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# smids_10x_deit_tiny_adamax_00001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9769
- Accuracy: 0.8935

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2848        | 1.0   | 750   | 0.3201          | 0.8702   |
| 0.1766        | 2.0   | 1500  | 0.2882          | 0.8869   |
| 0.1715        | 3.0   | 2250  | 0.2793          | 0.8918   |
| 0.1528        | 4.0   | 3000  | 0.2928          | 0.8918   |
| 0.1474        | 5.0   | 3750  | 0.3209          | 0.8902   |
| 0.0904        | 6.0   | 4500  | 0.3386          | 0.8835   |
| 0.0506        | 7.0   | 5250  | 0.4033          | 0.8918   |
| 0.0528        | 8.0   | 6000  | 0.4656          | 0.8985   |
| 0.0576        | 9.0   | 6750  | 0.4872          | 0.8852   |
| 0.0247        | 10.0  | 7500  | 0.5636          | 0.8968   |
| 0.0283        | 11.0  | 8250  | 0.5957          | 0.8918   |
| 0.017         | 12.0  | 9000  | 0.6715          | 0.8952   |
| 0.0013        | 13.0  | 9750  | 0.7105          | 0.8885   |
| 0.0004        | 14.0  | 10500 | 0.7432          | 0.8819   |
| 0.0011        | 15.0  | 11250 | 0.7702          | 0.8968   |
| 0.0001        | 16.0  | 12000 | 0.8390          | 0.8935   |
| 0.0001        | 17.0  | 12750 | 0.8598          | 0.8918   |
| 0.0002        | 18.0  | 13500 | 0.8606          | 0.8852   |
| 0.0           | 19.0  | 14250 | 0.8893          | 0.8902   |
| 0.0001        | 20.0  | 15000 | 0.9092          | 0.8869   |
| 0.0           | 21.0  | 15750 | 0.9243          | 0.8935   |
| 0.0414        | 22.0  | 16500 | 0.8856          | 0.8935   |
| 0.0196        | 23.0  | 17250 | 0.9361          | 0.8902   |
| 0.0           | 24.0  | 18000 | 0.9456          | 0.8952   |
| 0.0           | 25.0  | 18750 | 0.9417          | 0.8935   |
| 0.0           | 26.0  | 19500 | 0.9178          | 0.8968   |
| 0.0233        | 27.0  | 20250 | 0.9491          | 0.8902   |
| 0.0           | 28.0  | 21000 | 0.9447          | 0.9002   |
| 0.0           | 29.0  | 21750 | 0.9458          | 0.8952   |
| 0.0           | 30.0  | 22500 | 0.9429          | 0.8935   |
| 0.0           | 31.0  | 23250 | 0.9485          | 0.8902   |
| 0.0           | 32.0  | 24000 | 0.9592          | 0.8918   |
| 0.0           | 33.0  | 24750 | 0.9720          | 0.8935   |
| 0.0           | 34.0  | 25500 | 0.9605          | 0.8918   |
| 0.0           | 35.0  | 26250 | 0.9711          | 0.8952   |
| 0.0           | 36.0  | 27000 | 0.9779          | 0.8902   |
| 0.0248        | 37.0  | 27750 | 0.9824          | 0.8918   |
| 0.0           | 38.0  | 28500 | 0.9776          | 0.8968   |
| 0.0           | 39.0  | 29250 | 0.9729          | 0.8968   |
| 0.0           | 40.0  | 30000 | 0.9687          | 0.8952   |
| 0.0           | 41.0  | 30750 | 0.9781          | 0.8952   |
| 0.0           | 42.0  | 31500 | 0.9860          | 0.8918   |
| 0.0           | 43.0  | 32250 | 0.9836          | 0.8918   |
| 0.0           | 44.0  | 33000 | 0.9765          | 0.8952   |
| 0.0004        | 45.0  | 33750 | 0.9803          | 0.8935   |
| 0.0           | 46.0  | 34500 | 0.9754          | 0.8952   |
| 0.0           | 47.0  | 35250 | 0.9749          | 0.8952   |
| 0.0           | 48.0  | 36000 | 0.9757          | 0.8952   |
| 0.0           | 49.0  | 36750 | 0.9765          | 0.8935   |
| 0.0           | 50.0  | 37500 | 0.9769          | 0.8935   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-tiny-patch16-224", "model-index": [{"name": "smids_10x_deit_tiny_adamax_00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8935108153078203, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us