https://github.com/qengineering/faster_rcnn_ncnn
Faster R-CNN for ncnn framework
https://github.com/qengineering/faster_rcnn_ncnn
cpp deep-learning faster-r-cnn faster-rcnn-ncnn ncnn ncnn-framework raspberry raspberry-pi raspberry-pi-3 raspberry-pi-4
Last synced: 10 months ago
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Faster R-CNN for ncnn framework
- Host: GitHub
- URL: https://github.com/qengineering/faster_rcnn_ncnn
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2019-09-16T09:05:26.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-27T09:57:37.000Z (about 4 years ago)
- Last Synced: 2025-03-27T09:23:19.272Z (10 months ago)
- Topics: cpp, deep-learning, faster-r-cnn, faster-rcnn-ncnn, ncnn, ncnn-framework, raspberry, raspberry-pi, raspberry-pi-3, raspberry-pi-4
- Language: C++
- Size: 55.7 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Faster_RCNN_ncnn for the ncnn framework
## Faster RCNN ncnn with the ncnn framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.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)
------------
Training set: VOC2007
Size: 600x600 (!)
Prediction time: 26042 mSec(!) (RPi 4)
------------
## 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/Faster_RCNN_ncnn/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your *MyDir* folder must now look like this:
Traffic.jpg
ZF_faster_rcnn_final.bin (download this file from: https://drive.google.com/open?id=1w3F4PL03SVtvoS_ux_GfCkY0YLMGH-yA )
ZF_faster_rcnn_final.proto
Faster_rcnn.cpb
fasterrcnn.cpp
------------
## Running the app.
To run the application load the project file YoloV2.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
------------
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)