ageitgey / ageitgey/face_recognition
Jetson Nano Performance
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- Dominant language
- Python
- Stars
- 56.8k
- Forks
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Description
I tried the doorbell_camera.py and I get really bad performance, like 6fps. Is this normal? I was looking at jtop (https://github.com/rbonghi/jetson_stats) and I barely see this thing hit any type of limit. The biggest thing is I see is it pegging an ARM core at 98-100% and the 3 other cores at between 8-20%, other than that its barely touching any of the real power of the jetson nano. Mainly the program is spending it's time at https://gist.github.com/ageitgey/84943a12dd0d9f54e90f824b94e4c2a9#file-doorbell_camera-py-L137
Which is where it populates the face locations in the current frame, could that not be done with a bit more performance using CUDA?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by inspecting doorbell_camera.py around line 137, where face locations are populated, and compare its behavior with the Jetson Nano metrics shown through jtop. Determine whether the reported 6fps is expected and whether the face-location step has a documented CUDA-capable path; completion criteria are not defined by the issue.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100