# 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)

```text
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)

```text
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