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- Followers 7
murshidul haqu
Kerala
Implementation of high-speed object detection by combination of edge terminal and VPU (YoloV3 · tiny-YoloV3)
YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO ...learn more
Project status: Under Development
Internet of Things, Artificial Intelligence
Groups
Internet of Things,
DeepLearning,
Movidius™ Neural Compute Group
Intel Technologies
OpenVINO,
Intel Opt ML/DL Framework,
Movidius NCS
Overview / Usage
- Environment
- LattePanda Alpha (Intel 7th Core m3-7y30) or LaptopPC (Intel 8th Core i7-8750H)
- Ubuntu 16.04 x86_64
- RaspberryPi3
- Raspbian Stretch armv7l
- OpenVINO toolkit 2018 R5 (2018.5.445)
- Python 3.5
- C++
- OpenCV 4.0.1-openvino
- Tensorflow v1.11.0 or Tensorflow-GPU v1.11.0 (pip install)
- YoloV3 (MS-COCO)
- tiny-YoloV3 (MS-COCO)
- USB Camera (PlaystationEye) / Movie file (mp4)
- Intel Neural Compute Stick v1 / v2
- Environment construction procedure
https://github.com/PINTO0309/OpenVINO-YoloV3#environment-construction-procedure
- How to check the graph structure of a ".pb" file
https://github.com/PINTO0309/OpenVINO-YoloV3#how-to-check-the-graph-structure-of-a-pb-file-part4
- Issue
How to offload OpenVINO non-compliant layer to Tensorflow (undefined symbol: _ZN15InferenceEngine10TensorDescC1Ev)
OpenVINO failing on YoloV3's YoloRegion, only one working on FP16, all working on FP32
- Future tasks
- Work that makes compatibility with Multiple models
Methodology / Approach
OpenVINO
Technologies Used
OpenVINO+YoloV3/tiny-YoloV3+NCS/NCS2
Repository
https://github.com/PINTO0309/OpenVINO-YoloV3
Other links
- [13 FPS] NCS2 x 4 + Full size YoloV3 performance has been tripled
- [24 FPS] Boost RaspberryPi3 with four Neural Compute Stick 2 (NCS2) MobileNet-SSD / YoloV3 [48 FPS for Core i7]
Collaborators