
<!-- 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_beit_large_SGD_lr001_fold3

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2443
- Accuracy: 0.9367

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4134        | 1.0   | 750   | 0.4578          | 0.825    |
| 0.3897        | 2.0   | 1500  | 0.3494          | 0.8717   |
| 0.2538        | 3.0   | 2250  | 0.3074          | 0.89     |
| 0.2898        | 4.0   | 3000  | 0.2876          | 0.8967   |
| 0.212         | 5.0   | 3750  | 0.2739          | 0.9      |
| 0.1975        | 6.0   | 4500  | 0.2648          | 0.9067   |
| 0.1857        | 7.0   | 5250  | 0.2544          | 0.91     |
| 0.2047        | 8.0   | 6000  | 0.2614          | 0.91     |
| 0.1901        | 9.0   | 6750  | 0.2463          | 0.915    |
| 0.182         | 10.0  | 7500  | 0.2440          | 0.915    |
| 0.2655        | 11.0  | 8250  | 0.2426          | 0.9183   |
| 0.1584        | 12.0  | 9000  | 0.2405          | 0.92     |
| 0.2169        | 13.0  | 9750  | 0.2368          | 0.925    |
| 0.1666        | 14.0  | 10500 | 0.2365          | 0.9333   |
| 0.282         | 15.0  | 11250 | 0.2380          | 0.9267   |
| 0.1277        | 16.0  | 12000 | 0.2369          | 0.9267   |
| 0.2054        | 17.0  | 12750 | 0.2344          | 0.925    |
| 0.1465        | 18.0  | 13500 | 0.2352          | 0.93     |
| 0.1759        | 19.0  | 14250 | 0.2365          | 0.9283   |
| 0.0868        | 20.0  | 15000 | 0.2403          | 0.9283   |
| 0.1812        | 21.0  | 15750 | 0.2380          | 0.9283   |
| 0.1248        | 22.0  | 16500 | 0.2389          | 0.9317   |
| 0.1404        | 23.0  | 17250 | 0.2377          | 0.9367   |
| 0.1164        | 24.0  | 18000 | 0.2350          | 0.9333   |
| 0.1067        | 25.0  | 18750 | 0.2360          | 0.9333   |
| 0.0814        | 26.0  | 19500 | 0.2358          | 0.935    |
| 0.1065        | 27.0  | 20250 | 0.2388          | 0.9317   |
| 0.0953        | 28.0  | 21000 | 0.2398          | 0.9317   |
| 0.1216        | 29.0  | 21750 | 0.2358          | 0.9383   |
| 0.1495        | 30.0  | 22500 | 0.2401          | 0.935    |
| 0.0682        | 31.0  | 23250 | 0.2459          | 0.935    |
| 0.3061        | 32.0  | 24000 | 0.2403          | 0.935    |
| 0.214         | 33.0  | 24750 | 0.2441          | 0.9333   |
| 0.0919        | 34.0  | 25500 | 0.2429          | 0.935    |
| 0.1257        | 35.0  | 26250 | 0.2397          | 0.9367   |
| 0.1134        | 36.0  | 27000 | 0.2409          | 0.935    |
| 0.1221        | 37.0  | 27750 | 0.2433          | 0.935    |
| 0.1964        | 38.0  | 28500 | 0.2410          | 0.935    |
| 0.0926        | 39.0  | 29250 | 0.2470          | 0.9333   |
| 0.1151        | 40.0  | 30000 | 0.2439          | 0.9333   |
| 0.1818        | 41.0  | 30750 | 0.2427          | 0.9367   |
| 0.1265        | 42.0  | 31500 | 0.2433          | 0.935    |
| 0.107         | 43.0  | 32250 | 0.2429          | 0.9367   |
| 0.1988        | 44.0  | 33000 | 0.2445          | 0.935    |
| 0.1644        | 45.0  | 33750 | 0.2452          | 0.935    |
| 0.0768        | 46.0  | 34500 | 0.2451          | 0.935    |
| 0.1023        | 47.0  | 35250 | 0.2466          | 0.935    |
| 0.1141        | 48.0  | 36000 | 0.2460          | 0.935    |
| 0.0835        | 49.0  | 36750 | 0.2441          | 0.9367   |
| 0.1655        | 50.0  | 37500 | 0.2443          | 0.9367   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_5x_beit_large_SGD_lr001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9366666666666666, "name": "Accuracy"}]}]}]}
transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us