Update Readme.md

README.md

@@ -1,4 +1,4 @@
-# Yolo-v3 and Yolo-v2 for Windows and Linux
+# Yolo-v4 and Yolo-v3/v2 for Windows and Linux
 ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui)
 
 More details: http://pjreddie.com/darknet/yolo/
@@ -55,7 +55,7 @@ FPS on RTX 2070 (R) and Tesla V100 (V):
     * `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops
     * `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops
     * `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops
-    * `width=320 height=320` in cfg: **  60% mAP@0.5 (  38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops
+    * `width=320 height=320` in cfg:   **60% mAP@0.5 (  38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops
 
 * [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing)
 
@@ -153,7 +153,7 @@ Others: https://www.youtube.com/user/pjreddie/videos
 * run-time tips and warnings if you use incorrect cfg-file or dataset
 * many other fixes of code...
 
-And added manual - [How to train Yolo v3/v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
+And added manual - [How to train Yolo v4-v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
 
 Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light
 
@@ -163,12 +163,12 @@ On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector
 
 On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64` 
 
-* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25`
+* 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 v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4`
-* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0`
-* Yolo v3 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 v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi`
+* 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`
@@ -198,7 +198,7 @@ On Linux find executable file `./darknet` in the root directory, while on Window
 3. Start Smart WebCam on your phone
 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:
 
-* Yolo v3 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`
+* 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`)
 
@@ -232,7 +232,7 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
 * `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run
     `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera`
 
-To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
+To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights`
 
 ### How to compile on Windows (using `CMake-GUI`)
 
@@ -384,9 +384,9 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#t
 
 ## How to train with multi-GPU:
 
-1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74`
+1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg yolov4.conv.137`
 
-2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3`
+2. Then stop and by using partially-trained model `/backup/yolov4_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train cfg/coco.data cfg/yolov4.cfg /backup/yolov4_1000.weights -gpus 0,1,2,3`
 
 Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set `learning_rate = 0.00025` (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times `burn_in =` and `max_batches =` in your cfg-file. I.e. use `burn_in = 4000` instead of `1000`. Same goes for `steps=` if `policy=steps` is set.
 
@@ -395,7 +395,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
 ## How to train (to detect your custom objects):
 (to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data))
 
-Training Yolo v3:
+Training Yolo v4 (and v3):
 
 1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and:
 
@@ -666,9 +666,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
 
 ## How to mark bounded boxes of objects and create annotation files:
 
-Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark
+Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: https://github.com/AlexeyAB/Yolo_mark
 
-With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 & v3
+With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 - v4
 
 Different tools for marking objects in images:
 1. in C++: https://github.com/AlexeyAB/Yolo_mark