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# vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3494
- Accuracy: 0.8484
- Recall: 0.8484
- F1: 0.8478
- Precision: 0.8513

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5792        | 0.9974 | 293  | 0.5989          | 0.7969   | 0.7969 | 0.7829 | 0.7897    |
| 0.42          | 1.9983 | 587  | 0.5251          | 0.8046   | 0.8046 | 0.7960 | 0.7985    |
| 0.3501        | 2.9991 | 881  | 0.4299          | 0.8335   | 0.8335 | 0.8312 | 0.8363    |
| 0.3187        | 4.0    | 1175 | 0.4302          | 0.8169   | 0.8169 | 0.8144 | 0.8182    |
| 0.3873        | 4.9974 | 1468 | 0.4246          | 0.8250   | 0.8250 | 0.8238 | 0.8326    |
| 0.3786        | 5.9983 | 1762 | 0.3881          | 0.8306   | 0.8306 | 0.8303 | 0.8394    |
| 0.337         | 6.9991 | 2056 | 0.3803          | 0.8306   | 0.8306 | 0.8304 | 0.8351    |
| 0.2717        | 8.0    | 2350 | 0.3785          | 0.8395   | 0.8395 | 0.8361 | 0.8482    |
| 0.2753        | 8.9974 | 2643 | 0.3805          | 0.8327   | 0.8327 | 0.8314 | 0.8346    |
| 0.2814        | 9.9745 | 2930 | 0.3362          | 0.8480   | 0.8480 | 0.8467 | 0.8499    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1

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