| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - f1 |
| - recall |
| - precision |
| model-index: |
| - name: vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification |
| 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: 0.8197969543147208 |
| language: |
| - en |
| pipeline_tag: image-classification |
| --- |
| |
| <h1>vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification</h1> |
| |
| 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: 0.8584 |
| - Accuracy: 0.8198 |
| - Weighted f1: 0.7987 |
| - Micro f1: 0.8198 |
| - Macro f1: 0.8054 |
| - Weighted recall: 0.8198 |
| - Micro recall: 0.8198 |
| - Macro recall: 0.8149 |
| - Weighted precision: 0.8615 |
| - Micro precision: 0.8198 |
| - Macro precision: 0.8769 |
| |
| <div style="text-align: center;"> |
| <h2> |
| Model Description |
| </h2> |
| |
| <a href=“https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Vit%20-%20Image%20Classification.ipynb”> |
| Click here for the code that I used to create this model. |
| </a> |
| |
| This project is part of a comparison of seventeen (17) transformers. |
| |
| |
| <a href="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/README.md"> |
| Click here to see the README markdown file for the full project. |
| </a> |
| |
| <h2> |
| Intended Uses & Limitations |
| </h2> |
| This model is intended to demonstrate my ability to solve a complex problem using technology. |
| <br /> |
| |
| <h2> |
| Training & Evaluation Data |
| </h2> |
| <a href="https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri"> |
| Brain Tumor Image Classification Dataset |
| </a> |
| <h2> |
| Sample Images |
| </h2> |
| <img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Sample%20Images.png" /> |
| <h2> |
| Class Distribution of Training Dataset |
| </h2> |
| <img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Training%20Dataset.png"/> |
| <h2> |
| Class Distribution of Evaluation Dataset |
| </h2> |
| <img src="https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Brain%20Tumors%20Image%20Classification%20Comparison/Images/Class%20Distribution%20-%20Testing%20Dataset.png"/> |
| </div> |
| |
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | 1.3668 | 1.0 | 180 | 1.0736 | 0.6853 | 0.6524 | 0.6853 | 0.6428 | 0.6853 | 0.6853 | 0.6530 | 0.7637 | 0.6853 | 0.7866 | |
| | 1.3668 | 2.0 | 360 | 1.0249 | 0.7792 | 0.7335 | 0.7792 | 0.7411 | 0.7792 | 0.7792 | 0.7758 | 0.8391 | 0.7792 | 0.8528 | |
| | 0.1864 | 3.0 | 540 | 0.8584 | 0.8198 | 0.7987 | 0.8198 | 0.8054 | 0.8198 | 0.8198 | 0.8149 | 0.8615 | 0.8198 | 0.8769 | |
| |
| ### Framework versions |
| |
| - Transformers 4.28.1 |
| - Pytorch 2.0.0 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
| |
| |
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
| |
| |
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
