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# smids_10x_deit_tiny_sgd_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: 0.3822
- Accuracy: 0.85

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0766        | 1.0   | 750   | 1.0931          | 0.4283   |
| 0.9667        | 2.0   | 1500  | 1.0054          | 0.5083   |
| 0.9193        | 3.0   | 2250  | 0.9314          | 0.5533   |
| 0.7955        | 4.0   | 3000  | 0.8645          | 0.595    |
| 0.78          | 5.0   | 3750  | 0.7998          | 0.6283   |
| 0.6828        | 6.0   | 4500  | 0.7419          | 0.6833   |
| 0.6256        | 7.0   | 5250  | 0.6910          | 0.715    |
| 0.6533        | 8.0   | 6000  | 0.6501          | 0.7417   |
| 0.5725        | 9.0   | 6750  | 0.6153          | 0.76     |
| 0.5585        | 10.0  | 7500  | 0.5875          | 0.7733   |
| 0.5119        | 11.0  | 8250  | 0.5647          | 0.7917   |
| 0.4751        | 12.0  | 9000  | 0.5443          | 0.8033   |
| 0.4748        | 13.0  | 9750  | 0.5264          | 0.815    |
| 0.543         | 14.0  | 10500 | 0.5129          | 0.8167   |
| 0.4792        | 15.0  | 11250 | 0.4995          | 0.8233   |
| 0.4638        | 16.0  | 12000 | 0.4900          | 0.8267   |
| 0.4373        | 17.0  | 12750 | 0.4791          | 0.8267   |
| 0.4624        | 18.0  | 13500 | 0.4695          | 0.8333   |
| 0.4581        | 19.0  | 14250 | 0.4625          | 0.8317   |
| 0.4031        | 20.0  | 15000 | 0.4549          | 0.8283   |
| 0.3798        | 21.0  | 15750 | 0.4466          | 0.8367   |
| 0.4127        | 22.0  | 16500 | 0.4410          | 0.84     |
| 0.4351        | 23.0  | 17250 | 0.4350          | 0.8433   |
| 0.4464        | 24.0  | 18000 | 0.4308          | 0.835    |
| 0.4334        | 25.0  | 18750 | 0.4260          | 0.8333   |
| 0.4382        | 26.0  | 19500 | 0.4212          | 0.8367   |
| 0.3088        | 27.0  | 20250 | 0.4169          | 0.8367   |
| 0.3982        | 28.0  | 21000 | 0.4136          | 0.8367   |
| 0.4061        | 29.0  | 21750 | 0.4101          | 0.8367   |
| 0.4007        | 30.0  | 22500 | 0.4079          | 0.84     |
| 0.3333        | 31.0  | 23250 | 0.4046          | 0.8417   |
| 0.3804        | 32.0  | 24000 | 0.4012          | 0.84     |
| 0.4007        | 33.0  | 24750 | 0.3989          | 0.8417   |
| 0.4048        | 34.0  | 25500 | 0.3970          | 0.8417   |
| 0.3319        | 35.0  | 26250 | 0.3948          | 0.8433   |
| 0.3736        | 36.0  | 27000 | 0.3932          | 0.8467   |
| 0.3994        | 37.0  | 27750 | 0.3918          | 0.8483   |
| 0.3998        | 38.0  | 28500 | 0.3900          | 0.8483   |
| 0.3526        | 39.0  | 29250 | 0.3888          | 0.85     |
| 0.4438        | 40.0  | 30000 | 0.3872          | 0.8483   |
| 0.3369        | 41.0  | 30750 | 0.3860          | 0.8517   |
| 0.3716        | 42.0  | 31500 | 0.3855          | 0.8517   |
| 0.3661        | 43.0  | 32250 | 0.3844          | 0.8517   |
| 0.3454        | 44.0  | 33000 | 0.3840          | 0.8517   |
| 0.3872        | 45.0  | 33750 | 0.3835          | 0.8517   |
| 0.3238        | 46.0  | 34500 | 0.3830          | 0.8517   |
| 0.404         | 47.0  | 35250 | 0.3826          | 0.8517   |
| 0.3607        | 48.0  | 36000 | 0.3823          | 0.8517   |
| 0.3506        | 49.0  | 36750 | 0.3823          | 0.8517   |
| 0.3442        | 50.0  | 37500 | 0.3822          | 0.85     |


### 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_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.85, "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