开源！适用于Win和Linux平台的YOLO4和YOLO3( 三 ) _Linux

How to use on the command lineOn Linux use ./darknet instead of darknet.exe, like this:./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights
On Linux find executable file ./darknet in the root directory, while on Windows find it in the directory builddarknet\x64

Yolo v4 COCO - image: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25
Output coordinates of objects: darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg
Yolo v4 COCO - video: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4
Yolo v4 COCO - WebCam 0: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0
Yolo v4 COCO for net-videocam - Smart WebCam: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg
Yolo v4 - save result videofile res.avi: darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi
Yolo v3 Tiny COCO - video: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
JSON and MJPEG server that allows multiple connections from your soft or Web-browser ip-address:8070 and 8090: ./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
Yolo v3 Tiny on GPU #1: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4
Alternative method Yolo v3 COCO - image: darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25
Train on Amazon EC2, to see mAP & Loss-chart using URL like: http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090 in the Chrome/Firefox (Darknet should be compiled with OpenCV): ./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map
186 MB Yolo9000 - image: darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights
Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
To process a list of images data/train.txt and save results of detection to result.json file use: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt
To process a list of images data/train.txt and save results of detection to result.txt use:
darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt
Pseudo-lableing - to process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt (in this way you can increase the amount of training data) use: darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
To calculate anchors: darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
To check accuracy mAP@IoU=50: darknet.exe detector map data/obj.data yolo-obj.cfg backupyolo-obj_7000.weights
To check accuracy mAP@IoU=75: darknet.exe detector map data/obj.data yolo-obj.cfg backupyolo-obj_7000.weights -iou_thresh 0.75

For using network video-camera mjpeg-stream with any Android smartphone

Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCamSmart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam
Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
Start Smart WebCam on your phone
Replace the address below, on shown in the phone application (Smart WebCam) and launch:

Yolo v4 COCO-model: darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

How to compile on Linux (using cmake)The CMakeLists.txt will attempt to find installed optional dependencies like CUDA, cudnn, ZED and build against those. It will also create a shared object library file to use darknet for code development.
Open a bash terminal inside the cloned repository and launch:
./build.shHow to compile on Linux (using make)Just do make in the darknet directory. (You can try to compile and run it on Google Colab in cloud link (press «Open in Playground» button at the top-left corner) and watch the video link ) Before make, you can set such options in the Makefile: link