- Move test scripts to tests/ directory for better organization
- Add improved YOLOv5 postprocessing with reference implementation
- Update gitignore to exclude *.mflow files and include main.spec
- Add debug capabilities and coordinate scaling improvements
- Enhance multi-series support with proper validation
- Add AGENTS.md documentation and example utilities
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Implement PostProcessorOptions system with built-in postprocessing types (fire detection, YOLO v3/v5, classification, raw output)
- Add fire detection as default option maintaining backward compatibility
- Support YOLO v3/v5 object detection with bounding box visualization in live view windows
- Integrate text output with confidence scores and visual indicators for all postprocess types
- Update exact nodes postprocess_node.py to configure postprocessing through UI properties
- Add comprehensive example demonstrating all available postprocessing options and usage patterns
- Enhance WebcamInferenceRunner with dynamic visualization based on result types
Technical improvements:
- Created PostProcessType enum and PostProcessorOptions configuration class
- Built-in postprocessing eliminates external dependencies on Kneron Default examples
- Added BoundingBox, ObjectDetectionResult, and ClassificationResult data structures
- Enhanced live view with color-coded confidence bars and object detection overlays
- Integrated postprocessing options into MultiDongle constructor and exact nodes system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Key improvements:
- Add timeout mechanism (2s) for result ordering to prevent slow devices from blocking pipeline
- Implement performance-biased load balancing with 2x penalty for low-GOPS devices (< 10 GOPS)
- Adjust KL520 GOPS from 3 to 2 for more accurate performance representation
- Remove KL540 references to focus on available hardware
- Add intelligent sequence skipping with timeout results for better throughput
This resolves the issue where multi-series mode had lower FPS than single KL720
due to KL520 devices creating bottlenecks in the result ordering queue.
Performance impact:
- Reduces KL520 task allocation from ~12.5% to ~5-8%
- Prevents pipeline stalls from slow inference results
- Maintains result ordering integrity with timeout fallback
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix mflow_converter to properly handle multi-series configuration creation
- Update InferencePipeline to correctly initialize MultiDongle with multi-series config
- Add comprehensive multi-series configuration validation in mflow_converter
- Enhance deployment dialog to display multi-series configuration details
- Improve analysis and configuration tabs to show proper multi-series info
This resolves the issue where multi-series mode was falling back to single-series
during inference initialization, ensuring proper multi-series dongle support.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
