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# SMIDS_5x_beit_large_Adamax_lr001_fold4

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: 1.7326
- Accuracy: 0.8433

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6635        | 1.0   | 750   | 0.6374          | 0.74     |
| 0.5059        | 2.0   | 1500  | 0.4781          | 0.8      |
| 0.3108        | 3.0   | 2250  | 0.5467          | 0.8083   |
| 0.3755        | 4.0   | 3000  | 0.4702          | 0.8283   |
| 0.3037        | 5.0   | 3750  | 0.5379          | 0.805    |
| 0.2953        | 6.0   | 4500  | 0.5422          | 0.82     |
| 0.2574        | 7.0   | 5250  | 0.5963          | 0.8267   |
| 0.2066        | 8.0   | 6000  | 0.5748          | 0.8117   |
| 0.2865        | 9.0   | 6750  | 0.6579          | 0.8233   |
| 0.2173        | 10.0  | 7500  | 0.6633          | 0.8333   |
| 0.1995        | 11.0  | 8250  | 0.6474          | 0.85     |
| 0.1359        | 12.0  | 9000  | 0.7877          | 0.8483   |
| 0.0957        | 13.0  | 9750  | 0.7525          | 0.845    |
| 0.168         | 14.0  | 10500 | 1.0396          | 0.835    |
| 0.1554        | 15.0  | 11250 | 0.7966          | 0.8183   |
| 0.016         | 16.0  | 12000 | 0.8881          | 0.835    |
| 0.037         | 17.0  | 12750 | 1.0675          | 0.835    |
| 0.0341        | 18.0  | 13500 | 1.1351          | 0.8317   |
| 0.1124        | 19.0  | 14250 | 1.2059          | 0.835    |
| 0.0111        | 20.0  | 15000 | 1.1038          | 0.8317   |
| 0.0037        | 21.0  | 15750 | 1.1503          | 0.83     |
| 0.0271        | 22.0  | 16500 | 1.2812          | 0.82     |
| 0.0066        | 23.0  | 17250 | 1.4516          | 0.8383   |
| 0.0033        | 24.0  | 18000 | 1.4199          | 0.8317   |
| 0.0002        | 25.0  | 18750 | 1.4577          | 0.835    |
| 0.0006        | 26.0  | 19500 | 1.2325          | 0.8533   |
| 0.0003        | 27.0  | 20250 | 1.4292          | 0.8317   |
| 0.0016        | 28.0  | 21000 | 1.3214          | 0.83     |
| 0.0004        | 29.0  | 21750 | 1.4365          | 0.8483   |
| 0.0001        | 30.0  | 22500 | 1.3428          | 0.835    |
| 0.0           | 31.0  | 23250 | 1.7055          | 0.825    |
| 0.0155        | 32.0  | 24000 | 1.6412          | 0.8383   |
| 0.0007        | 33.0  | 24750 | 1.6474          | 0.84     |
| 0.0293        | 34.0  | 25500 | 1.6507          | 0.8367   |
| 0.0           | 35.0  | 26250 | 1.7096          | 0.835    |
| 0.0           | 36.0  | 27000 | 1.5438          | 0.8433   |
| 0.0           | 37.0  | 27750 | 1.6144          | 0.8433   |
| 0.0003        | 38.0  | 28500 | 1.5908          | 0.8383   |
| 0.0014        | 39.0  | 29250 | 1.5352          | 0.84     |
| 0.0           | 40.0  | 30000 | 1.5800          | 0.8367   |
| 0.0           | 41.0  | 30750 | 1.6930          | 0.8417   |
| 0.0           | 42.0  | 31500 | 1.6931          | 0.8383   |
| 0.0           | 43.0  | 32250 | 1.6166          | 0.8433   |
| 0.0           | 44.0  | 33000 | 1.5961          | 0.8433   |
| 0.0           | 45.0  | 33750 | 1.6621          | 0.8417   |
| 0.0           | 46.0  | 34500 | 1.6549          | 0.845    |
| 0.0001        | 47.0  | 35250 | 1.6890          | 0.8433   |
| 0.0           | 48.0  | 36000 | 1.7167          | 0.8433   |
| 0.0           | 49.0  | 36750 | 1.7332          | 0.845    |
| 0.0           | 50.0  | 37500 | 1.7326          | 0.8433   |


### 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_Adamax_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8433333333333334, "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