[WIP] Convert tomato_classifier.py to Jupyter notebook format

[Copilot]

• Understand the repository structure and tomato_classifier.py file
• Create Jupyter notebook tomato_classifier_notebook.ipynb with all required sections:
  • Cell 1: Setup & Imports
  • Cell 2: Configuration
  • Cell 3: Custom Layers (ClipLayer, DenseNetPreprocessLayer)
  • Cell 4: Utility Functions
  • Cell 5: Data Classes
  • Cell 6: Image Validation Functions
  • Cell 7: Dataset Functions
  • Cell 8: Model Building
  • Cell 9: Training Functions
  • Cell 10: Metrics Computation
  • Cell 11: Visualization Functions (Part 1)
  • Cell 12: Visualization Functions (Part 2)
  • Cell 13: OOD Detection & Model Conversion
  • Cell 14: Report Generation
  • Cell 15: Training Pipeline (without main() - direct execution)
  • Cell 16: Run Training (execute with configuration)
• Add markdown cells with clear section headers and explanations
• Include Quick Start section at the top
• Ensure notebook works on Kaggle with auto-detection
• Validate notebook structure
• Run code review

Original prompt

Overview

Create a Jupyter notebook version of the tomato_classifier.py file that can be run block-by-block in notebook environments like Kaggle, Google Colab, or local Jupyter.

Requirements

Structure

Convert the existing tomato_classifier.py into a well-organized notebook (tomato_classifier_notebook.ipynb) with the following sections:

1. Cell 1: Setup & Imports

   • All imports and library installations
   • GPU/environment detection
   • Random seed setting

2. Cell 2: Configuration

   • All configurable parameters (IMG_SIZE, BATCH_SIZE, EPOCHS, etc.)
   • Dataset path configuration with auto-detection for Kaggle paths
   • User-modifiable settings

3. Cell 3: Custom Layers

   • ClipLayer and DenseNetPreprocessLayer classes
   • Register serializable decorators

4. Cell 4: Utility Functions

   • format_time, get_gpu_memory_info, setup_mixed_precision
   • create_output_directories, check_dataset_path

5. Cell 5: Data Classes

   • DatasetMetrics and TrainingHistory dataclasses

6. Cell 6: Image Validation Functions

   • has_valid_image_header, is_valid_image_file
   • VALID_IMAGE_EXTENSIONS

7. Cell 7: Dataset Functions

   • get_image_files_and_labels
   • process_path, create_dataset_from_paths
   • create_datasets, get_class_weights

8. Cell 8: Model Building

   • build_model function with DenseNet121

9. Cell 9: Training Functions

   • LearningRateLogger callback
   • train_model function

10. Cell 10: Metrics Computation

    • compute_comprehensive_metrics
    • get_confusion_details

11. Cell 11: Visualization Functions (Part 1)

    • Training history plots
    • Confusion matrix plots
    • ROC and PR curves

12. Cell 12: Visualization Functions (Part 2)

    • Per-class metrics plots
    • Dataset comparison plots
    • Confidence distribution and calibration curves

13. Cell 13: OOD Detection & Model Conversion

    • analyze_ood_performance
    • convert_to_tflite

14. Cell 14: Report Generation

    • All save_csv and save_txt functions

15. Cell 15: Main Pipeline

    • main() function

16. Cell 16: Run Training

    • Execute the training with user configuration
    • Example: run(epochs=20, epochs_ft=50, dry_run=False)

Additional Requirements

• Add markdown cells with clear section headers and explanations
• Include comments explaining what each cell does
• Add a "Quick Start" section at the top explaining how to use the notebook
• Ensure all cells can be run independently after running the setup cells
• Include example output comments where helpful
• Make sure the notebook works on Kaggle (auto-detect dataset paths)

Reference

The source code is in tomato_classifier.py at commit 14b7aa3.

This pull request was created as a result of the following prompt from Copilot chat.

Overview

Create a Jupyter notebook version of the tomato_classifier.py file that can be run block-by-block in notebook environments like Kaggle, Google Colab, or local Jupyter.

Requirements

Structure

Convert the existing tomato_classifier.py into a well-organized notebook (tomato_classifier_notebook.ipynb) with the following sections:

1. Cell 1: Setup & Imports

   • All imports and library installations
   • GPU/environment detection
   • Random seed setting

2. Cell 2: Configuration

   • All configurable parameters (IMG_SIZE, BATCH_SIZE, EPOCHS, etc.)
   • Dataset path configuration with auto-detection for Kaggle paths
   • User-modifiable settings

3. Cell 3: Custom Layers

   • ClipLayer and DenseNetPreprocessLayer classes
   • Register serializable decorators

4. Cell 4: Utility Functions

   • format_time, get_gpu_memory_info, setup_mixed_precision
   • create_output_directories, check_dataset_path

5. Cell 5: Data Classes

   • DatasetMetrics and TrainingHistory dataclasses

6. Cell 6: Image Validation Functions

   • has_valid_image_header, is_valid_image_file
   • VALID_IMAGE_EXTENSIONS

7. Cell 7: Dataset Functions

   • get_image_files_and_labels
   • process_path, create_dataset_from_paths
   • create_datasets, get_class_weights

8. Cell 8: Model Building

   • build_model function with DenseNet121

9. Cell 9: Training Functions

   • LearningRateLogger callback
   • train_model function

10. Cell 10: Metrics Computation

    • compute_comprehensive_metrics
    • get_confusion_details

11. Cell 11: Visualization Functions (Part 1)

    • Training history plots
    • Confusion matrix plots
    • ROC and PR curves

12. Cell 12: Visualization Functions (Part 2)

    • Per-class metrics plots
    • Dataset comparison plots
    • Confidence distribution and calibration curves

13. Cell 13: OOD Detection & Model Conversion

    • analyze_ood_performance
    • convert_to_tflite

14. Cell 14: Report Generation

    • All save_csv and save_txt functions

15. Cell 15: Main Pipeline

    • main() function

16. Cell 16: Run Training

    • Execute the training with user configuration
    • Example: run(epochs=20, epochs_ft=50, dry_run=False)

Additional Requirements

• Add markdown cells with clear section headers and explanations
• Include comments explaining what each cell does
• Add a "Quick Start" section at the top explaining how to use the notebook
• Ensure all cells can be run independently after running the setup cells
• Include example output comments where helpful
• Make sure the notebook works on Kaggle (auto-detect dataset paths)

Reference

The source code is in tomato_classifier.py at commit 14b7aa3.

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