
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vedt-lg

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1817
- F1: 0.93
- Roc Auc: 0.95
- Accuracy: 0.92

## 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: 16
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1   | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|:--------:|
| 0.5369        | 1.0   | 122  | 0.5339          | 0.53 | 0.67    | 0.41     |
| 0.3995        | 2.0   | 245  | 0.3591          | 0.8  | 0.84    | 0.73     |
| 0.2357        | 3.0   | 367  | 0.2492          | 0.89 | 0.92    | 0.88     |
| 0.1409        | 4.0   | 490  | 0.2015          | 0.91 | 0.93    | 0.9      |
| 0.1137        | 4.98  | 610  | 0.1817          | 0.93 | 0.95    | 0.92     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1

depression_low depression_medium depression_high