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--- |
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license: bigcode-openrail-m |
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language: |
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- en |
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- fr |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- biology |
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- code |
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- tensorflow |
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--- |
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# Mushroom Classification Model - JarviSpore |
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This repository contains **JarviSpore**, a mushroom image classification model trained on a multi-class dataset with 23 different types of mushrooms. Developed from scratch with TensorFlow and Keras, this model aims to provide accurate mushroom identification using advanced deep learning techniques, including *Grad-CAM* for interpreting predictions. This project explores the performance of from-scratch models compared to transfer learning. |
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## Model Details |
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- **Architecture**: Custom CNN (Convolutional Neural Network) |
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- **Number of Classes**: 23 mushroom classes |
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- **Input Format**: RGB images resized to 224x224 pixels |
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- **Framework**: TensorFlow & Keras |
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- **Training**: Conducted on a machine with an i9 14900k processor, 192GB RAM, and an RTX 3090 GPU |
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## Key Features |
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1. **Multi-Class Classification**: The model can predict among 23 mushroom species. |
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2. **Regularization**: Includes L2 regularization and Dropout to prevent overfitting. |
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3. **Class Weighting**: Manages dataset imbalances by applying specific weights for each class. |
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4. **Grad-CAM Visualization**: Utilizes Grad-CAM to generate heatmaps, allowing visualization of the regions influencing the model's predictions. |
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## Model Training |
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The model was trained using a structured dataset directory with data split as follows: |
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- train: Balanced training dataset |
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- validation: Validation set to monitor performance |
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- test: Test set to evaluate final accuracy |
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Main training hyperparameters include: |
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- **Batch Size**: 32 |
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- **Epochs**: 20 with Early Stopping |
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- **Learning Rate**: 0.0001 |
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Training was tracked and logged via MLflow, including accuracy and loss curves, as well as the best model weights saved automatically. |
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## Model Usage |
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### Prerequisites |
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Ensure the following libraries are installed: |
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bash |
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pip install tensorflow pillow matplotlib numpy |
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### Loading the Model |
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To load and use the model for predictions: |
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python |
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import tensorflow as tf |
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from PIL import Image |
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import numpy as np |
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# Load the model |
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model = tf.keras.models.load_model("path_to_model.h5") |
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# Prepare an image for prediction |
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def prepare_image(image_path): |
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img = Image.open(image_path).convert("RGB") |
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img = img.resize((224, 224)) |
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img_array = tf.keras.preprocessing.image.img_to_array(img) |
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img_array = np.expand_dims(img_array, axis=0) |
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return img_array |
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# Prediction |
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image_path = "path_to_image.jpg" |
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img_array = prepare_image(image_path) |
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predictions = model.predict(img_array) |
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predicted_class = np.argmax(predictions[0]) |
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print(f"Predicted Class: {predicted_class}") |
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### Grad-CAM Visualization |
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The integrated *Grad-CAM* functionality allows interpretation of the model's predictions. To use it, select an image and apply the Grad-CAM function to display the heatmap overlaid on the original image, highlighting areas influencing the model. |
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Grad-CAM example usage: |
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python |
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# Example usage of the make_gradcam_heatmap function |
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heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer_name") |
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# Superimpose the heatmap on the original image |
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superimposed_img = superimpose_heatmap(Image.open(image_path), heatmap) |
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superimposed_img.show() |
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## Evaluation |
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The model was evaluated on the test set with an average accuracy above random chance, showing promising results for a first from-scratch version. |
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## Contributing |
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Contributions to improve accuracy or add new features (e.g., other visualization techniques or advanced optimization) are welcome. Please submit a pull request with relevant modifications. |
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## License |
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This model is licensed under a controlled license: please refer to the LICENSE file for details. You may use this model for personal projects, but any modifications or redistribution must be approved. |
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