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# meat_calssify_fresh_crop_fixed_overlap_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: 0.3158
- Accuracy: 0.9051

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0836        | 1.0   | 20   | 1.0836          | 0.3892   |
| 1.0325        | 2.0   | 40   | 1.0308          | 0.5032   |
| 0.9331        | 3.0   | 60   | 0.9478          | 0.5506   |
| 0.8711        | 4.0   | 80   | 0.9827          | 0.5380   |
| 0.8252        | 5.0   | 100  | 0.9171          | 0.5665   |
| 0.7597        | 6.0   | 120  | 0.8175          | 0.6234   |
| 0.6528        | 7.0   | 140  | 0.7884          | 0.6835   |
| 0.5646        | 8.0   | 160  | 0.7034          | 0.7025   |
| 0.5026        | 9.0   | 180  | 0.6805          | 0.7025   |
| 0.4534        | 10.0  | 200  | 0.6223          | 0.7690   |
| 0.4244        | 11.0  | 220  | 0.6262          | 0.7405   |
| 0.4077        | 12.0  | 240  | 0.6230          | 0.7595   |
| 0.3962        | 13.0  | 260  | 0.6731          | 0.7184   |
| 0.3587        | 14.0  | 280  | 0.5633          | 0.7911   |
| 0.316         | 15.0  | 300  | 0.5808          | 0.7848   |
| 0.2472        | 16.0  | 320  | 0.5478          | 0.7943   |
| 0.277         | 17.0  | 340  | 0.5609          | 0.8038   |
| 0.2586        | 18.0  | 360  | 0.5427          | 0.8133   |
| 0.2405        | 19.0  | 380  | 0.5207          | 0.8165   |
| 0.2141        | 20.0  | 400  | 0.4552          | 0.8323   |
| 0.2052        | 21.0  | 420  | 0.5201          | 0.8006   |
| 0.2182        | 22.0  | 440  | 0.3928          | 0.8544   |
| 0.1698        | 23.0  | 460  | 0.4459          | 0.8449   |
| 0.1618        | 24.0  | 480  | 0.4502          | 0.8323   |
| 0.1915        | 25.0  | 500  | 0.4057          | 0.8703   |
| 0.1596        | 26.0  | 520  | 0.4650          | 0.8386   |
| 0.1446        | 27.0  | 540  | 0.3713          | 0.8766   |
| 0.17          | 28.0  | 560  | 0.4394          | 0.8544   |
| 0.141         | 29.0  | 580  | 0.5494          | 0.8196   |
| 0.1563        | 30.0  | 600  | 0.5431          | 0.8196   |
| 0.1216        | 31.0  | 620  | 0.5010          | 0.8481   |
| 0.1081        | 32.0  | 640  | 0.4454          | 0.8608   |
| 0.1205        | 33.0  | 660  | 0.4664          | 0.8418   |
| 0.1325        | 34.0  | 680  | 0.4690          | 0.8481   |
| 0.1152        | 35.0  | 700  | 0.3433          | 0.9019   |
| 0.1218        | 36.0  | 720  | 0.4063          | 0.8671   |
| 0.1163        | 37.0  | 740  | 0.3552          | 0.8861   |
| 0.0976        | 38.0  | 760  | 0.4137          | 0.8734   |
| 0.1163        | 39.0  | 780  | 0.4193          | 0.8797   |
| 0.1034        | 40.0  | 800  | 0.3740          | 0.8892   |
| 0.1033        | 41.0  | 820  | 0.4036          | 0.8671   |
| 0.0806        | 42.0  | 840  | 0.4396          | 0.8639   |
| 0.0764        | 43.0  | 860  | 0.4137          | 0.8608   |
| 0.0955        | 44.0  | 880  | 0.4019          | 0.8734   |
| 0.0768        | 45.0  | 900  | 0.3778          | 0.8829   |
| 0.0824        | 46.0  | 920  | 0.3930          | 0.8829   |
| 0.0837        | 47.0  | 940  | 0.3524          | 0.8924   |
| 0.0817        | 48.0  | 960  | 0.3113          | 0.9177   |
| 0.0767        | 49.0  | 980  | 0.3881          | 0.8797   |
| 0.0769        | 50.0  | 1000 | 0.3158          | 0.9051   |


### Framework versions

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

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