| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: efficientnet-b5-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: 0.8020304568527918 |
| | --- |
| | |
| | <h1>efficientnet-b5-Brain_Tumors_Image_Classification</h1> |
| | |
| | This model is a fine-tuned version of [google/efficientnet-b5](https://huggingface.co/google/efficientnet-b5). |
| | |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.9410 |
| | - Accuracy: 0.8020 |
| | - F1 |
| | - Weighted: 0.7736 |
| | - Micro: 0.8020 |
| | - Macro: 0.7802 |
| | - Recall |
| | - Weighted: 0.8020 |
| | - Micro: 0.8020 |
| | - Macro: 0.7977 |
| | - Precision |
| | - Weighted: 0.8535 |
| | - Micro: 0.8020 |
| | - Macro: 0.8682 |
| | |
| | <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/EfficientNet%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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
| | | 1.3872 | 1.0 | 180 | 1.0601 | 0.6853 | 0.6485 | 0.6853 | 0.6550 | 0.6853 | 0.6853 | 0.6802 | 0.8177 | 0.6853 | 0.8330 | |
| | | 1.3872 | 2.0 | 360 | 0.9533 | 0.7843 | 0.7483 | 0.7843 | 0.7548 | 0.7843 | 0.7843 | 0.7819 | 0.8354 | 0.7843 | 0.8471 | |
| | | 0.8186 | 3.0 | 540 | 0.9410 | 0.8020 | 0.7736 | 0.8020 | 0.7802 | 0.8020 | 0.8020 | 0.7977 | 0.8535 | 0.8020 | 0.8682 | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.1 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.11.0 |
| | - Tokenizers 0.13.3 |
| | |
