visdet Roadmap¶

This roadmap outlines the planned development trajectory for visdet based on comprehensive analysis of the current codebase, identified gaps, and future goals.

1. Component Benchmarking System¶

Phase 1: Core Benchmarking Framework (Immediate Priority)¶

[ ] Implement component-level benchmarking system
[ ] Create standardized benchmarking protocols for all neural network components
[ ] Implement instrumentation for measuring forward/backward pass times
[ ] Develop memory usage tracking for each component
[ ] Build FLOPS and parameter counting utilities
[ ] Create JSON export functionality for benchmark results
[ ] Benchmark core components
[ ] Backbone networks (ResNet, Swin, etc.)
[ ] Necks (FPN, PAN, etc.)
[ ] Detection/segmentation heads
[ ] ROI extractors and attention mechanisms
[ ] Post-processing operations (NMS variants)
[ ] Develop visualization and reporting tools
[ ] Interactive dashboard for benchmark results
[ ] Comparative analysis between component variants
[ ] Historical performance tracking

Phase 2: Advanced Benchmarking (Short-Term)¶

[ ] Extend benchmarking to specialized components
[ ] Activation functions
[ ] Normalization layers
[ ] Loss functions
[ ] Custom CUDA vs. PyTorch implementations
[ ] Implement system-level benchmarks
[ ] End-to-end model training throughput
[ ] Inference latency at different batch sizes
[ ] GPU memory utilization over time
[ ] CPU/GPU workload balance analysis
[ ] Create CI/CD integration
[ ] Automated benchmarking for PRs
[ ] Performance regression detection
[ ] Hardware-normalized comparisons

Phase 3: Production Benchmarking (Medium-Term)¶

[ ] Develop deployment target benchmarking
[ ] Cloud inference performance (various instance types)
[ ] Edge device benchmarks (mobile GPUs, embedded systems)
[ ] CPU-only performance profiling
[ ] Add benchmark-driven optimization suggestions
[ ] Automated bottleneck identification
[ ] Component replacement recommendations
[ ] Model architecture optimization hints
[ ] Create public benchmark database
[ ] Comparative benchmarks across hardware
[ ] Standard test suites for reproducibility
[ ] API for querying benchmark data

2. Test Coverage & Core Stability¶

Phase 1: Critical Test Migration (Immediate Priority)¶

[ ] Migrate core detector tests from MMDetection
[ ] test_models/test_detectors/test_faster_rcnn.py (Core architecture)
[ ] test_models/test_roi_heads/test_standard_roi_head.py (RPN & classification)
[ ] test_models/test_dense_heads/test_anchor_head.py (Base architecture)
[ ] test_data/test_datasets/test_coco_dataset.py (Data correctness)
[ ] test_utils/test_nms.py (Post-processing critical op)
[ ] test_utils/test_anchor.py (Anchor generation)
[ ] test_runtime/test_apis.py (Public API validation)
[ ] Integrate codediff into CI/CD for test coverage tracking
[ ] Implement critical data pipeline tests for training stability
[ ] Create test documentation for future test contributors

Phase 2: Core Feature Parity (Short-Term)¶

[ ] Complete namespace refactoring (visdet.cv and visdet.engine)
[ ] Migrate remaining backbone tests
[ ] Add ROI Head variant tests
[ ] Implement data augmentation pipeline tests
[ ] Develop utility/helper function tests
[ ] Add ONNX export validation tests

Phase 3: Complete Test Coverage (Medium-Term)¶

[ ] Migrate all remaining dense head implementation tests
[ ] Implement data loading edge case tests
[ ] Add config file compatibility tests
[ ] Develop tracking integration tests
[ ] Implement downstream use case validation tests

2. Modern Training Features¶

Phase 1: Core Training Improvements (Short-Term)¶

[ ] Implement progressive image resizing training
[ ] Configurable multi-stage training with increasing resolution
[ ] Auto-detection of optimal starting size
[ ] Memory monitoring and adaptation
[ ] Develop learning rate finder
[ ] Integration with SimpleRunner API
[ ] Automated hyperparameter recommendation
[ ] Visual reporting of LR exploration
[ ] Implement discriminative learning rates
[ ] Layer-wise rate configuration
[ ] Backbone/head separate optimization
[ ] Integration with all optimizer types

Phase 2: Advanced Training Features (Medium-Term)¶

[ ] Implement 1cycle learning rate schedules
[ ] Automated schedule creation
[ ] Visual debugging tools
[ ] Integration with all optimizer types
[ ] Develop modern fine-tuning techniques
[ ] LoRA-style parameter-efficient fine-tuning
[ ] Adaptation for object detection
[ ] Performance benchmarks vs. full fine-tuning
[ ] Add auto-augmentation capabilities
[ ] Policy search for optimal augmentations
[ ] Domain-specific augmentation strategies
[ ] Performance impact analysis

