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# SMIDS_10x_beit_large_adamax_lr0001_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: 1.3674
- Accuracy: 0.9085

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3173        | 1.0   | 1500  | 0.3520          | 0.9018   |
| 0.143         | 2.0   | 3000  | 0.4612          | 0.9035   |
| 0.0646        | 3.0   | 4500  | 0.5311          | 0.9018   |
| 0.0273        | 4.0   | 6000  | 0.7034          | 0.9052   |
| 0.0366        | 5.0   | 7500  | 0.7005          | 0.9018   |
| 0.0           | 6.0   | 9000  | 0.7926          | 0.8968   |
| 0.1855        | 7.0   | 10500 | 0.9299          | 0.8985   |
| 0.0           | 8.0   | 12000 | 0.8044          | 0.9101   |
| 0.0           | 9.0   | 13500 | 0.8063          | 0.9002   |
| 0.0           | 10.0  | 15000 | 0.9211          | 0.8952   |
| 0.0001        | 11.0  | 16500 | 0.8324          | 0.9018   |
| 0.0           | 12.0  | 18000 | 0.9864          | 0.9068   |
| 0.0           | 13.0  | 19500 | 1.0563          | 0.8918   |
| 0.0           | 14.0  | 21000 | 1.0182          | 0.8968   |
| 0.0147        | 15.0  | 22500 | 0.9189          | 0.9101   |
| 0.0           | 16.0  | 24000 | 1.0058          | 0.9018   |
| 0.004         | 17.0  | 25500 | 0.8606          | 0.9118   |
| 0.0           | 18.0  | 27000 | 0.9773          | 0.9068   |
| 0.0           | 19.0  | 28500 | 0.9715          | 0.9002   |
| 0.0           | 20.0  | 30000 | 1.0013          | 0.8968   |
| 0.0001        | 21.0  | 31500 | 1.1434          | 0.8918   |
| 0.0047        | 22.0  | 33000 | 1.0025          | 0.9052   |
| 0.0           | 23.0  | 34500 | 1.0806          | 0.9085   |
| 0.0           | 24.0  | 36000 | 1.0986          | 0.9035   |
| 0.0           | 25.0  | 37500 | 1.1166          | 0.9135   |
| 0.0           | 26.0  | 39000 | 1.1303          | 0.9085   |
| 0.0102        | 27.0  | 40500 | 1.1368          | 0.9035   |
| 0.0           | 28.0  | 42000 | 1.2109          | 0.9068   |
| 0.0           | 29.0  | 43500 | 1.1872          | 0.9085   |
| 0.0           | 30.0  | 45000 | 1.2507          | 0.8985   |
| 0.0           | 31.0  | 46500 | 1.2743          | 0.8985   |
| 0.0           | 32.0  | 48000 | 1.2395          | 0.9085   |
| 0.0           | 33.0  | 49500 | 1.2238          | 0.9085   |
| 0.0           | 34.0  | 51000 | 1.3382          | 0.9035   |
| 0.0           | 35.0  | 52500 | 1.3422          | 0.9068   |
| 0.0           | 36.0  | 54000 | 1.3132          | 0.9035   |
| 0.0           | 37.0  | 55500 | 1.3621          | 0.9052   |
| 0.0           | 38.0  | 57000 | 1.3957          | 0.9052   |
| 0.0           | 39.0  | 58500 | 1.3497          | 0.9052   |
| 0.0           | 40.0  | 60000 | 1.3550          | 0.9068   |
| 0.0           | 41.0  | 61500 | 1.3505          | 0.9118   |
| 0.0           | 42.0  | 63000 | 1.3522          | 0.9101   |
| 0.0           | 43.0  | 64500 | 1.3666          | 0.9085   |
| 0.0           | 44.0  | 66000 | 1.3624          | 0.9101   |
| 0.0           | 45.0  | 67500 | 1.3564          | 0.9085   |
| 0.0           | 46.0  | 69000 | 1.3585          | 0.9068   |
| 0.0           | 47.0  | 70500 | 1.3589          | 0.9052   |
| 0.0           | 48.0  | 72000 | 1.3639          | 0.9085   |
| 0.0           | 49.0  | 73500 | 1.3643          | 0.9085   |
| 0.0           | 50.0  | 75000 | 1.3674          | 0.9085   |


### 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_adamax_lr0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.908485856905158, "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