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# SMIDS_5x_beit_large_Adamax_lr00001_fold2

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: 0.9844
- Accuracy: 0.9068

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3455        | 1.0   | 750   | 0.2557          | 0.9068   |
| 0.1366        | 2.0   | 1500  | 0.3152          | 0.9185   |
| 0.1038        | 3.0   | 2250  | 0.4774          | 0.9018   |
| 0.0412        | 4.0   | 3000  | 0.5931          | 0.9035   |
| 0.0269        | 5.0   | 3750  | 0.6670          | 0.8985   |
| 0.0002        | 6.0   | 4500  | 0.7085          | 0.9101   |
| 0.0029        | 7.0   | 5250  | 0.7716          | 0.9118   |
| 0.0004        | 8.0   | 6000  | 0.8645          | 0.8952   |
| 0.0013        | 9.0   | 6750  | 0.7621          | 0.9168   |
| 0.0001        | 10.0  | 7500  | 0.8635          | 0.9002   |
| 0.0004        | 11.0  | 8250  | 0.9876          | 0.8985   |
| 0.0041        | 12.0  | 9000  | 0.8853          | 0.9085   |
| 0.0           | 13.0  | 9750  | 0.8700          | 0.9085   |
| 0.0002        | 14.0  | 10500 | 0.8354          | 0.9185   |
| 0.0           | 15.0  | 11250 | 0.8713          | 0.9168   |
| 0.0           | 16.0  | 12000 | 0.8659          | 0.9135   |
| 0.0           | 17.0  | 12750 | 0.9307          | 0.9052   |
| 0.0002        | 18.0  | 13500 | 0.9841          | 0.9068   |
| 0.0           | 19.0  | 14250 | 0.9317          | 0.9101   |
| 0.0           | 20.0  | 15000 | 0.9871          | 0.9035   |
| 0.0           | 21.0  | 15750 | 1.0844          | 0.8968   |
| 0.0           | 22.0  | 16500 | 0.9391          | 0.9068   |
| 0.0588        | 23.0  | 17250 | 0.9980          | 0.9101   |
| 0.0           | 24.0  | 18000 | 0.9800          | 0.9101   |
| 0.0405        | 25.0  | 18750 | 1.0166          | 0.9118   |
| 0.0           | 26.0  | 19500 | 1.0372          | 0.9018   |
| 0.0277        | 27.0  | 20250 | 0.9707          | 0.9068   |
| 0.0           | 28.0  | 21000 | 0.9832          | 0.9068   |
| 0.0           | 29.0  | 21750 | 0.9673          | 0.9085   |
| 0.0072        | 30.0  | 22500 | 0.9937          | 0.9068   |
| 0.0           | 31.0  | 23250 | 0.9732          | 0.9068   |
| 0.0           | 32.0  | 24000 | 0.9508          | 0.9135   |
| 0.0           | 33.0  | 24750 | 0.9683          | 0.9068   |
| 0.0001        | 34.0  | 25500 | 0.9824          | 0.9101   |
| 0.0066        | 35.0  | 26250 | 1.0136          | 0.9085   |
| 0.0           | 36.0  | 27000 | 0.9598          | 0.9118   |
| 0.0039        | 37.0  | 27750 | 0.9937          | 0.9118   |
| 0.0001        | 38.0  | 28500 | 0.9797          | 0.9068   |
| 0.0           | 39.0  | 29250 | 1.0004          | 0.9085   |
| 0.0           | 40.0  | 30000 | 1.0041          | 0.9052   |
| 0.0           | 41.0  | 30750 | 0.9829          | 0.9085   |
| 0.0           | 42.0  | 31500 | 0.9742          | 0.9052   |
| 0.0           | 43.0  | 32250 | 0.9870          | 0.9018   |
| 0.0033        | 44.0  | 33000 | 0.9964          | 0.9035   |
| 0.0           | 45.0  | 33750 | 0.9962          | 0.9052   |
| 0.0           | 46.0  | 34500 | 0.9808          | 0.9085   |
| 0.0001        | 47.0  | 35250 | 0.9837          | 0.9068   |
| 0.0           | 48.0  | 36000 | 0.9823          | 0.9068   |
| 0.0017        | 49.0  | 36750 | 0.9878          | 0.9052   |
| 0.0           | 50.0  | 37500 | 0.9844          | 0.9068   |


### 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_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9068219633943427, "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