From 840aac5115600194b40bf4d060193e6512b2a426 Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 11 Jan 2019 23:24:57 +0300 Subject: [PATCH] Update Readme.md --- README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3a7a2d53..7e1802ca 100644 --- a/README.md +++ b/README.md @@ -90,7 +90,8 @@ Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg * 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 * 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 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 @@ -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` -* **Yolo v3** COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` +* Yolo v3 COCO - **image**: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` -* **Yolo v3** COCO - video: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` -* **Yolo v3** COCO - WebCam 0: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` -* **Yolo v3** COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` +* Yolo v3 COCO - **video**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` +* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` +* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg` * **Yolo v3 - save result to the file res.avi**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` * **Yolo v3 Tiny** COCO - video: `darknet.exe detector demo data/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` or 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -http_port 8090 -ext_output` * **Yolo v3 Tiny** on GPU #0: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 test.mp4` * Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights`