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# image_classification

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: 1.6838
- Accuracy: 0.525

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 10   | 1.3274          | 0.5125   |
| No log        | 2.0   | 20   | 1.3119          | 0.5188   |
| No log        | 3.0   | 30   | 1.3825          | 0.4625   |
| No log        | 4.0   | 40   | 1.2916          | 0.5312   |
| No log        | 5.0   | 50   | 1.2821          | 0.525    |
| No log        | 6.0   | 60   | 1.2407          | 0.525    |
| No log        | 7.0   | 70   | 1.3288          | 0.5125   |
| No log        | 8.0   | 80   | 1.2818          | 0.525    |
| No log        | 9.0   | 90   | 1.3710          | 0.4875   |
| No log        | 10.0  | 100  | 1.3298          | 0.5312   |
| No log        | 11.0  | 110  | 1.3539          | 0.475    |
| No log        | 12.0  | 120  | 1.4498          | 0.4688   |
| No log        | 13.0  | 130  | 1.5422          | 0.4437   |
| No log        | 14.0  | 140  | 1.4870          | 0.4625   |
| No log        | 15.0  | 150  | 1.4354          | 0.525    |
| No log        | 16.0  | 160  | 1.4286          | 0.4938   |
| No log        | 17.0  | 170  | 1.5332          | 0.4437   |
| No log        | 18.0  | 180  | 1.4164          | 0.5188   |
| No log        | 19.0  | 190  | 1.5024          | 0.4625   |
| No log        | 20.0  | 200  | 1.4730          | 0.5125   |
| No log        | 21.0  | 210  | 1.3083          | 0.55     |
| No log        | 22.0  | 220  | 1.4468          | 0.525    |
| No log        | 23.0  | 230  | 1.3198          | 0.525    |
| No log        | 24.0  | 240  | 1.3530          | 0.5563   |
| No log        | 25.0  | 250  | 1.4821          | 0.4938   |
| No log        | 26.0  | 260  | 1.3475          | 0.5437   |
| No log        | 27.0  | 270  | 1.5152          | 0.4875   |
| No log        | 28.0  | 280  | 1.4290          | 0.55     |
| No log        | 29.0  | 290  | 1.5505          | 0.5      |
| No log        | 30.0  | 300  | 1.5796          | 0.5062   |
| No log        | 31.0  | 310  | 1.5988          | 0.5125   |
| No log        | 32.0  | 320  | 1.6272          | 0.4875   |
| No log        | 33.0  | 330  | 1.4324          | 0.5437   |
| No log        | 34.0  | 340  | 1.5245          | 0.5062   |
| No log        | 35.0  | 350  | 1.7228          | 0.45     |
| No log        | 36.0  | 360  | 1.4861          | 0.525    |
| No log        | 37.0  | 370  | 1.5317          | 0.5312   |
| No log        | 38.0  | 380  | 1.7776          | 0.475    |
| No log        | 39.0  | 390  | 1.5386          | 0.5563   |
| No log        | 40.0  | 400  | 1.7608          | 0.475    |
| No log        | 41.0  | 410  | 1.5469          | 0.55     |
| No log        | 42.0  | 420  | 1.6919          | 0.4625   |
| No log        | 43.0  | 430  | 1.5814          | 0.525    |
| No log        | 44.0  | 440  | 1.5877          | 0.5125   |
| No log        | 45.0  | 450  | 1.6370          | 0.5188   |
| No log        | 46.0  | 460  | 1.7375          | 0.5188   |
| No log        | 47.0  | 470  | 1.7004          | 0.5      |
| No log        | 48.0  | 480  | 1.6309          | 0.4938   |
| No log        | 49.0  | 490  | 1.5931          | 0.5437   |
