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# SMIDS_10x_deit_base_adamax_lr001_fold5

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1555
- Accuracy: 0.8983

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2836        | 1.0   | 750   | 0.3148          | 0.8783   |
| 0.2521        | 2.0   | 1500  | 0.3549          | 0.855    |
| 0.197         | 3.0   | 2250  | 0.2784          | 0.9033   |
| 0.1479        | 4.0   | 3000  | 0.4313          | 0.875    |
| 0.2653        | 5.0   | 3750  | 0.4295          | 0.89     |
| 0.1402        | 6.0   | 4500  | 0.3919          | 0.8917   |
| 0.1252        | 7.0   | 5250  | 0.3986          | 0.9017   |
| 0.0512        | 8.0   | 6000  | 0.4055          | 0.9067   |
| 0.075         | 9.0   | 6750  | 0.5568          | 0.8883   |
| 0.0257        | 10.0  | 7500  | 0.5002          | 0.895    |
| 0.0412        | 11.0  | 8250  | 0.4334          | 0.9      |
| 0.0362        | 12.0  | 9000  | 0.5297          | 0.9017   |
| 0.0107        | 13.0  | 9750  | 0.5317          | 0.8983   |
| 0.0368        | 14.0  | 10500 | 0.6015          | 0.8867   |
| 0.0028        | 15.0  | 11250 | 0.6460          | 0.9083   |
| 0.0047        | 16.0  | 12000 | 0.5423          | 0.9017   |
| 0.0269        | 17.0  | 12750 | 0.6312          | 0.89     |
| 0.0006        | 18.0  | 13500 | 0.7916          | 0.885    |
| 0.0079        | 19.0  | 14250 | 0.7098          | 0.8883   |
| 0.0001        | 20.0  | 15000 | 0.6811          | 0.895    |
| 0.0003        | 21.0  | 15750 | 0.6029          | 0.9017   |
| 0.002         | 22.0  | 16500 | 0.7205          | 0.895    |
| 0.0444        | 23.0  | 17250 | 0.5748          | 0.8967   |
| 0.0071        | 24.0  | 18000 | 0.6884          | 0.8983   |
| 0.0001        | 25.0  | 18750 | 0.8118          | 0.9017   |
| 0.0242        | 26.0  | 19500 | 0.7422          | 0.8983   |
| 0.0           | 27.0  | 20250 | 0.6831          | 0.9067   |
| 0.0015        | 28.0  | 21000 | 0.8463          | 0.9      |
| 0.0001        | 29.0  | 21750 | 0.8406          | 0.9      |
| 0.0063        | 30.0  | 22500 | 0.8650          | 0.905    |
| 0.0014        | 31.0  | 23250 | 0.8156          | 0.91     |
| 0.0002        | 32.0  | 24000 | 0.8003          | 0.9033   |
| 0.0           | 33.0  | 24750 | 0.9006          | 0.8967   |
| 0.0           | 34.0  | 25500 | 0.9577          | 0.9017   |
| 0.0           | 35.0  | 26250 | 0.9468          | 0.9      |
| 0.0           | 36.0  | 27000 | 0.9588          | 0.895    |
| 0.0037        | 37.0  | 27750 | 0.9252          | 0.9      |
| 0.0           | 38.0  | 28500 | 0.9582          | 0.895    |
| 0.0           | 39.0  | 29250 | 1.0397          | 0.8933   |
| 0.0           | 40.0  | 30000 | 1.0427          | 0.8917   |
| 0.0           | 41.0  | 30750 | 1.0408          | 0.8917   |
| 0.0           | 42.0  | 31500 | 1.0988          | 0.9      |
| 0.0           | 43.0  | 32250 | 1.0364          | 0.8967   |
| 0.0           | 44.0  | 33000 | 1.0741          | 0.8983   |
| 0.0           | 45.0  | 33750 | 1.1207          | 0.9017   |
| 0.0           | 46.0  | 34500 | 1.1311          | 0.9      |
| 0.0           | 47.0  | 35250 | 1.1330          | 0.9      |
| 0.0           | 48.0  | 36000 | 1.1490          | 0.8983   |
| 0.0           | 49.0  | 36750 | 1.1542          | 0.8983   |
| 0.0           | 50.0  | 37500 | 1.1555          | 0.8983   |


### 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": "facebook/deit-base-patch16-224", "model-index": [{"name": "SMIDS_10x_deit_base_adamax_lr001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8983333333333333, "name": "Accuracy"}]}]}]}
transformers safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us