~speedprog/mtg/mtg_card_detector.git

			@@ -100,7 +100,6 @@
			* 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 -ext_output < data/train.txt > result.txt`
			You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509

			##### For using network video-camera mjpeg-stream with any Android smartphone:

			@@ -133,7 +132,7 @@

			### How to compile on Windows:

			1. If you have MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set x64 and Release, and do the: Build -> Build darknet. NOTE: If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
			1. If you have MSVS 2015, CUDA 9.1, cuDNN 7.0 and OpenCV 3.x (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set x64 and Release https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. NOTE: If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).

			1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`

			@@ -159,6 +158,8 @@

			5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x:
			`\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;`

			Note: CUDA must be installed only after that MSVS2015 had been installed.

			### How to compile (custom):

			@@ -180,7 +181,7 @@

			`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`

			- compile to .exe (X64 & Release) and put .dll-s near with .exe:
			- compile to .exe (X64 & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg

			* `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64

			@@ -218,7 +219,7 @@

			More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc

			Note: If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
			Note: If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well.

			## How to train with multi-GPU:

			@@ -317,7 +318,7 @@

			* Also you can get result earlier than all 45000 iterations.

			Note: If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
			Note: If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well.

			### How to train tiny-yolo (to detect your custom objects):

			@@ -415,12 +416,22 @@
			`darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
			then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file

			* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
			* check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark

			* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more

			* desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files)

			* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file

			* for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720
			and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717

			* General rule - your training dataset should include such a set of relative sizes of objects that you want to detect - differing by no more than 2 times:

			* `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width`
			* `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height`

			* to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598

			2. After training - for detection: