---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: ViT-Breast-Cancer
  results: []
widget:
- src: https://pathology.jhu.edu/build/assets/breast/_gallery/invasive-lobular-carcinoma.jpg
  example_title: Invasive Lobular Carcinoma
pipeline_tag: image-classification
---

# ViT-Breast-Cancer

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a dataset of breast cancer microscope slides.

## Model description

This is a fine-tuned ViT (Google) that serves more as an exploration of vision transformers in medicine for my learning than as anything specific. I fine-tuned this model on a dataset of ~7000 images of breast cancer slides labelled as 'benign' or 'cancerous'.
I used the Transformers library and the out-of-the-box ViTForImageClassification configuration. 
Despite this being an incredibly barebones fine-tune, I hope you fine it useful! Any recommendations are welcome!

## Intended uses & limitations

This is a super basic fine tuned model. Please evaluate its performance for yourself do determine whether it can be useful for you. In a big picture sense, this model can tell apart benign and cancerous breast tissue samples. 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear

### Training results

- training_loss = 0.007100


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1