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# smids_10x_deit_tiny_sgd_0001_fold4

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: 0.4161
- Accuracy: 0.8417

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0688        | 1.0   | 750   | 1.0895          | 0.3983   |
| 0.9867        | 2.0   | 1500  | 0.9975          | 0.4833   |
| 0.9278        | 3.0   | 2250  | 0.9175          | 0.54     |
| 0.8528        | 4.0   | 3000  | 0.8474          | 0.5783   |
| 0.714         | 5.0   | 3750  | 0.7808          | 0.635    |
| 0.6866        | 6.0   | 4500  | 0.7206          | 0.6917   |
| 0.6101        | 7.0   | 5250  | 0.6718          | 0.7167   |
| 0.6452        | 8.0   | 6000  | 0.6299          | 0.7333   |
| 0.6113        | 9.0   | 6750  | 0.5989          | 0.7467   |
| 0.5055        | 10.0  | 7500  | 0.5743          | 0.7633   |
| 0.4983        | 11.0  | 8250  | 0.5553          | 0.7717   |
| 0.5538        | 12.0  | 9000  | 0.5377          | 0.7733   |
| 0.4959        | 13.0  | 9750  | 0.5236          | 0.7833   |
| 0.4737        | 14.0  | 10500 | 0.5129          | 0.7867   |
| 0.4376        | 15.0  | 11250 | 0.5024          | 0.7967   |
| 0.3926        | 16.0  | 12000 | 0.4941          | 0.8033   |
| 0.397         | 17.0  | 12750 | 0.4866          | 0.805    |
| 0.4304        | 18.0  | 13500 | 0.4793          | 0.8067   |
| 0.4526        | 19.0  | 14250 | 0.4737          | 0.81     |
| 0.4267        | 20.0  | 15000 | 0.4680          | 0.81     |
| 0.3746        | 21.0  | 15750 | 0.4626          | 0.8183   |
| 0.4237        | 22.0  | 16500 | 0.4581          | 0.815    |
| 0.4022        | 23.0  | 17250 | 0.4540          | 0.82     |
| 0.465         | 24.0  | 18000 | 0.4503          | 0.8233   |
| 0.3585        | 25.0  | 18750 | 0.4464          | 0.8267   |
| 0.3671        | 26.0  | 19500 | 0.4431          | 0.8267   |
| 0.3889        | 27.0  | 20250 | 0.4400          | 0.8283   |
| 0.3836        | 28.0  | 21000 | 0.4372          | 0.83     |
| 0.3751        | 29.0  | 21750 | 0.4351          | 0.83     |
| 0.3772        | 30.0  | 22500 | 0.4334          | 0.8333   |
| 0.3959        | 31.0  | 23250 | 0.4312          | 0.8333   |
| 0.3701        | 32.0  | 24000 | 0.4290          | 0.8317   |
| 0.3441        | 33.0  | 24750 | 0.4274          | 0.8317   |
| 0.371         | 34.0  | 25500 | 0.4262          | 0.8333   |
| 0.327         | 35.0  | 26250 | 0.4246          | 0.8333   |
| 0.3799        | 36.0  | 27000 | 0.4233          | 0.8367   |
| 0.3186        | 37.0  | 27750 | 0.4226          | 0.835    |
| 0.3955        | 38.0  | 28500 | 0.4215          | 0.835    |
| 0.4171        | 39.0  | 29250 | 0.4206          | 0.835    |
| 0.4116        | 40.0  | 30000 | 0.4196          | 0.8367   |
| 0.369         | 41.0  | 30750 | 0.4189          | 0.8383   |
| 0.3461        | 42.0  | 31500 | 0.4184          | 0.8383   |
| 0.3837        | 43.0  | 32250 | 0.4178          | 0.8417   |
| 0.3565        | 44.0  | 33000 | 0.4174          | 0.8417   |
| 0.3745        | 45.0  | 33750 | 0.4170          | 0.8417   |
| 0.3413        | 46.0  | 34500 | 0.4167          | 0.8417   |
| 0.301         | 47.0  | 35250 | 0.4164          | 0.8417   |
| 0.3105        | 48.0  | 36000 | 0.4162          | 0.8417   |
| 0.3511        | 49.0  | 36750 | 0.4161          | 0.8417   |
| 0.3221        | 50.0  | 37500 | 0.4161          | 0.8417   |


### 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_sgd_0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8416666666666667, "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