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# SMIDS_10x_beit_large_adamax_lr001_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: 1.1801
- Accuracy: 0.8935

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4134        | 1.0   | 1500  | 0.3865          | 0.8619   |
| 0.4213        | 2.0   | 3000  | 0.3826          | 0.8669   |
| 0.3093        | 3.0   | 4500  | 0.3172          | 0.8686   |
| 0.4044        | 4.0   | 6000  | 0.4112          | 0.8602   |
| 0.2228        | 5.0   | 7500  | 0.4291          | 0.8602   |
| 0.1446        | 6.0   | 9000  | 0.3511          | 0.8885   |
| 0.1812        | 7.0   | 10500 | 0.4466          | 0.8869   |
| 0.0977        | 8.0   | 12000 | 0.3354          | 0.8985   |
| 0.1256        | 9.0   | 13500 | 0.5332          | 0.8769   |
| 0.0592        | 10.0  | 15000 | 0.5954          | 0.8769   |
| 0.148         | 11.0  | 16500 | 0.6982          | 0.8719   |
| 0.0841        | 12.0  | 18000 | 0.5120          | 0.8968   |
| 0.0743        | 13.0  | 19500 | 0.7495          | 0.8569   |
| 0.0024        | 14.0  | 21000 | 0.5634          | 0.8902   |
| 0.0023        | 15.0  | 22500 | 0.6139          | 0.9002   |
| 0.027         | 16.0  | 24000 | 0.6038          | 0.9002   |
| 0.0006        | 17.0  | 25500 | 0.7155          | 0.8952   |
| 0.0006        | 18.0  | 27000 | 0.7559          | 0.8918   |
| 0.0008        | 19.0  | 28500 | 0.7483          | 0.8935   |
| 0.0089        | 20.0  | 30000 | 0.7820          | 0.8869   |
| 0.0036        | 21.0  | 31500 | 0.8566          | 0.8902   |
| 0.0687        | 22.0  | 33000 | 0.9354          | 0.8802   |
| 0.0029        | 23.0  | 34500 | 0.8159          | 0.8885   |
| 0.0001        | 24.0  | 36000 | 0.8273          | 0.8885   |
| 0.0           | 25.0  | 37500 | 0.8247          | 0.8918   |
| 0.0001        | 26.0  | 39000 | 0.6357          | 0.8968   |
| 0.0292        | 27.0  | 40500 | 0.7434          | 0.9068   |
| 0.0001        | 28.0  | 42000 | 0.8032          | 0.8952   |
| 0.0003        | 29.0  | 43500 | 0.8008          | 0.9018   |
| 0.0           | 30.0  | 45000 | 0.8479          | 0.9018   |
| 0.0004        | 31.0  | 46500 | 0.9181          | 0.8968   |
| 0.0           | 32.0  | 48000 | 0.9657          | 0.9035   |
| 0.0001        | 33.0  | 49500 | 0.9088          | 0.8935   |
| 0.0           | 34.0  | 51000 | 1.0294          | 0.8835   |
| 0.0           | 35.0  | 52500 | 1.0399          | 0.8952   |
| 0.0           | 36.0  | 54000 | 1.0674          | 0.8902   |
| 0.0001        | 37.0  | 55500 | 1.0172          | 0.8985   |
| 0.0004        | 38.0  | 57000 | 0.9968          | 0.8869   |
| 0.0           | 39.0  | 58500 | 1.0410          | 0.8985   |
| 0.0           | 40.0  | 60000 | 1.0276          | 0.8935   |
| 0.0           | 41.0  | 61500 | 1.0252          | 0.9002   |
| 0.0           | 42.0  | 63000 | 1.1429          | 0.8985   |
| 0.0           | 43.0  | 64500 | 1.1733          | 0.8968   |
| 0.0           | 44.0  | 66000 | 1.1360          | 0.9002   |
| 0.0           | 45.0  | 67500 | 1.1449          | 0.8918   |
| 0.0           | 46.0  | 69000 | 1.1361          | 0.9002   |
| 0.0           | 47.0  | 70500 | 1.1607          | 0.8935   |
| 0.0           | 48.0  | 72000 | 1.1682          | 0.8935   |
| 0.0           | 49.0  | 73500 | 1.1770          | 0.8935   |
| 0.0           | 50.0  | 75000 | 1.1801          | 0.8935   |


### 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_lr001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8935108153078203, "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