| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: regnet-y-064-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: 0.8045685279187818 |
| | --- |
| | |
| | <h1>regnet-y-064-Brain_Tumors_Image_Classification</h1> |
| | |
| | This model is a fine-tuned version of [facebook/regnet-y-064](https://huggingface.co/facebook/regnet-y-064). |
| | |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1561 |
| | - Accuracy: 0.8046 |
| | - Weighted f1: 0.7776 |
| | - Micro f1: 0.8046 |
| | - Macro f1: 0.7839 |
| | - Weighted recall: 0.8046 |
| | - Micro recall: 0.8046 |
| | - Macro recall: 0.7978 |
| | - Weighted precision: 0.8574 |
| | - Micro precision: 0.8046 |
| | - Macro precision: 0.8736 |
| | |
| | <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/RegNet%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. |
| | |
| | |
| | <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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | | 1.288 | 1.0 | 180 | 1.3796 | 0.6548 | 0.5991 | 0.6548 | 0.5868 | 0.6548 | 0.6548 | 0.6176 | 0.8046 | 0.6548 | 0.8285 | |
| | | 1.288 | 2.0 | 360 | 1.0964 | 0.7944 | 0.7687 | 0.7944 | 0.7755 | 0.7944 | 0.7944 | 0.7872 | 0.8555 | 0.7944 | 0.8727 | |
| | | 0.1498 | 3.0 | 540 | 1.1561 | 0.8046 | 0.7776 | 0.8046 | 0.7839 | 0.8046 | 0.8046 | 0.7978 | 0.8574 | 0.8046 | 0.8736 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.1 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.11.0 |
| | - Tokenizers 0.13.3 |
| | |
