---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
base_model: facebook/deit-base-patch16-224
model-index:
- name: derma-deit-base-finetuned
  results: []
---

<!-- 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. -->

# derma-deit-base-finetuned

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5476
- Accuracy: 0.7960
- Precision: 0.6643
- Recall: 0.5891
- F1: 0.6164

## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9012        | 1.0   | 109  | 0.7630          | 0.7228   | 0.4263    | 0.3269 | 0.3462 |
| 0.7636        | 2.0   | 219  | 0.7212          | 0.7288   | 0.5912    | 0.3631 | 0.3789 |
| 0.7189        | 3.0   | 328  | 0.7622          | 0.7258   | 0.4465    | 0.4230 | 0.3935 |
| 0.6904        | 4.0   | 438  | 0.7281          | 0.7438   | 0.4888    | 0.4484 | 0.4115 |
| 0.7658        | 5.0   | 547  | 0.7215          | 0.7398   | 0.4855    | 0.4252 | 0.3753 |
| 0.6363        | 6.0   | 657  | 0.6329          | 0.7677   | 0.6350    | 0.4928 | 0.5121 |
| 0.6299        | 7.0   | 766  | 0.6117          | 0.7717   | 0.5962    | 0.5781 | 0.5713 |
| 0.6011        | 8.0   | 876  | 0.5919          | 0.7797   | 0.6162    | 0.5757 | 0.5902 |
| 0.6043        | 9.0   | 985  | 0.5476          | 0.7946   | 0.6295    | 0.5813 | 0.5983 |
| 0.5671        | 9.95  | 1090 | 0.5544          | 0.7956   | 0.6273    | 0.5810 | 0.5998 |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2