3014        | 32.0  | 320  | 0.7253          | 0.7215   |
| 0.2279        | 33.0  | 330  | 0.7050          | 0.7089   |
| 0.2363        | 34.0  | 340  | 0.7361          | 0.7785   |
| 0.2085        | 35.0  | 350  | 0.6596          | 0.7658   |
| 0.1808        | 36.0  | 360  | 0.7104          | 0.7532   |
| 0.2051        | 37.0  | 370  | 0.7471          | 0.7152   |
| 0.1911        | 38.0  | 380  | 0.8262          | 0.7025   |
| 0.2027        | 39.0  | 390  | 0.7785          | 0.7532   |
| 0.1944        | 40.0  | 400  | 0.8136          | 0.6835   |
| 0.1627        | 41.0  | 410  | 0.8254          | 0.7152   |
| 0.1619        | 42.0  | 420  | 0.8766          | 0.6772   |
| 0.1619        | 43.0  | 430  | 0.6940          | 0.7405   |
| 0.1635        | 44.0  | 440  | 0.8477          | 0.7215   |
| 0.1323        | 45.0  | 450  | 0.6644          | 0.7848   |
| 0.1253        | 46.0  | 460  | 0.7747          | 0.7468   |
| 0.1254        | 47.0  | 470  | 0.9075          | 0.6962   |
| 0.1494        | 48.0  | 480  | 0.8104          | 0.7405   |
| 0.1702        | 49.0  | 490  | 0.7167          | 0.7532   |
| 0.1591        | 50.0  | 500  | 0.8214          | 0.6962   |
| 0.1105        | 51.0  | 510  | 0.9359          | 0.7152   |
| 0.1354        | 52.0  | 520  | 0.7214          | 0.7342   |
| 0.119         | 53.0  | 530  | 0.7825          | 0.7342   |
| 0.0841        | 54.0  | 540  | 0.7528          | 0.7595   |
| 0.12          | 55.0  | 550  | 0.7002          | 0.7658   |
| 0.1096        | 56.0  | 560  | 0.7747          | 0.7785   |
| 0.1192        | 57.0  | 570  | 0.7368          | 0.7532   |
| 0.1268        | 58.0  | 580  | 0.7098          | 0.7722   |
| 0.1351        | 59.0  | 590  | 0.6097          | 0.7848   |
| 0.1248        | 60.0  | 600  | 0.8102          | 0.7215   |
| 0.1378        | 61.0  | 610  | 0.6786          | 0.7405   |
| 0.1208        | 62.0  | 620  | 0.5467          | 0.8101   |
| 0.0786        | 63.0  | 630  | 0.7059          | 0.7785   |
| 0.1048        | 64.0  | 640  | 0.7945          | 0.7278   |
| 0.0954        | 65.0  | 650  | 0.8258          | 0.7278   |
| 0.121         | 66.0  | 660  | 0.7267          | 0.7532   |
| 0.0921        | 67.0  | 670  | 0.5914          | 0.7911   |
| 0.092         | 68.0  | 680  | 0.6923          | 0.7722   |
| 0.1153        | 69.0  | 690  | 0.6655          | 0.8038   |
| 0.0987        | 70.0  | 700  | 0.6774          | 0.7722   |
| 0.0797        | 71.0  | 710  | 0.6143          | 0.7975   |
| 0.0842        | 72.0  | 720  | 0.7301          | 0.7595   |
| 0.0707        | 73.0  | 730  | 0.7614          | 0.7405   |
| 0.0848        | 74.0  | 740  | 0.7578          | 0.7785   |
| 0.0853        | 75.0  | 750  | 0.7785          | 0.7405   |
| 0.0761        | 76.0  | 760  | 0.8719          | 0.7532   |
| 0.1019        | 77.0  | 770  | 0.5698          | 0.8165   |
| 0.0747        | 78.0  | 780  | 0.7956          | 0.7278   |
| 0.0657        | 79.0  | 790  | 0.5792          | 0.7975   |
| 0.0969        | 80.0  | 800  | 0.5721          | 0.8101   |
| 0.0597        | 81.0  | 810  | 0.7171          | 0.7785   |
| 0.0787        | 82.0  | 820  | 0.7493          | 0.7595   |
| 0.0823        | 83.0  | 830  | 0.6758          | 0.8038   |
| 0.0828        | 84.0  | 840  | 0.8082          | 0.7722   |
| 0.0693        | 85.0  | 850  | 0.7310          | 0.7911   |
| 0.074         | 86.0  | 860  | 0.6492          | 0.8228   |
| 0.0736        | 87.0  | 870  | 0.7373          | 0.7785   |
| 0.0763        | 88.0  | 880  | 0.7254          | 0.7848   |
| 0.0823        | 89.0  | 890  | 0.8261          | 0.7785   |
| 0.0614        | 90.0  | 900  | 0.6919          | 0.7911   |
| 0.0916        | 91.0  | 910  | 0.5884          | 0.7975   |
| 0.0539        | 92.0  | 920  | 0.6960          | 0.7658   |
| 0.0604        | 93.0  | 930  | 0.6502          | 0.7975   |
| 0.0596        | 94.0  | 940  | 0.6058          | 0.7975   |
| 0.0599        | 95.0  | 950  | 0.7166          | 0.7785   |
| 0.0452        | 96.0  | 960  | 0.8093          | 0.7658   |
| 0.0556        | 97.0  | 970  | 0.6589          | 0.8354   |
| 0.0675        | 98.0  | 980  | 0.7471          | 0.8101   |
| 0.0581        | 99.0  | 990  | 0.6568          | 0.8038   |
| 0.0515        | 100.0 | 1000 | 0.7219          | 0.7975   |


### Framework versions

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

fresh1 fresh2 fresh3