From fb56f6d569de0812923c881d3f62e6f4007518fa Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 08 May 2018 15:02:23 +0000
Subject: [PATCH] Fixed README.md
---
src/image.c | 2 +-
build/darknet/x64/darknet_yolo_v3.cmd | 2 +-
src/yolo_layer.c | 2 +-
README.md | 12 +++++-------
4 files changed, 8 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index bd11ac5..028b262 100644
--- a/README.md
+++ b/README.md
@@ -85,20 +85,18 @@
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`
-* Alternative method 194 MB COCO-model - image: `darknet.exe detect 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 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`
-* 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`
+* 43 MB VOC-model for video: `darknet.exe detector demo data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights test.mp4 -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`
diff --git a/build/darknet/x64/darknet_yolo_v3.cmd b/build/darknet/x64/darknet_yolo_v3.cmd
index b3347ee..2d05bdc 100644
--- a/build/darknet/x64/darknet_yolo_v3.cmd
+++ b/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
\ No newline at end of file
diff --git a/src/image.c b/src/image.c
index 467b95c..790d004 100644
--- a/src/image.c
+++ b/src/image.c
@@ -284,7 +284,7 @@
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);
diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index b2443ba..e37c175 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -55,7 +55,7 @@
l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif
- fprintf(stderr, "detection\n");
+ fprintf(stderr, "yolo\n");
srand(0);
return l;
--
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