https://github.com/qengineering/centerface-ncnn-raspberry-pi-4
CenterFace face detection with ncnn for Raspberry Pi 4
https://github.com/qengineering/centerface-ncnn-raspberry-pi-4
aarch64 centerface deep-learning face-detection ncnn ncnn-model raspberry-pi-4 raspberry-pi-64-os
Last synced: 3 months ago
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CenterFace face detection with ncnn for Raspberry Pi 4
- Host: GitHub
- URL: https://github.com/qengineering/centerface-ncnn-raspberry-pi-4
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2021-12-10T12:03:32.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-30T16:27:38.000Z (over 2 years ago)
- Last Synced: 2025-01-26T03:45:47.760Z (5 months ago)
- Topics: aarch64, centerface, deep-learning, face-detection, ncnn, ncnn-model, raspberry-pi-4, raspberry-pi-64-os
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 16.8 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CenterFace face detection Raspberry Pi 4
## CenterFace face detection with the ncnn framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://arxiv.org/ftp/arxiv/papers/1911/1911.03599.pdf
Special made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)
------------
## Benchmark.
| Model | framework | model |size | mAP | Jetson Nano
2015 MHz | RPi 4 64-OS
1950 MHz |
| ------------- | :-----: | :-----: | :-----: | :-----: | :-------------: | :-------------: |
| Ultra-Light-Fast| ncnn | slim-320 | 320x240 | 67.1 | - FPS | 26 FPS |
| Ultra-Light-Fast| ncnn | RFB-320 | 320x240 | 69.8 | - FPS | 23 FPS |
| Ultra-Light-Fast| MNN | slim-320 | 320x240 | 67.1 | 70 FPS | 65 FPS |
| Ultra-Light-Fast| MNN | RFB-320 | 320x240 | 69.8 | 60 FPS | 56 FPS |
| Ultra-Light-Fast| OpenCV | slim-320 | 320x240 | 67.1 | 48 FPS | 40 FPS |
| Ultra-Light-Fast| OpenCV | RFB-320 | 320x240 | 69.8 | 43 FPS | 35 FPS |
| Ultra-Light-Fast + Landmarks| ncnn | slim-320 | 320x240 | 67.1 | 50 FPS | 24 FPS |
| LFFD| ncnn | 5 stage | 320x240 | 88.6 | 16.4 FPS | 4.85 FPS |
| LFFD| ncnn | 8 stage | 320x240 | 88.6 | 11.7 FPS | 3.45 FPS |
| LFFD| MNN | 5 stage | 320x240 | 88.6 | 2.6 FPS | 2.17 FPS |
| LFFD| MNN | 8 stage | 320x240 | 88.6 | 1.8 FPS | 1.49 FPS |
| CenterFace| ncnn | - | 320x240 | 93 | 16.5 FPS | **6.8 FPS** |
| [YoloV5 face](https://github.com/Qengineering/YoloV5-face-ncnn-RPi4) | ncnn | - | 320x320 | 93.6 | - FPS | 17.2 FPS |
| [YoloV5 face](https://github.com/Qengineering/YoloV5-face-ncnn-RPi4) | ncnn | - | 480x480 | 93.6 | - FPS | 7.2 FPS |
| [YoloV5 face](https://github.com/Qengineering/YoloV5-face-ncnn-RPi4) | ncnn | - | 640x640 | 93.6 | - FPS | 4.0 FPS |
| [YoloV5 face](https://github.com/Qengineering/YoloV5-face-ncnn-RPi4) | ncnn | - | 1280x1280 | 93.6 | - FPS | 1.0 FPS |
| [YoloV5 face](https://github.com/Qengineering/YoloV5-face-ncnn-RPi4) | ncnn | - | 1920x1920 | 93.6 | - FPS | 0.5 FPS |
------------
## Dependencies.
To run the application, you have to:
- A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html)
- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html)
- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)
------------
## Installing the app.
To extract and run the network in Code::Blocks
$ mkdir *MyDir*
$ cd *MyDir*
$ wget https://github.com/Qengineering/CenterFace-ncnn-Raspberry-Pi-4/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md
Your *MyDir* folder must now look like this:
selfie.jpg
Walks2.mp4
CenterFace_ncnn.cpb
main.cpp
ncnn_centerface.h
ncnn_centerface.cpp
centerface.bin
centerface.param
------------
## Running the app.
To run the application load the project file CenterFace_ncnn.cbp in Code::Blocks.
Next, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
By default, the images are resized to 320x240. It gives you a fast, but less accurate result.
Commenting on line 8 `#define RESIZE` will scan the full size of the image, resulting in the most accurate output. However, it will take more processing time depending on the size.
------------
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