3. Data Pipeline Optimization¶

Phase 1: Core Pipeline Improvements (Short-Term)¶

[ ] Integrate Kornia for differentiable augmentations
[ ] Replace non-differentiable operations
[ ] End-to-end gradient flow for data pipeline
[ ] Performance benchmarking vs. current approach
[ ] Implement efficient data loading enhancements
[ ] Memory mapping for large datasets
[ ] Prefetching optimizations
[ ] Memory usage monitoring and reporting
[ ] Develop better data visualization tools
[ ] Interactive pipeline debugging
[ ] Augmentation inspection utilities
[ ] Dataset statistics and quality metrics

Phase 2: Advanced Data Features (Medium-Term)¶

[ ] Evaluate and implement GPU-accelerated data processing
[ ] DALI integration feasibility assessment
[ ] Performance benchmarking vs. CPU processing
[ ] Mixed CPU/GPU pipeline optimization
[ ] Implement data quality assurance tools
[ ] Anomaly detection in training data
[ ] Annotation quality assessment
[ ] Dataset bias detection and reporting
[ ] Add streaming dataset support
[ ] On-the-fly downloading capabilities
[ ] Cloud storage integration (S3, GCS)
[ ] Caching and versioning strategies

4. YAML Configuration System¶

Phase 1: Complete Implementation (Short-Term)¶

[ ] Develop Python-to-YAML migration tool
[ ] Create Pydantic schemas for all core components
[ ] Add config visualization and dependency graphs
[ ] Implement IDE support (autocomplete, validation)
[ ] Develop config inheritance from remote URLs

Phase 2: Advanced Configuration (Medium-Term)¶

[ ] Create visual configuration editor
[ ] Implement experiment tracking integration
[ ] Develop parameter sensitivity analysis tools
[ ] Add configuration recommendation system
[ ] Build configuration version control

5. Integration with Modern Libraries¶

Phase 1: Core Integrations (Medium-Term)¶

[ ] Triton integration for custom operators
[ ] Feasibility assessment
[ ] Initial implementation for key operators
[ ] Performance benchmarking
[ ] Evaluate and implement SPDL for data loading
[ ] Performance testing
[ ] Integration with existing pipeline
[ ] Observability enhancements
[ ] Modal integration for cloud training
[ ] Proof of concept
[ ] Documentation and examples
[ ] Performance evaluation

Phase 2: Advanced Integrations (Long-Term)¶

[ ] Tutel integration for Mixture of Experts
[ ] Architecture exploration
[ ] Performance testing
[ ] Training recipes and documentation
[ ] DeepSpeed integration for large model training
[ ] ZeRO optimizer implementation
[ ] Distributed training capabilities
[ ] Model compression techniques

6. Documentation & User Experience¶

Phase 1: Core Documentation (Short-Term)¶

[ ] Update SimpleRunner documentation with examples
[ ] Create comprehensive YAML configuration guide
[ ] Develop migration guide from MMDetection
[ ] Add more tutorials for common tasks
[ ] Document modern training approaches

Phase 2: Advanced Documentation (Medium-Term)¶

[ ] Create interactive notebook tutorials
[ ] Develop performance tuning guides
[ ] Add advanced configuration recipes
[ ] Create model debugging guides
[ ] Document custom model development

7. Performance Optimization¶

Phase 1: Core Optimizations (Medium-Term)¶

[ ] Implement memory optimization techniques
[ ] Gradient checkpointing
[ ] Precision control
[ ] Memory profiling tools
[ ] Add training throughput improvements
[ ] Better batch size optimization
[ ] Pipeline parallelism options
[ ] Distributed training enhancements
[ ] Develop inference optimization capabilities
[ ] Model quantization
[ ] Pruning and compression
[ ] Batch inference optimization

Phase 2: Advanced Performance Features (Long-Term)¶

[ ] Implement model distillation framework
[ ] Add advanced compression techniques
[ ] Develop hardware-specific optimizations
[ ] Create automated performance tuning tools
[ ] Implement low-resource training capabilities

Timeline and Prioritization¶

Immediate Focus (0-3 months)¶

Core benchmarking framework implementation
Critical test migration (Phase 1)
Core namespace refactoring completion
Core training improvements (LR finder, discriminative rates)
YAML configuration system completion

Short-Term (3-6 months)¶

Progressive image resizing implementation
Kornia integration for differentiable augmentations
Complete test coverage (Phase 2)
Core documentation updates

Medium-Term (6-12 months)¶

Advanced training features (1cycle, LoRA fine-tuning)
Data pipeline optimization
Initial modern library integrations
Performance optimization techniques

Long-Term (12+ months)¶

Advanced integrations (Tutel, DeepSpeed)
Advanced performance features
Complete test parity with MMDetection
Advanced configuration and tooling

Conclusion¶

This roadmap prioritizes:

Component benchmarking - Creating a comprehensive system to measure and optimize performance of every neural network component
Stability through test coverage - Ensuring visdet maintains feature parity with MMDetection while allowing safe evolution
Modern training techniques - Bringing proven approaches from image classification and LLMs to object detection
Developer experience - Making visdet more accessible through better configuration, documentation, and interfaces
Performance optimization - Ensuring visdet can scale from small experiments to production workloads

The roadmap is designed to be modular, allowing parallel work on different components while maintaining a clear sense of priority. Key early milestones focus on test coverage and core stability, providing a solid foundation for more innovative features in later phases.