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# meat_calssify_fresh_crop_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: 1.1160
- Accuracy: 0.7161

## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.09          | 1.0   | 39   | 1.0822          | 0.4710   |
| 1.0215        | 2.0   | 78   | 0.9976          | 0.5419   |
| 0.9299        | 3.0   | 117  | 0.9265          | 0.5548   |
| 0.8981        | 4.0   | 156  | 0.8926          | 0.5935   |
| 0.8256        | 5.0   | 195  | 0.9415          | 0.5548   |
| 0.7763        | 6.0   | 234  | 0.9029          | 0.5935   |
| 0.6964        | 7.0   | 273  | 0.8402          | 0.5935   |
| 0.678         | 8.0   | 312  | 0.8272          | 0.6      |
| 0.6114        | 9.0   | 351  | 0.9511          | 0.5935   |
| 0.5694        | 10.0  | 390  | 0.7493          | 0.6645   |
| 0.5335        | 11.0  | 429  | 0.8895          | 0.6452   |
| 0.4437        | 12.0  | 468  | 0.7902          | 0.6774   |
| 0.4836        | 13.0  | 507  | 0.8206          | 0.6387   |
| 0.4167        | 14.0  | 546  | 0.8594          | 0.6710   |
| 0.3775        | 15.0  | 585  | 0.8840          | 0.6645   |
| 0.3132        | 16.0  | 624  | 0.7669          | 0.6710   |
| 0.3099        | 17.0  | 663  | 0.8012          | 0.6903   |
| 0.307         | 18.0  | 702  | 0.8098          | 0.6839   |
| 0.2905        | 19.0  | 741  | 0.7889          | 0.7226   |
| 0.2854        | 20.0  | 780  | 0.8555          | 0.6968   |
| 0.1875        | 21.0  | 819  | 0.8501          | 0.7097   |
| 0.2485        | 22.0  | 858  | 0.8381          | 0.7419   |
| 0.22          | 23.0  | 897  | 1.0090          | 0.6774   |
| 0.2283        | 24.0  | 936  | 0.9999          | 0.6323   |
| 0.1934        | 25.0  | 975  | 0.9455          | 0.7097   |
| 0.1841        | 26.0  | 1014 | 0.7737          | 0.7484   |
| 0.1711        | 27.0  | 1053 | 0.8872          | 0.7355   |
| 0.1579        | 28.0  | 1092 | 1.0535          | 0.6903   |
| 0.176         | 29.0  | 1131 | 0.9783          | 0.6968   |
| 0.2307        | 30.0  | 1170 | 0.8435          | 0.7226   |
| 0.1379        | 31.0  | 1209 | 0.9598          | 0.7097   |
| 0.1181        | 32.0  | 1248 | 0.9325          | 0.7419   |
| 0.1529        | 33.0  | 1287 | 1.0973          | 0.6839   |
| 0.1252        | 34.0  | 1326 | 0.8859          | 0.7484   |
| 0.1005        | 35.0  | 1365 | 0.9212          | 0.7613   |
| 0.1446        | 36.0  | 1404 | 0.7894          | 0.7806   |
| 0.0776        | 37.0  | 1443 | 0.9259          | 0.7484   |
| 0.1067        | 38.0  | 1482 | 1.0468          | 0.7226   |
| 0.0983        | 39.0  | 1521 | 0.9468          | 0.7355   |
| 0.1155        | 40.0  | 1560 | 1.0564          | 0.7226   |
| 0.1037        | 41.0  | 1599 | 1.0964          | 0.6968   |
| 0.102         | 42.0  | 1638 | 0.9690          | 0.7290   |
| 0.0904        | 43.0  | 1677 | 0.9662          | 0.7419   |
| 0.0577        | 44.0  | 1716 | 1.2786          | 0.6645   |
| 0.1086        | 45.0  | 1755 | 1.0993          | 0.7226   |
| 0.0698        | 46.0  | 1794 | 1.1927          | 0.7032   |
| 0.0532        | 47.0  | 1833 | 0.9616          | 0.7484   |
| 0.0705        | 48.0  | 1872 | 0.7846          | 0.7806   |
| 0.0611        | 49.0  | 1911 | 0.9952          | 0.7290   |
| 0.0769        | 50.0  | 1950 | 1.1160          | 0.7161   |


### Framework versions

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

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