README.md

metadata

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.

It achieves the following results on the evaluation set:

• Loss: 0.0016
• Accuracy: 0.9994
• F1
  • Weighted: 0.9994
  • Micro: 0.9994
  • Macro: 0.9994
• Recall
  • Weighted: 0.9994
  • Micro: 0.9994
  • Macro: 0.9994
• Precision
  • Weighted: 0.9994
  • Micro: 0.9994
  • Macro: 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

Sample Images From Dataset:

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