---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/deit-base-patch16-224
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: organsmnist-vit-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. -->

# organsmnist-vit-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: 0.2964
- Accuracy: 0.8993
- Precision: 0.8443
- Recall: 0.8396
- F1: 0.8394

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9084        | 1.0   | 218  | 0.7151          | 0.7288   | 0.6998    | 0.6620 | 0.6412 |
| 0.89          | 2.0   | 436  | 0.3658          | 0.8540   | 0.7873    | 0.7898 | 0.7660 |
| 0.7851        | 3.0   | 654  | 0.3514          | 0.8438   | 0.8110    | 0.7674 | 0.7741 |
| 0.7144        | 4.0   | 872  | 0.3632          | 0.8670   | 0.8415    | 0.8133 | 0.7980 |
| 0.7383        | 5.0   | 1090 | 0.3680          | 0.8581   | 0.7769    | 0.8029 | 0.7786 |
| 0.6065        | 6.0   | 1308 | 0.2824          | 0.8870   | 0.8481    | 0.8328 | 0.8305 |
| 0.521         | 7.0   | 1526 | 0.2769          | 0.8940   | 0.8439    | 0.8404 | 0.8297 |
| 0.5305        | 8.0   | 1744 | 0.2611          | 0.9001   | 0.8517    | 0.8463 | 0.8447 |
| 0.4522        | 9.0   | 1962 | 0.2742          | 0.9058   | 0.8594    | 0.8517 | 0.8411 |
| 0.4445        | 10.0  | 2180 | 0.2964          | 0.8993   | 0.8443    | 0.8396 | 0.8394 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2