| 0.2996        | 50.0  | 500  | 1.7687          | 0.5062   |
| 0.2996        | 51.0  | 510  | 1.5321          | 0.5188   |
| 0.2996        | 52.0  | 520  | 1.8099          | 0.4688   |
| 0.2996        | 53.0  | 530  | 1.5138          | 0.575    |
| 0.2996        | 54.0  | 540  | 1.7569          | 0.4688   |
| 0.2996        | 55.0  | 550  | 1.7451          | 0.4813   |
| 0.2996        | 56.0  | 560  | 1.6871          | 0.5125   |
| 0.2996        | 57.0  | 570  | 1.6471          | 0.525    |
| 0.2996        | 58.0  | 580  | 1.6966          | 0.525    |
| 0.2996        | 59.0  | 590  | 1.7714          | 0.5      |
| 0.2996        | 60.0  | 600  | 1.4985          | 0.5938   |
| 0.2996        | 61.0  | 610  | 1.9804          | 0.4313   |
| 0.2996        | 62.0  | 620  | 1.6116          | 0.5375   |
| 0.2996        | 63.0  | 630  | 1.6056          | 0.525    |
| 0.2996        | 64.0  | 640  | 1.6115          | 0.5062   |
| 0.2996        | 65.0  | 650  | 1.9694          | 0.4625   |
| 0.2996        | 66.0  | 660  | 1.6338          | 0.5563   |
| 0.2996        | 67.0  | 670  | 1.4823          | 0.5938   |
| 0.2996        | 68.0  | 680  | 1.9253          | 0.5      |
| 0.2996        | 69.0  | 690  | 1.9015          | 0.4813   |
| 0.2996        | 70.0  | 700  | 1.5446          | 0.5687   |
| 0.2996        | 71.0  | 710  | 1.9302          | 0.4938   |
| 0.2996        | 72.0  | 720  | 1.6973          | 0.5375   |
| 0.2996        | 73.0  | 730  | 1.8271          | 0.5      |
| 0.2996        | 74.0  | 740  | 1.7559          | 0.5188   |
| 0.2996        | 75.0  | 750  | 1.8127          | 0.5312   |
| 0.2996        | 76.0  | 760  | 1.8096          | 0.4938   |
| 0.2996        | 77.0  | 770  | 1.8460          | 0.5062   |
| 0.2996        | 78.0  | 780  | 1.8853          | 0.4813   |
| 0.2996        | 79.0  | 790  | 1.7706          | 0.5125   |
| 0.2996        | 80.0  | 800  | 1.8129          | 0.5312   |
| 0.2996        | 81.0  | 810  | 1.9488          | 0.4688   |
| 0.2996        | 82.0  | 820  | 1.8817          | 0.4813   |
| 0.2996        | 83.0  | 830  | 1.6759          | 0.5563   |
| 0.2996        | 84.0  | 840  | 1.6884          | 0.5      |
| 0.2996        | 85.0  | 850  | 1.8146          | 0.4875   |
| 0.2996        | 86.0  | 860  | 1.6610          | 0.55     |
| 0.2996        | 87.0  | 870  | 1.8811          | 0.475    |
| 0.2996        | 88.0  | 880  | 1.8964          | 0.5062   |
| 0.2996        | 89.0  | 890  | 1.6848          | 0.5437   |
| 0.2996        | 90.0  | 900  | 1.8642          | 0.4938   |
| 0.2996        | 91.0  | 910  | 1.8819          | 0.5125   |
| 0.2996        | 92.0  | 920  | 1.9193          | 0.4875   |
| 0.2996        | 93.0  | 930  | 1.8110          | 0.5      |
| 0.2996        | 94.0  | 940  | 1.9086          | 0.4813   |
| 0.2996        | 95.0  | 950  | 1.8895          | 0.4625   |
| 0.2996        | 96.0  | 960  | 1.7554          | 0.5312   |
| 0.2996        | 97.0  | 970  | 1.8978          | 0.5188   |
| 0.2996        | 98.0  | 980  | 1.9791          | 0.4875   |
| 0.2996        | 99.0  | 990  | 1.7030          | 0.5687   |
| 0.0883        | 100.0 | 1000 | 1.8398          | 0.4813   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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