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# SMIDS_10x_beit_large_rmsprop_lr00001_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: 1.2403
- Accuracy: 0.9167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1025        | 1.0   | 1500  | 0.3208          | 0.91     |
| 0.0323        | 2.0   | 3000  | 0.5459          | 0.915    |
| 0.0098        | 3.0   | 4500  | 0.6199          | 0.9233   |
| 0.0048        | 4.0   | 6000  | 0.6756          | 0.9133   |
| 0.007         | 5.0   | 7500  | 0.7576          | 0.9183   |
| 0.0274        | 6.0   | 9000  | 0.8747          | 0.895    |
| 0.0           | 7.0   | 10500 | 0.7799          | 0.9217   |
| 0.0           | 8.0   | 12000 | 0.7280          | 0.92     |
| 0.0034        | 9.0   | 13500 | 0.8042          | 0.9083   |
| 0.0           | 10.0  | 15000 | 0.8008          | 0.9117   |
| 0.0           | 11.0  | 16500 | 0.8067          | 0.9133   |
| 0.0           | 12.0  | 18000 | 0.8994          | 0.91     |
| 0.0           | 13.0  | 19500 | 0.9217          | 0.9083   |
| 0.0197        | 14.0  | 21000 | 1.1015          | 0.89     |
| 0.0           | 15.0  | 22500 | 0.9789          | 0.9083   |
| 0.0002        | 16.0  | 24000 | 1.0258          | 0.9      |
| 0.0           | 17.0  | 25500 | 0.9842          | 0.9117   |
| 0.0           | 18.0  | 27000 | 0.9784          | 0.9083   |
| 0.0           | 19.0  | 28500 | 1.0333          | 0.91     |
| 0.0           | 20.0  | 30000 | 1.0131          | 0.9      |
| 0.0           | 21.0  | 31500 | 0.9002          | 0.9133   |
| 0.0           | 22.0  | 33000 | 1.0273          | 0.91     |
| 0.0           | 23.0  | 34500 | 1.1805          | 0.9017   |
| 0.0           | 24.0  | 36000 | 0.9931          | 0.9133   |
| 0.0           | 25.0  | 37500 | 1.2358          | 0.8983   |
| 0.0           | 26.0  | 39000 | 0.9739          | 0.9083   |
| 0.0           | 27.0  | 40500 | 0.9325          | 0.9233   |
| 0.0           | 28.0  | 42000 | 1.0023          | 0.9133   |
| 0.0           | 29.0  | 43500 | 1.3496          | 0.8967   |
| 0.0           | 30.0  | 45000 | 1.0819          | 0.9167   |
| 0.0           | 31.0  | 46500 | 1.1240          | 0.915    |
| 0.0           | 32.0  | 48000 | 1.2818          | 0.905    |
| 0.0           | 33.0  | 49500 | 1.1499          | 0.9133   |
| 0.0           | 34.0  | 51000 | 1.2924          | 0.9067   |
| 0.0           | 35.0  | 52500 | 1.2407          | 0.9133   |
| 0.0           | 36.0  | 54000 | 1.4198          | 0.9017   |
| 0.0           | 37.0  | 55500 | 1.2064          | 0.9167   |
| 0.0           | 38.0  | 57000 | 1.1870          | 0.915    |
| 0.0           | 39.0  | 58500 | 1.2018          | 0.915    |
| 0.0           | 40.0  | 60000 | 1.0906          | 0.92     |
| 0.0           | 41.0  | 61500 | 1.1579          | 0.9217   |
| 0.0           | 42.0  | 63000 | 1.2410          | 0.9133   |
| 0.0           | 43.0  | 64500 | 1.2770          | 0.9117   |
| 0.0           | 44.0  | 66000 | 1.2662          | 0.9117   |
| 0.0           | 45.0  | 67500 | 1.2880          | 0.9117   |
| 0.0           | 46.0  | 69000 | 1.2684          | 0.9133   |
| 0.0           | 47.0  | 70500 | 1.2411          | 0.9133   |
| 0.0           | 48.0  | 72000 | 1.2411          | 0.9167   |
| 0.0           | 49.0  | 73500 | 1.2378          | 0.9183   |
| 0.0           | 50.0  | 75000 | 1.2403          | 0.9167   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_10x_beit_large_rmsprop_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9166666666666666, "name": "Accuracy"}]}]}]}
transformers safetensors 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