
<!-- 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_5x_deit_tiny_sgd_001_fold4

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3364
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7169        | 1.0   | 375   | 0.7100          | 0.7433   |
| 0.5434        | 2.0   | 750   | 0.5311          | 0.79     |
| 0.3914        | 3.0   | 1125  | 0.4593          | 0.8267   |
| 0.3964        | 4.0   | 1500  | 0.4238          | 0.8467   |
| 0.3421        | 5.0   | 1875  | 0.4004          | 0.84     |
| 0.3188        | 6.0   | 2250  | 0.3866          | 0.845    |
| 0.3163        | 7.0   | 2625  | 0.3739          | 0.855    |
| 0.2969        | 8.0   | 3000  | 0.3662          | 0.8533   |
| 0.2771        | 9.0   | 3375  | 0.3587          | 0.8567   |
| 0.2869        | 10.0  | 3750  | 0.3526          | 0.8533   |
| 0.2532        | 11.0  | 4125  | 0.3512          | 0.855    |
| 0.2402        | 12.0  | 4500  | 0.3469          | 0.8617   |
| 0.2625        | 13.0  | 4875  | 0.3448          | 0.855    |
| 0.2773        | 14.0  | 5250  | 0.3426          | 0.8583   |
| 0.1973        | 15.0  | 5625  | 0.3405          | 0.86     |
| 0.1939        | 16.0  | 6000  | 0.3381          | 0.8633   |
| 0.2343        | 17.0  | 6375  | 0.3367          | 0.8633   |
| 0.2253        | 18.0  | 6750  | 0.3355          | 0.8667   |
| 0.2395        | 19.0  | 7125  | 0.3367          | 0.8633   |
| 0.1792        | 20.0  | 7500  | 0.3346          | 0.8667   |
| 0.2141        | 21.0  | 7875  | 0.3352          | 0.865    |
| 0.206         | 22.0  | 8250  | 0.3351          | 0.8683   |
| 0.1902        | 23.0  | 8625  | 0.3337          | 0.87     |
| 0.1953        | 24.0  | 9000  | 0.3324          | 0.8717   |
| 0.2357        | 25.0  | 9375  | 0.3339          | 0.8683   |
| 0.1602        | 26.0  | 9750  | 0.3324          | 0.8717   |
| 0.2058        | 27.0  | 10125 | 0.3335          | 0.8667   |
| 0.1817        | 28.0  | 10500 | 0.3349          | 0.87     |
| 0.1565        | 29.0  | 10875 | 0.3343          | 0.8667   |
| 0.2147        | 30.0  | 11250 | 0.3327          | 0.8717   |
| 0.1942        | 31.0  | 11625 | 0.3340          | 0.87     |
| 0.1633        | 32.0  | 12000 | 0.3333          | 0.8717   |
| 0.1571        | 33.0  | 12375 | 0.3335          | 0.8733   |
| 0.218         | 34.0  | 12750 | 0.3350          | 0.8733   |
| 0.1424        | 35.0  | 13125 | 0.3354          | 0.8783   |
| 0.1796        | 36.0  | 13500 | 0.3353          | 0.8717   |
| 0.1702        | 37.0  | 13875 | 0.3349          | 0.8767   |
| 0.161         | 38.0  | 14250 | 0.3343          | 0.875    |
| 0.1961        | 39.0  | 14625 | 0.3352          | 0.8767   |
| 0.1721        | 40.0  | 15000 | 0.3365          | 0.88     |
| 0.1561        | 41.0  | 15375 | 0.3358          | 0.8783   |
| 0.1604        | 42.0  | 15750 | 0.3354          | 0.8783   |
| 0.1786        | 43.0  | 16125 | 0.3364          | 0.8817   |
| 0.1636        | 44.0  | 16500 | 0.3360          | 0.88     |
| 0.2307        | 45.0  | 16875 | 0.3365          | 0.8783   |
| 0.1578        | 46.0  | 17250 | 0.3360          | 0.8783   |
| 0.232         | 47.0  | 17625 | 0.3366          | 0.8817   |
| 0.1744        | 48.0  | 18000 | 0.3365          | 0.88     |
| 0.1493        | 49.0  | 18375 | 0.3364          | 0.88     |
| 0.1447        | 50.0  | 18750 | 0.3364          | 0.88     |


### 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-small-patch16-224", "model-index": [{"name": "smids_5x_deit_tiny_sgd_001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.88, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us