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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: efficientnet-b5-Brain_Tumors_Image_Classification |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8020304568527918 |
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--- |
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<h1>efficientnet-b5-Brain_Tumors_Image_Classification</h1> |
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This model is a fine-tuned version of [google/efficientnet-b5](https://huggingface.co/google/efficientnet-b5). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9410 |
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- Accuracy: 0.8020 |
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- F1 |
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- Weighted: 0.7736 |
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- Micro: 0.8020 |
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- Macro: 0.7802 |
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- Recall |
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- Weighted: 0.8020 |
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- Micro: 0.8020 |
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- Macro: 0.7977 |
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- Precision |
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- Weighted: 0.8535 |
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- Micro: 0.8020 |
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- Macro: 0.8682 |
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<div style="text-align: center;"> |
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<h2> |
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Model Description |
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</h2> |
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<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"> |
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Click here for the code that I used to create this model. |
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</a> |
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This project is part of a comparison of seventeen (17) transformers. |
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<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"> |
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Click here to see the README markdown file for the full project |
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</a> |
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<h2> |
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Intended Uses & Limitations |
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</h2> |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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<h2> |
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Training & Evaluation Data |
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</h2> |
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<a href="https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri"> |
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Brain Tumor Image Classification Dataset |
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</a> |
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<h2> |
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Sample Images |
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</h2> |
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<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" /> |
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<h2> |
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Class Distribution of Training Dataset |
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</h2> |
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<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"/> |
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<h2> |
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Class Distribution of Evaluation Dataset |
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</h2> |
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<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"/> |
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</div> |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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