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# smids_10x_deit_tiny_rms_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: 2.3327
- Accuracy: 0.8367

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7568        | 1.0   | 750   | 0.7218          | 0.6717   |
| 0.6894        | 2.0   | 1500  | 0.6565          | 0.6583   |
| 0.6167        | 3.0   | 2250  | 0.6175          | 0.7017   |
| 0.6218        | 4.0   | 3000  | 0.6221          | 0.705    |
| 0.5637        | 5.0   | 3750  | 0.5565          | 0.7667   |
| 0.5037        | 6.0   | 4500  | 0.5481          | 0.7533   |
| 0.441         | 7.0   | 5250  | 0.5584          | 0.7667   |
| 0.5186        | 8.0   | 6000  | 0.5150          | 0.78     |
| 0.4475        | 9.0   | 6750  | 0.5478          | 0.775    |
| 0.4117        | 10.0  | 7500  | 0.5408          | 0.7867   |
| 0.4227        | 11.0  | 8250  | 0.4928          | 0.8017   |
| 0.4314        | 12.0  | 9000  | 0.5195          | 0.785    |
| 0.3606        | 13.0  | 9750  | 0.4998          | 0.8033   |
| 0.3206        | 14.0  | 10500 | 0.5323          | 0.8083   |
| 0.2628        | 15.0  | 11250 | 0.5344          | 0.79     |
| 0.2274        | 16.0  | 12000 | 0.5778          | 0.81     |
| 0.1649        | 17.0  | 12750 | 0.5559          | 0.8283   |
| 0.2197        | 18.0  | 13500 | 0.5838          | 0.8017   |
| 0.242         | 19.0  | 14250 | 0.5757          | 0.83     |
| 0.178         | 20.0  | 15000 | 0.6143          | 0.8183   |
| 0.1596        | 21.0  | 15750 | 0.6500          | 0.8267   |
| 0.1681        | 22.0  | 16500 | 0.7191          | 0.83     |
| 0.1356        | 23.0  | 17250 | 0.6652          | 0.83     |
| 0.154         | 24.0  | 18000 | 0.7572          | 0.835    |
| 0.0973        | 25.0  | 18750 | 0.7886          | 0.8283   |
| 0.1206        | 26.0  | 19500 | 0.9030          | 0.8033   |
| 0.0856        | 27.0  | 20250 | 1.0266          | 0.8083   |
| 0.0629        | 28.0  | 21000 | 0.8154          | 0.8333   |
| 0.0803        | 29.0  | 21750 | 1.0582          | 0.8133   |
| 0.0608        | 30.0  | 22500 | 1.1240          | 0.8317   |
| 0.0468        | 31.0  | 23250 | 1.1197          | 0.8183   |
| 0.0343        | 32.0  | 24000 | 1.2322          | 0.8217   |
| 0.0156        | 33.0  | 24750 | 1.3344          | 0.8367   |
| 0.0192        | 34.0  | 25500 | 1.3961          | 0.8133   |
| 0.0219        | 35.0  | 26250 | 1.5315          | 0.8033   |
| 0.0147        | 36.0  | 27000 | 1.5425          | 0.8233   |
| 0.0123        | 37.0  | 27750 | 1.6413          | 0.835    |
| 0.0089        | 38.0  | 28500 | 1.7045          | 0.8167   |
| 0.0003        | 39.0  | 29250 | 1.6054          | 0.8183   |
| 0.0102        | 40.0  | 30000 | 1.6942          | 0.825    |
| 0.0008        | 41.0  | 30750 | 1.7260          | 0.84     |
| 0.0077        | 42.0  | 31500 | 1.9643          | 0.8217   |
| 0.0048        | 43.0  | 32250 | 2.0335          | 0.825    |
| 0.0015        | 44.0  | 33000 | 2.2512          | 0.8367   |
| 0.0015        | 45.0  | 33750 | 2.1796          | 0.8333   |
| 0.0001        | 46.0  | 34500 | 2.2799          | 0.83     |
| 0.0           | 47.0  | 35250 | 2.2493          | 0.8317   |
| 0.0           | 48.0  | 36000 | 2.3177          | 0.8417   |
| 0.0           | 49.0  | 36750 | 2.3130          | 0.8317   |
| 0.0           | 50.0  | 37500 | 2.3327          | 0.8367   |


### 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_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8366666666666667, "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