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# smids_10x_deit_tiny_rms_0001_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: 1.2831
- Accuracy: 0.745

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8666        | 1.0   | 750   | 0.8178          | 0.5867   |
| 0.793         | 2.0   | 1500  | 0.8394          | 0.5383   |
| 0.7315        | 3.0   | 2250  | 0.8051          | 0.6133   |
| 0.6465        | 4.0   | 3000  | 0.7374          | 0.65     |
| 0.703         | 5.0   | 3750  | 0.7241          | 0.6517   |
| 0.6607        | 6.0   | 4500  | 0.6935          | 0.6617   |
| 0.6906        | 7.0   | 5250  | 0.6781          | 0.675    |
| 0.673         | 8.0   | 6000  | 0.6701          | 0.7      |
| 0.5876        | 9.0   | 6750  | 0.6156          | 0.715    |
| 0.5761        | 10.0  | 7500  | 0.6686          | 0.6883   |
| 0.695         | 11.0  | 8250  | 0.6673          | 0.675    |
| 0.5527        | 12.0  | 9000  | 0.6193          | 0.7183   |
| 0.5532        | 13.0  | 9750  | 0.6407          | 0.6983   |
| 0.6398        | 14.0  | 10500 | 0.6327          | 0.7267   |
| 0.5686        | 15.0  | 11250 | 0.6250          | 0.71     |
| 0.6507        | 16.0  | 12000 | 0.6131          | 0.7183   |
| 0.586         | 17.0  | 12750 | 0.5959          | 0.7367   |
| 0.6263        | 18.0  | 13500 | 0.6433          | 0.7083   |
| 0.5943        | 19.0  | 14250 | 0.5766          | 0.7467   |
| 0.6095        | 20.0  | 15000 | 0.5801          | 0.7383   |
| 0.4915        | 21.0  | 15750 | 0.5843          | 0.7467   |
| 0.5994        | 22.0  | 16500 | 0.5711          | 0.74     |
| 0.4915        | 23.0  | 17250 | 0.5881          | 0.7367   |
| 0.5455        | 24.0  | 18000 | 0.5829          | 0.73     |
| 0.5646        | 25.0  | 18750 | 0.6056          | 0.73     |
| 0.4802        | 26.0  | 19500 | 0.5993          | 0.73     |
| 0.4066        | 27.0  | 20250 | 0.5797          | 0.7617   |
| 0.5295        | 28.0  | 21000 | 0.6131          | 0.7433   |
| 0.4838        | 29.0  | 21750 | 0.5976          | 0.7533   |
| 0.454         | 30.0  | 22500 | 0.5851          | 0.755    |
| 0.3428        | 31.0  | 23250 | 0.6240          | 0.745    |
| 0.3934        | 32.0  | 24000 | 0.6108          | 0.755    |
| 0.3564        | 33.0  | 24750 | 0.6563          | 0.755    |
| 0.4234        | 34.0  | 25500 | 0.6360          | 0.7633   |
| 0.3741        | 35.0  | 26250 | 0.6145          | 0.765    |
| 0.3785        | 36.0  | 27000 | 0.6637          | 0.7583   |
| 0.3282        | 37.0  | 27750 | 0.6548          | 0.7817   |
| 0.3768        | 38.0  | 28500 | 0.7250          | 0.7483   |
| 0.3263        | 39.0  | 29250 | 0.6603          | 0.7633   |
| 0.3862        | 40.0  | 30000 | 0.6936          | 0.7617   |
| 0.2705        | 41.0  | 30750 | 0.7486          | 0.7733   |
| 0.2694        | 42.0  | 31500 | 0.8322          | 0.7683   |
| 0.2855        | 43.0  | 32250 | 0.8068          | 0.7733   |
| 0.2669        | 44.0  | 33000 | 0.9199          | 0.755    |
| 0.2143        | 45.0  | 33750 | 0.9335          | 0.7667   |
| 0.1925        | 46.0  | 34500 | 1.0133          | 0.76     |
| 0.1987        | 47.0  | 35250 | 1.0665          | 0.745    |
| 0.1978        | 48.0  | 36000 | 1.1590          | 0.75     |
| 0.1441        | 49.0  | 36750 | 1.2474          | 0.7517   |
| 0.1372        | 50.0  | 37500 | 1.2831          | 0.745    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+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_0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.745, "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