
<!-- 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_lr0001_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: 0.3184
- Accuracy: 0.8783

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0416        | 1.0   | 750   | 1.0496          | 0.4617   |
| 0.842         | 2.0   | 1500  | 0.8754          | 0.5867   |
| 0.6732        | 3.0   | 2250  | 0.7628          | 0.655    |
| 0.6558        | 4.0   | 3000  | 0.6816          | 0.7167   |
| 0.5649        | 5.0   | 3750  | 0.6229          | 0.7567   |
| 0.5725        | 6.0   | 4500  | 0.5768          | 0.7717   |
| 0.5051        | 7.0   | 5250  | 0.5405          | 0.7867   |
| 0.4785        | 8.0   | 6000  | 0.5106          | 0.8      |
| 0.4502        | 9.0   | 6750  | 0.4865          | 0.8183   |
| 0.459         | 10.0  | 7500  | 0.4663          | 0.825    |
| 0.4652        | 11.0  | 8250  | 0.4489          | 0.8267   |
| 0.3845        | 12.0  | 9000  | 0.4345          | 0.8283   |
| 0.4015        | 13.0  | 9750  | 0.4220          | 0.8333   |
| 0.3495        | 14.0  | 10500 | 0.4110          | 0.8383   |
| 0.4404        | 15.0  | 11250 | 0.4019          | 0.84     |
| 0.3293        | 16.0  | 12000 | 0.3936          | 0.8433   |
| 0.418         | 17.0  | 12750 | 0.3860          | 0.8517   |
| 0.3713        | 18.0  | 13500 | 0.3792          | 0.8533   |
| 0.388         | 19.0  | 14250 | 0.3731          | 0.8567   |
| 0.3056        | 20.0  | 15000 | 0.3679          | 0.8583   |
| 0.3283        | 21.0  | 15750 | 0.3637          | 0.8633   |
| 0.3036        | 22.0  | 16500 | 0.3588          | 0.8667   |
| 0.3515        | 23.0  | 17250 | 0.3548          | 0.8667   |
| 0.3016        | 24.0  | 18000 | 0.3513          | 0.8667   |
| 0.3487        | 25.0  | 18750 | 0.3478          | 0.865    |
| 0.2654        | 26.0  | 19500 | 0.3445          | 0.865    |
| 0.3213        | 27.0  | 20250 | 0.3419          | 0.8683   |
| 0.2599        | 28.0  | 21000 | 0.3392          | 0.8667   |
| 0.2435        | 29.0  | 21750 | 0.3369          | 0.865    |
| 0.3034        | 30.0  | 22500 | 0.3352          | 0.8683   |
| 0.2822        | 31.0  | 23250 | 0.3333          | 0.87     |
| 0.4196        | 32.0  | 24000 | 0.3316          | 0.87     |
| 0.4078        | 33.0  | 24750 | 0.3298          | 0.8717   |
| 0.3083        | 34.0  | 25500 | 0.3285          | 0.8733   |
| 0.3626        | 35.0  | 26250 | 0.3269          | 0.8767   |
| 0.2744        | 36.0  | 27000 | 0.3258          | 0.8767   |
| 0.287         | 37.0  | 27750 | 0.3248          | 0.8767   |
| 0.3727        | 38.0  | 28500 | 0.3238          | 0.8783   |
| 0.3081        | 39.0  | 29250 | 0.3228          | 0.8783   |
| 0.2783        | 40.0  | 30000 | 0.3219          | 0.8783   |
| 0.2906        | 41.0  | 30750 | 0.3211          | 0.8783   |
| 0.2899        | 42.0  | 31500 | 0.3206          | 0.8783   |
| 0.3103        | 43.0  | 32250 | 0.3200          | 0.8783   |
| 0.3549        | 44.0  | 33000 | 0.3195          | 0.8783   |
| 0.3654        | 45.0  | 33750 | 0.3191          | 0.8783   |
| 0.2355        | 46.0  | 34500 | 0.3188          | 0.8783   |
| 0.2194        | 47.0  | 35250 | 0.3186          | 0.8783   |
| 0.2844        | 48.0  | 36000 | 0.3185          | 0.8783   |
| 0.242         | 49.0  | 36750 | 0.3184          | 0.8783   |
| 0.3336        | 50.0  | 37500 | 0.3184          | 0.8783   |


### 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_lr0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8783333333333333, "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