4.1 KiB
Tasks: ResNet Phenology Classifier
Input: Design documents from /specs/1-phenology-classifier/
Prerequisites: plan.md (required), spec.md (required for user stories), research.md, data-model.md, contracts/
Tests: Included as per constitution testing standards.
Organization: Tasks are grouped by user story to enable independent implementation and testing of each story.
Format: [ID] [P?] [Story] Description
- [P]: Can run in parallel (different files, no dependencies)
- [Story]: Which user story this task belongs to (e.g., US1, US2, US3)
- Include exact file paths in descriptions
Path Conventions
- Single project:
src/,tests/at repository root
Dependencies
- US1 (Train Model) must complete before US2 (Evaluate) and US3 (Classify)
- US2 and US3 can run in parallel after US1
Parallel Execution Examples
- Setup tasks: T001-T005 can run in parallel
- US1 tasks: T010-T015 can run in parallel except for training which depends on data loading
- US2 and US3: Can run in parallel after US1 completion
Implementation Strategy
- MVP: Complete US1 for basic training capability
- Incremental: Add US2 for evaluation, then US3 for inference
- Each user story delivers independently testable value
Phase 1: Setup (Shared Infrastructure)
Purpose: Project initialization and basic structure
- T001 Create project directory structure per plan.md
- T002 Create requirements.txt with PyTorch, torchvision, pandas, scikit-learn
- T003 Create data/ directory and subdirectories for images and labels
- T004 Create models/ directory for saved models
- T005 Create src/init.py and basic module structure
Phase 2: Foundational (Blocking Prerequisites)
Purpose: Core components needed by all user stories
- T006 [P] Implement data loader in src/data_loader.py for CSV labels and image loading
- T007 [P] Define ResNet50 model in src/model.py with classification head
- T008 [P] Create utils.py with preprocessing and helper functions
- T009 [P] Set up test framework with pytest configuration
- T010 [P] Create unit tests for data loader in tests/test_data_loader.py
Phase 3: US1 - Train ResNet Model
Purpose: Enable model training on labeled datasets Independent Test: Train on subset and verify model learns (accuracy improves)
- T011 [P] [US1] Implement training script in src/train.py with data loading and ResNet training loop
- T012 [P] [US1] Add model saving functionality to train.py
- T013 [P] [US1] Implement data augmentation in utils.py for training
- T014 [P] [US1] Create unit tests for training components in tests/test_train.py
- T015 [US1] Integrate training pipeline and test end-to-end training
Phase 4: US2 - Evaluate Model Performance
Purpose: Provide evaluation metrics for trained models Independent Test: Run evaluation on test set and verify metrics output
- T016 [P] [US2] Implement evaluation script in src/evaluate.py with accuracy and F1-score calculation
- T017 [P] [US2] Add per-class metrics and confusion matrix in evaluate.py
- T018 [P] [US2] Create unit tests for evaluation in tests/test_evaluate.py
- T019 [US2] Integrate evaluation and test on trained model
Phase 5: US3 - Classify New Images
Purpose: Enable inference on new plant images Independent Test: Classify sample image and verify output format
- T020 [P] [US3] Implement inference script in src/inference.py for single image classification
- T021 [P] [US3] Create API endpoint in src/api.py using FastAPI for /classify POST
- T022 [P] [US3] Add input validation and error handling in api.py
- T023 [P] [US3] Create unit tests for inference in tests/test_inference.py
- T024 [US3] Integrate API and test classification endpoint
Final Phase: Polish & Cross-Cutting Concerns
Purpose: Quality assurance and production readiness
- T025 Add logging and monitoring to all scripts
- T026 Implement CI/CD pipeline with linting and testing
- T027 Add comprehensive documentation and README updates
- T028 Performance optimization and memory management
- T029 Final integration testing and validation