
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_10x_deit_tiny_rms_001_fold3

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: 0.5645
- Accuracy: 0.8183

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8687        | 1.0   | 750   | 0.8350          | 0.5417   |
| 0.7645        | 2.0   | 1500  | 0.8377          | 0.545    |
| 0.8147        | 3.0   | 2250  | 0.8321          | 0.5633   |
| 0.707         | 4.0   | 3000  | 0.8069          | 0.5617   |
| 0.7456        | 5.0   | 3750  | 0.7498          | 0.66     |
| 0.6732        | 6.0   | 4500  | 0.6947          | 0.7067   |
| 0.7173        | 7.0   | 5250  | 0.6562          | 0.7233   |
| 0.6807        | 8.0   | 6000  | 0.6396          | 0.735    |
| 0.5542        | 9.0   | 6750  | 0.6404          | 0.725    |
| 0.5945        | 10.0  | 7500  | 0.6253          | 0.715    |
| 0.5981        | 11.0  | 8250  | 0.6007          | 0.7333   |
| 0.6124        | 12.0  | 9000  | 0.5926          | 0.7467   |
| 0.5651        | 13.0  | 9750  | 0.6373          | 0.725    |
| 0.5876        | 14.0  | 10500 | 0.6106          | 0.735    |
| 0.595         | 15.0  | 11250 | 0.5814          | 0.7417   |
| 0.6259        | 16.0  | 12000 | 0.6014          | 0.755    |
| 0.5932        | 17.0  | 12750 | 0.6177          | 0.7433   |
| 0.5894        | 18.0  | 13500 | 0.7384          | 0.68     |
| 0.605         | 19.0  | 14250 | 0.6249          | 0.715    |
| 0.5663        | 20.0  | 15000 | 0.6124          | 0.7367   |
| 0.5134        | 21.0  | 15750 | 0.5785          | 0.7433   |
| 0.6186        | 22.0  | 16500 | 0.5747          | 0.7533   |
| 0.5238        | 23.0  | 17250 | 0.5818          | 0.76     |
| 0.5431        | 24.0  | 18000 | 0.5901          | 0.73     |
| 0.5802        | 25.0  | 18750 | 0.5751          | 0.7583   |
| 0.532         | 26.0  | 19500 | 0.6079          | 0.745    |
| 0.4391        | 27.0  | 20250 | 0.5654          | 0.7683   |
| 0.5546        | 28.0  | 21000 | 0.5837          | 0.7717   |
| 0.5308        | 29.0  | 21750 | 0.5546          | 0.76     |
| 0.5138        | 30.0  | 22500 | 0.5584          | 0.7633   |
| 0.4508        | 31.0  | 23250 | 0.5616          | 0.78     |
| 0.4928        | 32.0  | 24000 | 0.5495          | 0.7683   |
| 0.5015        | 33.0  | 24750 | 0.5514          | 0.7717   |
| 0.4951        | 34.0  | 25500 | 0.5352          | 0.7683   |
| 0.47          | 35.0  | 26250 | 0.5246          | 0.7767   |
| 0.4942        | 36.0  | 27000 | 0.5348          | 0.7833   |
| 0.4733        | 37.0  | 27750 | 0.5546          | 0.7833   |
| 0.4787        | 38.0  | 28500 | 0.5356          | 0.7883   |
| 0.4477        | 39.0  | 29250 | 0.5284          | 0.795    |
| 0.5359        | 40.0  | 30000 | 0.5502          | 0.8      |
| 0.4568        | 41.0  | 30750 | 0.5425          | 0.7883   |
| 0.4376        | 42.0  | 31500 | 0.5402          | 0.79     |
| 0.4262        | 43.0  | 32250 | 0.5808          | 0.7617   |
| 0.4405        | 44.0  | 33000 | 0.5553          | 0.7983   |
| 0.3884        | 45.0  | 33750 | 0.5497          | 0.7783   |
| 0.37          | 46.0  | 34500 | 0.5855          | 0.8067   |
| 0.413         | 47.0  | 35250 | 0.5591          | 0.815    |
| 0.3776        | 48.0  | 36000 | 0.5614          | 0.8067   |
| 0.3505        | 49.0  | 36750 | 0.5713          | 0.805    |
| 0.3537        | 50.0  | 37500 | 0.5645          | 0.8183   |


### 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_rms_001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8183333333333334, "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