https://github.com/qengineering/lffd-ncnn-jetson-nano
LFFD face detection with ncnn for Jetson Nano
https://github.com/qengineering/lffd-ncnn-jetson-nano
aarch64 deep-learning face-detection jetson-nano lffd ncnn ncnn-model
Last synced: 8 months ago
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LFFD face detection with ncnn for Jetson Nano
- Host: GitHub
- URL: https://github.com/qengineering/lffd-ncnn-jetson-nano
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2021-04-15T10:46:52.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-12-10T14:33:41.000Z (over 4 years ago)
- Last Synced: 2025-04-01T18:21:52.639Z (about 1 year ago)
- Topics: aarch64, deep-learning, face-detection, jetson-nano, lffd, ncnn, ncnn-model
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 24.2 MB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LFFD face detection Jetson Nano
## LFFD face detection with the ncnn framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://arxiv.org/pdf/1904.10633.pdf
Special made for a Jetson Nano 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 |
------------
## Dependencies.
### April 4 2021: Adapted for ncnn version 20210322 or later
To run the application, you have to:
- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-jetson-nano.html)
- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-jetson-nano.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/LFFD-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:
images folder
Walks2.mp4
FaceDetection_LFFD_ncnn.cpb
main.cpp
LFFD_ncnn.h
LFFD_ncnn.cpp
train_10_320_20L_5scales_v2_iter_1000000.bin
train_10_560_25L_8scales_v1_iter_1400000.bin
symbol_10_320_20L_5scales_v2_deploy.param
symbol_10_560_25L_8scales_v1_deploy.param
------------
## Running the app.
To run the application load the project file FaceDetection_LFFD_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).
------------
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