
## Description  

[Google ViT](https://huggingface.co/google/vit-base-patch16-224-in21k) model is finetuned on lung and colon histopathology image classification dataset. 
The dataset is available on [Kaggle](https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images). 

## About Dataset

It is a multiclass image classification dataset. This dataset contains 25,000 histopathological images with 5 classes.

There are five classes in the dataset, each with 5,000 images, being:

* Lung benign tissue
* Lung adenocarcinoma
* Lung squamous cell carcinoma
* Colon adenocarcinoma
* Colon benign tissue

## Training Results

Epoch |	Training Loss | Training Accuracy |	Validation Loss	| Validation Accuracy
--- | --- | --- | --- |---
1 |	0.2062 | 0.9760 | 0.0887 |	0.9948
2 |	0.0394 | 0.9990 | 0.0065 |	0.9986
3 |	0.0174 | 1.0 | 0.00507 | 0.9990
{"tags": ["medical"], "metrics": ["accuracy"], "pipeline_tag": "image-classification", "basemodel": "google/vit-base-patch16-224-in21k"}
transformers tf vit image-classification medical autotrain_compatible endpoints_compatible region:us