---
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: blood-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. -->

# blood-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: 0.0767
- Accuracy: 0.9737
- Precision: 0.9730
- Recall: 0.9706
- F1: 0.9718

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4839        | 1.0   | 187  | 0.2824          | 0.8978   | 0.9057    | 0.8662 | 0.8763 |
| 0.4762        | 2.0   | 374  | 0.2146          | 0.9282   | 0.9246    | 0.9161 | 0.9186 |
| 0.3445        | 3.0   | 561  | 0.2135          | 0.9235   | 0.9244    | 0.9159 | 0.9168 |
| 0.2963        | 4.0   | 748  | 0.1647          | 0.9416   | 0.9323    | 0.9427 | 0.9346 |
| 0.3328        | 5.0   | 935  | 0.1762          | 0.9387   | 0.9323    | 0.9372 | 0.9316 |
| 0.3138        | 6.0   | 1122 | 0.1480          | 0.9439   | 0.9421    | 0.9482 | 0.9426 |
| 0.2489        | 7.0   | 1309 | 0.1134          | 0.9620   | 0.9536    | 0.9609 | 0.9563 |
| 0.193         | 8.0   | 1496 | 0.1020          | 0.9638   | 0.9666    | 0.9581 | 0.9616 |
| 0.1973        | 9.0   | 1683 | 0.0754          | 0.9749   | 0.9733    | 0.9761 | 0.9743 |
| 0.1711        | 10.0  | 1870 | 0.0533          | 0.9819   | 0.9826    | 0.9824 | 0.9825 |


### Framework versions

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