Fixed README.md

README.md

@@ -85,20 +85,18 @@ Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
 
 On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
 
-* 194 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2`
+* **Yolo v3** 236 MB COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25`
-* Alternative method 194 MB COCO-model - image: `darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2`
+* Alternative method Yolo v3 COCO-model - image: `darknet.exe detect 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 -thresh 0.25 dog.jpg -ext_output`
 * 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0`
-* 194 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0`
 * 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
-* 194 MB COCO-model - **save result to the file res.avi**: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0 -out_filename res.avi`
 * 194 MB VOC-model - **save result to the file res.avi**: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi`
 * Alternative method 194 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
-* 60 MB VOC-model for video: `darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0`
+* 43 MB VOC-model for video: `darknet.exe detector demo data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights test.mp4 -i 0`
-* 194 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
+* **Yolo v3** 236 MB 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 -i 0`
 * 194 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
 * 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0`
 * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights`
-* 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4`
 * 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.txt` use:                             
     `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt`

build/darknet/x64/darknet_yolo_v3.cmd

@@ -1,5 +1,5 @@
 
-darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg
+darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 dog.jpg -ext_output
 
 
 pause

src/image.c

@@ -284,7 +284,7 @@ void draw_detections_v3(image im, detection *dets, int num, float thresh, char *
 		const int best_class = selected_detections[i].best_class;
 		printf("%s: %.0f%%", names[best_class],	selected_detections[i].det.prob[best_class] * 100);
 		if (ext_output)
-			printf("\t(left: %.0f\ttop: %.0f\tw: %0.f\th: %0.f)\n",
+			printf("\t(left: %.0f \ttop: %.0f \tw: %0.f \th: %0.f)\n",
 				(selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w,
 				(selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h,
 				selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h);

src/yolo_layer.c

@@ -55,7 +55,7 @@ layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int
     l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
 #endif
 
-    fprintf(stderr, "detection\n");
+    fprintf(stderr, "yolo\n");
     srand(0);
 
     return l;