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

# breastmnist-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: 0.3129
- Accuracy: 0.8782
- Precision: 0.8971
- Recall: 0.7888
- F1: 0.8232

## 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.9143 | 8    | 0.4751          | 0.7821   | 0.8851    | 0.5952 | 0.5951 |
| 0.5516        | 1.9429 | 17   | 0.4166          | 0.8462   | 0.8091    | 0.7895 | 0.7983 |
| 0.478         | 2.9714 | 26   | 0.3676          | 0.8205   | 0.7792    | 0.7419 | 0.7565 |
| 0.4617        | 4.0    | 35   | 0.3180          | 0.8718   | 0.8698    | 0.7920 | 0.8194 |
| 0.4208        | 4.9143 | 43   | 0.4562          | 0.8590   | 0.8173    | 0.8584 | 0.8325 |
| 0.3759        | 5.9429 | 52   | 0.3780          | 0.8718   | 0.8332    | 0.8521 | 0.8417 |
| 0.3689        | 6.9714 | 61   | 0.2993          | 0.8846   | 0.9018    | 0.8008 | 0.8342 |
| 0.3322        | 8.0    | 70   | 0.2785          | 0.8718   | 0.8698    | 0.7920 | 0.8194 |
| 0.3322        | 8.9143 | 78   | 0.2700          | 0.8846   | 0.9018    | 0.8008 | 0.8342 |
| 0.3242        | 9.1429 | 80   | 0.2690          | 0.8846   | 0.9018    | 0.8008 | 0.8342 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1