* optimized memory allocation during network resizing when `random=1`
* optimized memory allocation during network resizing when `random=1`
* optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1
* optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1
* added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`...
* added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`...
* added drawing of chart of average loss during training
* added drawing of chart of average-Loss and accuracy-mAP (`-map` flag) during training
* run `./darknet detector demo ... -json_port 8070 -mjpeg_port 8090` as JSON and MJPEG server to get results online over the network by using your soft or Web-browser
* added calculation of anchors for training
* added calculation of anchors for training
* added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
* added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
* fixed code for use Web-cam on OpenCV 3.x
* fixed code for use Web-cam on OpenCV 3.x
@ -108,13 +109,14 @@ Also, you might be interested in using a simplified repository where is implemen
On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output`