https://github.com/qengineering/yolov4-darknet-jetson-nano
YoloV4 with Darknet for Jetson Nano
https://github.com/qengineering/yolov4-darknet-jetson-nano
cpp darknet darknet-yolo darknet-yolov4-model jetson-nano yolo yolov4
Last synced: 4 months ago
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YoloV4 with Darknet for Jetson Nano
- Host: GitHub
- URL: https://github.com/qengineering/yolov4-darknet-jetson-nano
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2022-12-06T11:23:47.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-10T10:02:35.000Z (almost 3 years ago)
- Last Synced: 2025-06-21T17:01:55.675Z (4 months ago)
- Topics: cpp, darknet, darknet-yolo, darknet-yolov4-model, jetson-nano, yolo, yolov4
- Language: C++
- Homepage: https://qengineering.eu/install-darknet-on-jetson-nano.html
- Size: 39.5 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YoloV4 Jetson Nano
## YoloV4 with the Darknet framework.
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://arxiv.org/pdf/2004.10934.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 | size | objects | mAP | Jetson Nano 1479 MHz | RPi 4 64-OS 1950 MHz |
| ------------- | :-----: | :-----: | :-----: | :-------------: | :-------------: |
| [NanoDet](https://github.com/Qengineering/NanoDet-ncnn-Jetson-Nano) | 320x320 | 80 | 20.6 | 26.2 FPS | 13.0 FPS |
| [NanoDet Plus](https://github.com/Qengineering/NanoDetPlus-ncnn-Jetson-Nano) | 416x416 | 80 | 30.4 | 18.5 FPS | 5.0 FPS |
| [YoloFastestV2](https://github.com/Qengineering/YoloFastestV2-ncnn-Jetson-Nano) | 352x352 | 80 | 24.1 | 38.4 FPS | 18.8 FPS |
| [YoloV2](https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano) | 416x416 | 20 | 19.2 | 10.1 FPS | 3.0 FPS |
| [YoloV3](https://github.com/Qengineering/YoloV3-ncnn-Jetson-Nano) | 352x352 tiny | 20 | 16.6 | 17.7 FPS | 4.4 FPS |
| [YoloV4 Darknet](https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano) | 416x416 tiny | 80 | 21.7 | **16.5 FPS** | 3.4 FPS |
| [YoloV4](https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano) | 608x608 full | 80 | 45.3 | 1.3 FPS | 0.2 FPS |
| [YoloV5](https://github.com/Qengineering/YoloV5-ncnn-Jetson-Nano) | 640x640 small| 80 | 22.5 | 5.0 FPS | 1.6 FPS |
| [YoloV6](https://github.com/Qengineering/YoloV6-ncnn-Jetson-Nano) | 640x640 nano | 80 | 35.0 | 10.5 FPS | 2.7 FPS |
| [YoloV7](https://github.com/Qengineering/YoloV7-ncnn-Jetson-Nano) | 640x640 tiny| 80 | 38.7 | 8.5 FPS | 2.1 FPS |
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 416x416 nano | 80 | 25.8 | 22.6 FPS | 7.0 FPS |
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 416x416 tiny | 80 | 32.8 | 11.35 FPS | 2.8 FPS |
| [YoloX](https://github.com/Qengineering/YoloX-ncnn-Jetson-Nano) | 640x640 small | 80 | 40.5 | 3.65 FPS | 0.9 FPS |
------------
## Dependencies.
To run the application, you have to:
- Darknet installed. [Install Darknet](https://qengineering.eu/install-darknet-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/YoloV4-Darknet-Jetson-Nano/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:
James.mp4
parking.jpg
main.cpp
yolov4-tiny.cfg
yolov4-tiny.weights
coco.names
------------
## Running the app.
To run the application load the project file YoloV4.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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