---
license: apache-2.0
tags:
- generated_from_trainer
- medical
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-large-patch32-384-Hyper_Kvasir_Labeled_Images
  results: []
language:
- en
pipeline_tag: image-classification
---

# vit-large-patch32-384-Breast_Histopathology_Images

This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384).

It achieves the following results on the evaluation set:
- Loss: 0.3954
- Accuracy: 0.8202
- F1
  - Weighted: 0.8151
  - Micro: 0.8202
  - Macro: 0.7674
- Recall
  - Weighted: 0.8202
  - Micro: 0.8202
  - Macro: 0.7549
- Precision
  - Weighted: 0.8141
  - Micro: 0.8202
  - Macro: 0.7860

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/Breast%20Histopathology%20Images/Breast_Histopathology_Images_Using_ViT.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/EulerianKnight/breast-histopathology-images-train-test-valid-split

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.3536        | 1.0   | 649  | 0.3568          | 0.8455   | 0.8411      | 0.8455   | 0.8003   | 0.8455          | 0.8455       | 0.7863       | 0.8411             | 0.8455          | 0.8205          |
| 0.4417        | 2.0   | 1298 | 0.3954          | 0.8202   | 0.8151      | 0.8202   | 0.7674   | 0.8202          | 0.8202       | 0.7549       | 0.8141             | 0.8202          | 0.7860          |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3