---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: organc-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. -->

# organc-vit-base-finetuned

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 medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2248
- Accuracy: 0.9283
- Precision: 0.9231
- Recall: 0.9160
- F1: 0.9189

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6907        | 1.0   | 203  | 0.2221          | 0.9202   | 0.9165    | 0.8691 | 0.8480 |
| 0.5616        | 2.0   | 406  | 0.1278          | 0.9720   | 0.9657    | 0.9694 | 0.9666 |
| 0.5515        | 3.0   | 609  | 0.1428          | 0.9649   | 0.9626    | 0.9640 | 0.9621 |
| 0.4941        | 4.0   | 813  | 0.1016          | 0.9724   | 0.9683    | 0.9696 | 0.9683 |
| 0.4764        | 5.0   | 1016 | 0.0998          | 0.9716   | 0.9654    | 0.9649 | 0.9637 |
| 0.4599        | 6.0   | 1219 | 0.0941          | 0.9758   | 0.9775    | 0.9788 | 0.9778 |
| 0.4525        | 7.0   | 1422 | 0.0861          | 0.9795   | 0.9812    | 0.9793 | 0.9800 |
| 0.3835        | 8.0   | 1626 | 0.0788          | 0.9849   | 0.9846    | 0.9850 | 0.9847 |
| 0.2767        | 9.0   | 1829 | 0.0935          | 0.9774   | 0.9805    | 0.9800 | 0.9798 |
| 0.299         | 9.99  | 2030 | 0.0701          | 0.9854   | 0.9843    | 0.9864 | 0.9852 |


### Framework versions

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