https://github.com/alro10/yolo-darknet-on-jetson-tx2
How to run YOLO on Jetson TX2
https://github.com/alro10/yolo-darknet-on-jetson-tx2
deep-learning jetson jetson-tx2 object-detection yolo yolo-darknet
Last synced: 7 months ago
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How to run YOLO on Jetson TX2
- Host: GitHub
- URL: https://github.com/alro10/yolo-darknet-on-jetson-tx2
- Owner: Alro10
- License: mit
- Created: 2017-11-22T17:44:34.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2019-04-29T01:04:34.000Z (over 6 years ago)
- Last Synced: 2025-01-20T13:27:28.186Z (9 months ago)
- Topics: deep-learning, jetson, jetson-tx2, object-detection, yolo, yolo-darknet
- Size: 2.62 MB
- Stars: 100
- Watchers: 5
- Forks: 31
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YOLO-darknet-on-Jetson-TX2 and on-Jetson-TX1
Yolo darknet is an amazing algorithm that uses deep learning for real-time object detection but needs a good GPU, many CUDA cores. For **Jetson TX2 and TX1 I would like to recommend to you use this repository if you want to achieve better performance, more fps, and detect more objects [real-time object detection on Jetson TX2](https://github.com/Alro10/realtime_object_detection)**
## How to run YOLO on Jetson TX2
After boot (Jetpack 3.1) and install OPENCV...
Copy original Yolo repository:
$ git clone https://github.com/pjreddie/darknet.git
$ cd darknet
$ sudo sed -i 's/GPU=0/GPU=1/g' Makefile
$ sudo sed -i 's/CUDNN=0/CUDNN=1/g' Makefile
$ sudo sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
$ make -j4
You will have to download the pre-trained weight file yolo.weights or tiny-yolo but this is much faster but less accurate than the normal YOLO model.
$ wget https://pjreddie.com/media/files/yolo.weights
$ wget https://pjreddie.com/media/files/tiny-yolo-voc.weights
For TX1 and change the batch size and subdivisions if you run out od memory:
$ sudo nano cfg/yolov3.cfg
increase the batch size and reduce the subdivisions:
#batch=64
batch=32
#subdvisions=16
subdivisions=32
### How to run YOLO using onboard camara Jetson TX2? It's a really hard question, I needed to find many sites but I found the right solution:
*overclock*
```
$ sudo ./jetson_clocks.sh
$ ./darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"
```
Or if you wan to run using tiny-yolo only need to change
```
$ ./darknet detector test cfg/voc.data cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights
```
Run in videos
```
$ ./darknet detector demo cfg/coco.data cfg/yolo.cfg yolo.weights data/
```
Run in image
```
$ ./darknet detect cfg/yolo.cfg yolo.weights data/
```
I recommend to take a look...https://pjreddie.com/darknet/yolo/ for more details of YOLO!
I think it is important to install a SSD and setup to work as the root directory. Also build a kernel and extra modules, you can do the last recommendation after o before build and run YOLO. Jetson only has 32gb.
See this videos:
https://www.youtube.com/watch?v=ZpQgRdg8RmA&t=4s
# YOLOV3 on Jetson TX2 (last update)
After boot Jetson TX2 with Jetpack 3.2 (CUDA 9 and cuDNN 7) and install openCV (https://github.com/AlexanderRobles21/OpenCVTX2)
## Build darknet:
```
$ git clone https://github.com/pjreddie/darknet.git
$ cd darknet
$ sudo sed -i 's/GPU=0/GPU=1/g' Makefile
$ sudo sed -i 's/CUDNN=0/CUDNN=1/g' Makefile
$ sudo sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
$ make -j4
```
## Download weights
```
$ wget https://pjreddie.com/media/files/yolov3.weights
$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
```
## Run on JETSON TX2 using onboard cam
### For yolov3:
```
$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"
```
**Performance: 2-4fps**
### For tiny-yolov3:
```
$ ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink"
```
You are able to change the resolution just modify this part: **width=(int)1280, height=(int)720**.
**Performance: 12fps**
### Using usb webcam:
```
$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights /dev/video1
```
*This information was useful for your project? Consider to cite my repository!*