peng-zhihui/deepvision — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2021-11-09
Test a newly trained object detection model on an Android phone without writing a custom test app.
Compare your own model's output against model-zoo baselines like YOLO or Openpose on-device.
Prototype a robotics or vision project by capturing live camera feed and running inference in real time.
Compress and optimize a computer vision model so it runs within a phone's battery and memory limits.
| peng-zhihui/deepvision | snailclimb/interview-guide | zhisheng17/blog | |
|---|---|---|---|
| Stars | 1,944 | 2,116 | 1,646 |
| Language | Java | Java | Java |
| Last pushed | 2021-11-09 | — | 2022-10-05 |
| Maintenance | Dormant | — | Dormant |
| Setup difficulty | moderate | hard | moderate |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires an Android device or emulator plus a pre-trained model file to test against.
A Java framework that lets computer vision engineers test their trained models directly on phones and tablets without building a custom app each time.
Mainly Java. The stack also includes Java, OpenCV, TensorFlow Lite.
Dormant — no commits in 2+ years (last push 2021-11-09).
No license information was mentioned in the explanation.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Don't trust strangers blindly. Verify against the repo.