DeGirum/hailo_examples
Jul 2025 – Sep 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
AI IntegrationAPI IntegrationCode RefactoringComputer VisionData ProcessingData Transformation
franklin-degirum
PROFILE
Franklin-degirum
Over three months, Chris Franklin developed and refined advanced AI inference workflows in the DeGirum/hailo_examples repository, focusing on practical integration and deployment. He built end-to-end Jupyter Notebooks for object detection, license plate recognition, and video classification, leveraging Python, the DeGirum PySDK, and GStreamer. His work emphasized robust data flow between inference results and visualization tools, including the Supervision library, and addressed edge cases such as empty detections. By supporting multi-target inference across cloud, AI servers, and Hailo accelerators, Chris enabled flexible prototyping and streamlined onboarding for machine learning video workloads, demonstrating depth in computer vision and model deployment.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
14Total
Bugs
1
Commits
14
Features
4
Lines of code
1,275
Activity Months3
Your Network
11 peopleSame Organization
@degirum.com4
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Summary for 2025-09: Delivered an end-to-end video classification example using the R3D_18 model in the hailo_examples repository, featuring a notebook that demonstrates inference on a video file and a GStreamer pipeline for live streams. The work outlines multiple inference targets, including cloud, AI server, and Hailo accelerator, to support flexible deployment across edge and cloud environments. This initial baseline enables rapid prototyping, showcases cross-platform inference capabilities, and strengthens onboarding for ML video workloads.
1 Commits • 1 Features
Sep 1, 2025Summary for 2025-09: Delivered an end-to-end video classification example using the R3D_18 model in the hailo_examples repository, featuring a notebook that demonstrates inference on a video file and a GStreamer pipeline for live streams. The work outlines multiple inference targets, including cloud, AI server, and Hailo accelerator, to support flexible deployment across edge and cloud environments. This initial baseline enables rapid prototyping, showcases cross-platform inference capabilities, and strengthens onboarding for ML video workloads.
September 2025
August 2025
9 Commits • 2 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on business value and technical achievements in the DeGirum/hailo_examples repo. The month delivered two major feature improvements with strong security and QA hygiene, plus local-run readiness to accelerate prototyping and testing.
August 2025
9 Commits • 2 Features
Aug 1, 2025Concise monthly summary for 2025-08 focusing on business value and technical achievements in the DeGirum/hailo_examples repo. The month delivered two major feature improvements with strong security and QA hygiene, plus local-run readiness to accelerate prototyping and testing.
July 2025
4 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for DeGirum/hailo_examples: Delivered an end-to-end PySDK + Supervision integration notebook for Hailo hardware, enabling connection to inference hosts, running YOLO-based object detection, and visualizing predictions with streamlined data flow to Supervision annotations. Fixed robustness issues for empty detection results, gating annotations to actual detections, updated host addressing to local, and simplified internal data paths. Also refined the conversion path to return detections and labels, improving reliability of downstream supervision analytics. These efforts accelerate proof-of-concept evaluations, reduce runtime errors in demos, and demonstrate practical business value of integrated AI workflows.
4 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for DeGirum/hailo_examples: Delivered an end-to-end PySDK + Supervision integration notebook for Hailo hardware, enabling connection to inference hosts, running YOLO-based object detection, and visualizing predictions with streamlined data flow to Supervision annotations. Fixed robustness issues for empty detection results, gating annotations to actual detections, updated host addressing to local, and simplified internal data paths. Also refined the conversion path to return detections and labels, improving reliability of downstream supervision analytics. These efforts accelerate proof-of-concept evaluations, reduce runtime errors in demos, and demonstrate practical business value of integrated AI workflows.
July 2025
Activity
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Quality Metrics
Correctness94.4%
Maintainability94.4%
Architecture90.0%
Performance87.2%
AI Usage27.2%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AI InferenceAI IntegrationAPI IntegrationCode CleanupCode RefactoringComputer VisionData ProcessingData TransformationData VisualizationDeGirum PySDKDeep LearningDocumentationExample ConfigurationGStreamerInstance Segmentation
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