Implementation Plan: ResNet Phenology Classifier

Branch: 1-phenology-classifier | Date: 2025-11-04 | Spec: specs/1-phenology-classifier/spec.md Input: Feature specification from /specs/1-phenology-classifier/spec.md

Note: This template is filled in by the /speckit.plan command. See .specify/templates/commands/plan.md for the execution workflow.

Summary

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.

Technical Context

Language/Version: Python 3.11
Primary Dependencies: PyTorch, torchvision, pandas, scikit-learn
Storage: Image files (JPEG/PNG) and CSV labels
Testing: pytest, torch.testing
Target Platform: Linux server with GPU support Project Type: single (ML model training and inference script)
Performance Goals: >90% classification accuracy, <1 second inference time
Constraints: GPU memory availability, dataset size up to 10k images
Scale/Scope: Support for multiple phenological phases as defined in CSV

Constitution Check

GATE: Must pass before Phase 0 research. Re-check after Phase 1 design.

Code Quality and Modularity: Confirm adherence to PEP 8, type hints, docstrings, and modular design.
Rigorous Testing Standards: Plan includes unit tests, integration tests, and model validation tests.
Reproducibility and Versioning: Versioning strategy for code, data, and models; random seed management.
Model Evaluation and Validation: Metrics and bias checks defined for model assessment.
Continuous Integration and Quality Gates: CI/CD pipeline with linting, testing, and performance checks.

Project Structure

Documentation (this feature)

specs/1-phenology-classifier/
├── plan.md              # This file (/speckit.plan command output)
├── research.md          # Phase 0 output (/speckit.plan command)
├── data-model.md        # Phase 1 output (/speckit.plan command)
├── quickstart.md        # Phase 1 output (/speckit.plan command)
├── contracts/           # Phase 1 output (/speckit.plan command)
└── tasks.md             # Phase 2 output (/speckit.tasks command - NOT created by /speckit.plan)

Source Code (repository root)

src/
├── __init__.py
├── data_loader.py       # Load images and labels from CSV
├── model.py             # ResNet model definition
├── train.py             # Training script
├── evaluate.py          # Evaluation script
├── inference.py         # Inference on new images
└── utils.py             # Helper functions

tests/
├── test_data_loader.py
├── test_model.py
├── test_train.py
└── test_inference.py

data/
├── images/              # Plant images
└── labels.csv           # Image labels

models/                  # Saved trained models
requirements.txt         # Python dependencies

Phase Execution

Phase 0: Research (resolve unknowns)

Research best ResNet variant for image classification
Research data preprocessing for plant images
Research evaluation metrics for multi-class classification

Phase 1: Design (create contracts)

Define data model for images and labels
Design API for model inference
Create quickstart guide

Phase 2: Tasks (implementation breakdown)

Break down into specific coding tasks
Assign priorities and dependencies

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