---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_lung_and_colon_cancer
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9994
language:
- en
pipeline_tag: image-classification
---

# vit-base-patch16-224-in21k_lung_and_colon_cancer

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
It achieves the following results on the evaluation set:
- Loss: 0.0016
- Accuracy: 0.9994
- Weighted f1: 0.9994
- Micro f1: 0.9994
- Macro f1: 0.9994
- Weighted recall: 0.9994
- Micro recall: 0.9994
- Macro recall: 0.9994
- Weighted precision: 0.9994
- Micro precision: 0.9994
- Macro precision: 0.9994

## Model description

This is a multiclass image classification model of lung and colon cancers.

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/Multiclass%20Classification/Lung%20%26%20Colon%20Cancer/Lung_and_colon_cancer_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://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images

## Training procedure

### 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
- num_epochs: 3

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.0574        | 1.0   | 1250 | 0.0410          | 0.9864   | 0.9864      | 0.9864   | 0.9865   | 0.9864          | 0.9864       | 0.9864       | 0.9872             | 0.9864          | 0.9875          |
| 0.0031        | 2.0   | 2500 | 0.0105          | 0.9972   | 0.9972      | 0.9972   | 0.9972   | 0.9972          | 0.9972       | 0.9973       | 0.9972             | 0.9972          | 0.9972          |
| 0.0007        | 3.0   | 3750 | 0.0016          | 0.9994   | 0.9994      | 0.9994   | 0.9994   | 0.9994          | 0.9994       | 0.9994       | 0.9994             | 0.9994          | 0.9994          |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1