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# SMIDS_10x_beit_base_adamax_lr0001_fold3

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8880
- Accuracy: 0.9183

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2027        | 1.0   | 750   | 0.2916          | 0.9033   |
| 0.0987        | 2.0   | 1500  | 0.3320          | 0.9133   |
| 0.0537        | 3.0   | 2250  | 0.4124          | 0.9167   |
| 0.0015        | 4.0   | 3000  | 0.5580          | 0.9183   |
| 0.0097        | 5.0   | 3750  | 0.6218          | 0.9033   |
| 0.0491        | 6.0   | 4500  | 0.7322          | 0.9067   |
| 0.0118        | 7.0   | 5250  | 0.7975          | 0.905    |
| 0.0003        | 8.0   | 6000  | 0.6795          | 0.92     |
| 0.0074        | 9.0   | 6750  | 0.6908          | 0.9067   |
| 0.0061        | 10.0  | 7500  | 0.8456          | 0.9033   |
| 0.015         | 11.0  | 8250  | 0.9017          | 0.8967   |
| 0.0004        | 12.0  | 9000  | 0.9270          | 0.8967   |
| 0.0297        | 13.0  | 9750  | 0.8384          | 0.9017   |
| 0.0           | 14.0  | 10500 | 0.9572          | 0.8967   |
| 0.0079        | 15.0  | 11250 | 0.9601          | 0.9033   |
| 0.0           | 16.0  | 12000 | 0.8892          | 0.9017   |
| 0.001         | 17.0  | 12750 | 0.8747          | 0.9067   |
| 0.0006        | 18.0  | 13500 | 0.8149          | 0.9167   |
| 0.0001        | 19.0  | 14250 | 0.8993          | 0.905    |
| 0.0004        | 20.0  | 15000 | 0.8815          | 0.9017   |
| 0.0           | 21.0  | 15750 | 0.9567          | 0.9067   |
| 0.021         | 22.0  | 16500 | 0.8931          | 0.9167   |
| 0.0           | 23.0  | 17250 | 0.9197          | 0.905    |
| 0.0005        | 24.0  | 18000 | 0.9561          | 0.905    |
| 0.0           | 25.0  | 18750 | 0.9267          | 0.905    |
| 0.0001        | 26.0  | 19500 | 0.8869          | 0.915    |
| 0.0           | 27.0  | 20250 | 0.9075          | 0.92     |
| 0.0006        | 28.0  | 21000 | 0.8919          | 0.9233   |
| 0.0           | 29.0  | 21750 | 0.9073          | 0.9233   |
| 0.0           | 30.0  | 22500 | 0.9427          | 0.9133   |
| 0.0           | 31.0  | 23250 | 0.9699          | 0.9167   |
| 0.0           | 32.0  | 24000 | 0.8970          | 0.92     |
| 0.0           | 33.0  | 24750 | 0.8906          | 0.915    |
| 0.0           | 34.0  | 25500 | 0.9525          | 0.9067   |
| 0.0           | 35.0  | 26250 | 0.9507          | 0.9117   |
| 0.0           | 36.0  | 27000 | 0.9551          | 0.91     |
| 0.0           | 37.0  | 27750 | 0.9855          | 0.9083   |
| 0.0002        | 38.0  | 28500 | 0.9830          | 0.9083   |
| 0.0102        | 39.0  | 29250 | 0.9990          | 0.905    |
| 0.0           | 40.0  | 30000 | 0.9284          | 0.915    |
| 0.0003        | 41.0  | 30750 | 0.9459          | 0.91     |
| 0.0           | 42.0  | 31500 | 0.9266          | 0.9133   |
| 0.0           | 43.0  | 32250 | 0.9145          | 0.9133   |
| 0.0           | 44.0  | 33000 | 0.9081          | 0.9183   |
| 0.0           | 45.0  | 33750 | 0.8986          | 0.9183   |
| 0.0           | 46.0  | 34500 | 0.8823          | 0.9217   |
| 0.0           | 47.0  | 35250 | 0.8756          | 0.9217   |
| 0.0           | 48.0  | 36000 | 0.8922          | 0.9183   |
| 0.0           | 49.0  | 36750 | 0.8888          | 0.9183   |
| 0.0           | 50.0  | 37500 | 0.8880          | 0.9183   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_base_adamax_lr0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9183333333333333, "name": "Accuracy"}]}]}]}
transformers safetensors beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us