---
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: organc-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. -->

# organc-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: 0.2795
- Accuracy: 0.9240
- Precision: 0.9199
- Recall: 0.9123
- F1: 0.9154

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7947        | 1.0   | 203  | 0.3123          | 0.8976   | 0.9090    | 0.8450 | 0.8632 |
| 0.6703        | 2.0   | 406  | 0.1400          | 0.9607   | 0.9590    | 0.9543 | 0.9535 |
| 0.5941        | 3.0   | 609  | 0.1182          | 0.9699   | 0.9647    | 0.9681 | 0.9649 |
| 0.5837        | 4.0   | 813  | 0.1016          | 0.9678   | 0.9558    | 0.9586 | 0.9551 |
| 0.5193        | 5.0   | 1016 | 0.0800          | 0.9791   | 0.9701    | 0.9684 | 0.9675 |
| 0.5513        | 6.0   | 1219 | 0.0579          | 0.9862   | 0.9831    | 0.9855 | 0.9840 |
| 0.4343        | 7.0   | 1422 | 0.0775          | 0.9833   | 0.9858    | 0.9818 | 0.9835 |
| 0.3942        | 8.0   | 1626 | 0.0782          | 0.9833   | 0.9813    | 0.9827 | 0.9817 |
| 0.2971        | 9.0   | 1829 | 0.0839          | 0.9862   | 0.9884    | 0.9866 | 0.9873 |
| 0.3242        | 9.99  | 2030 | 0.0745          | 0.9870   | 0.9877    | 0.9863 | 0.9868 |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2