https://github.com/qengineering/rock-5-image
Rock Pi 5 image with OpenCV, deep learning frameworks and NPU drivers
https://github.com/qengineering/rock-5-image
debian11 ncnn npu opencv rknpu2 rock-pi-5 tnn
Last synced: 8 months ago
JSON representation
Rock Pi 5 image with OpenCV, deep learning frameworks and NPU drivers
- Host: GitHub
- URL: https://github.com/qengineering/rock-5-image
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2023-04-06T08:51:36.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-30T16:16:38.000Z (about 2 years ago)
- Last Synced: 2025-04-13T18:55:12.024Z (about 1 year ago)
- Topics: debian11, ncnn, npu, opencv, rknpu2, rock-pi-5, tnn
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 13.7 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Rock 5 image
## A Rock 5 image with OpenCV, ncnn, TNN and NPU
[](https://opensource.org/licenses/BSD-3-Clause)
------------
## Installation.
- Get a 32 GB (minimal) SD card holding the image.
- Download the `RockPi5.img.xz` image (2.8 GByte) from our [Sync](https://ln5.sync.com/dl/b9c189080/csvcycve-qn6f2zt8-49z54nm6-m9gvzbf3) site.
- Flash the image on the SD card with the [Imager](https://www.raspberrypi.org/software/) or [balenaEtcher](https://www.balena.io/etcher/).
- Insert the SD card in your Rock 5 and enjoy.
- Username: ***rock***
- no password
------------
## Model performance benchmark(FPS)
All models, with C++ examples, can be found on the SD image.
| demo | model_name | inputs_shape | dtype | Rock 5 |
| ---------------- | ---------------------------- | ----------------------- | ----- | ------------- |
| yolov5 | yolov5s_relu | [1, 3, 640, 640] | INT8 | 50.0 |
| | yolov5n | [1, 3, 640, 640] | INT8 | 58.8 |
| | yolov5s | [1, 3, 640, 640] | INT8 | 37.7 |
| | yolov5m | [1, 3, 640, 640] | INT8 | 16.2 |
| yolov6 | yolov6n | [1, 3, 640, 640] | INT8 | 63.0 |
| | yolov6s | [1, 3, 640, 640] | INT8 | 29.5 |
| | yolov6m | [1, 3, 640, 640] | INT8 | 15.4 |
| yolov7 | yolov7-tiny | [1, 3, 640, 640] | INT8 | 53.4 |
| | yolov7 | [1, 3, 640, 640] | INT8 | 9.4 |
| yolov8 | yolov8n | [1, 3, 640, 640] | INT8 | 53.1 |
| | yolov8s | [1, 3, 640, 640] | INT8 | 28.5 |
| | yolov8m | [1, 3, 640, 640] | INT8 | 12.1 |
| yolox | yolox_s | [1, 3, 640, 640] | INT8 | 30.0 |
| | yolox_m | [1, 3, 640, 640] | INT8 | 12.9 |
| ppyoloe | ppyoloe_s | [1, 3, 640, 640] | INT8 | 28.8 |
| | ppyoloe_m | [1, 3, 640, 640] | INT8 | 13.1 |
| yolov5_seg | yolov5n-seg | [1, 3, 640, 640] | INT8 | 9.4 |
| | yolov5s-seg | [1, 3, 640, 640] | INT8 | 7.8 |
| | yolov5m-seg | [1, 3, 640, 640] | INT8 | 6.1 |
| yolov8_seg | yolov8n-seg | [1, 3, 640, 640] | INT8 | 8.9 |
| | yolov8s-seg | [1, 3, 640, 640] | INT8 | 7.3 |
| | yolov8m-seg | [1, 3, 640, 640] | INT8 | 4.5 |
| ppseg | ppseg_lite_1024x512 | [1, 3, 512, 512] | INT8 | 27.5 |
| RetinaFace | RetinaFace_mobile320 | [1, 3, 320, 320] | INT8 | 243.6 |
| | RetinaFace_resnet50_320 | [1, 3, 320, 320] | INT8 | 43.4 |
| PPOCR-Det | ppocrv4_det | [1, 3, 480, 480] | INT8 | 31.5 |
| PPOCR-Rec | ppocrv4_rec | [1, 3, 48, 320] | FP16 | 35.7 |
* Due to the pixel-wise filling and drawing, segmentation models are relatively slow
------------
## Tips.
* If you need extra space delete the opencv and the opencv_contrib folder from the SD card. They are no longer needed since all libraries are stored in the /usr/ directory.
* Use a tool like [GParted](https://gparted.org/) `sudo apt-get install gparted` to expand the image to larger SD cards. We recommend a minimum of 64 GB. Deep learning requires a lot of space.
* An example of YoloV5 running on the NPU (25 FPS) is included.
------------
## Pre-installed frameworks.
- [OpenCV](https://qengineering.eu/deep-learning-with-opencv-on-raspberry-pi-4.html) 4.9.0
- [ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) 20240410
- NPU [rknpu2](https://github.com/airockchip/rknn-toolkit2/tree/master/rknpu2) 1.5.2
- NPU [model zoo](https://github.com/airockchip/rknn_model_zoo) 2.0.0
- NPU [model zoo models](https://github.com/Qengineering/rknn_model_zoo) 2.0.0
- [TeamViewer aarch64](https://www.teamviewer.com/en/download/linux/) 15.24.5
------------
### Thanks.
A more than special thanks to [***Stuart Naylor***](https://forum.radxa.com/u/stuartiannaylor/summary), who, ever so kindly, provided us the Rock Pi 5 for free.
------------
------------
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)