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
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/<file-name>
Run in image
$ ./darknet detect cfg/yolo.cfg yolo.weights data/<file-name>
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
After boot Jetson TX2 with Jetpack 3.2 (CUDA 9 and cuDNN 7) and install openCV (https://github.com/AlexanderRobles21/OpenCVTX2)
$ 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
$ wget https://pjreddie.com/media/files/yolov3.weights
$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
$ ./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
$ ./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
$ ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights /dev/video1
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