88 lines
3.5 KiB
Markdown
88 lines
3.5 KiB
Markdown
# Implementation Plan: ResNet Phenology Classifier
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**Branch**: `1-phenology-classifier` | **Date**: 2025-11-04 | **Spec**: specs/1-phenology-classifier/spec.md
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**Input**: Feature specification from `/specs/1-phenology-classifier/spec.md`
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**Note**: This template is filled in by the `/speckit.plan` command. See `.specify/templates/commands/plan.md` for the execution workflow.
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## Summary
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Build a ResNet model to classify plant images by phenological phase using labeled datasets. Technical approach: Use PyTorch to implement ResNet architecture, train on image dataset with CSV labels, evaluate performance metrics.
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## Technical Context
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**Language/Version**: Python 3.11
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**Primary Dependencies**: PyTorch, torchvision, pandas, scikit-learn
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**Storage**: Image files (JPEG/PNG) and CSV labels
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**Testing**: pytest, torch.testing
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**Target Platform**: Linux server with GPU support
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**Project Type**: single (ML model training and inference script)
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**Performance Goals**: >90% classification accuracy, <1 second inference time
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**Constraints**: GPU memory availability, dataset size up to 10k images
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**Scale/Scope**: Support for multiple phenological phases as defined in CSV
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## Constitution Check
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*GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.*
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- Code Quality and Modularity: Confirm adherence to PEP 8, type hints, docstrings, and modular design.
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- Rigorous Testing Standards: Plan includes unit tests, integration tests, and model validation tests.
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- Reproducibility and Versioning: Versioning strategy for code, data, and models; random seed management.
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- Model Evaluation and Validation: Metrics and bias checks defined for model assessment.
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- Continuous Integration and Quality Gates: CI/CD pipeline with linting, testing, and performance checks.
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## Project Structure
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### Documentation (this feature)
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```text
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specs/1-phenology-classifier/
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├── plan.md # This file (/speckit.plan command output)
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├── research.md # Phase 0 output (/speckit.plan command)
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├── data-model.md # Phase 1 output (/speckit.plan command)
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├── quickstart.md # Phase 1 output (/speckit.plan command)
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├── contracts/ # Phase 1 output (/speckit.plan command)
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└── tasks.md # Phase 2 output (/speckit.tasks command - NOT created by /speckit.plan)
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```
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### Source Code (repository root)
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```text
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src/
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├── __init__.py
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├── data_loader.py # Load images and labels from CSV
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├── model.py # ResNet model definition
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├── train.py # Training script
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├── evaluate.py # Evaluation script
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├── inference.py # Inference on new images
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└── utils.py # Helper functions
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tests/
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├── test_data_loader.py
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├── test_model.py
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├── test_train.py
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└── test_inference.py
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data/
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├── images/ # Plant images
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└── labels.csv # Image labels
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models/ # Saved trained models
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requirements.txt # Python dependencies
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```
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### Phase Execution
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**Phase 0: Research** (resolve unknowns)
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- Research best ResNet variant for image classification
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- Research data preprocessing for plant images
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- Research evaluation metrics for multi-class classification
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**Phase 1: Design** (create contracts)
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- Define data model for images and labels
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- Design API for model inference
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- Create quickstart guide
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**Phase 2: Tasks** (implementation breakdown)
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- Break down into specific coding tasks
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- Assign priorities and dependencies |