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| | | 1. [How to use](#how-to-use) |
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| | | 2. [How to compile](#how-to-compile) |
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| | | 3. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) |
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| | | 4. [How to train (to detect your custom objects)](t#how-to-train-to-detect-your-custom-objects) |
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| | | 4. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) |
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| | | 5. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) |
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| | | |  |  https://arxiv.org/abs/1612.08242 | |
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| | | 1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam |
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| | | Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam |
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| | | IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam |
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| | | * Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2 |
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| | | * IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam |
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| | | 2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB |
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| | | 3. Start Smart WebCam on your phone |
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| | | 1. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` |
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| | | 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir |
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| | | 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc` will be created dir `build\darknet\x64\data\voc\VOCdevkit\`: |
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| | | * http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar |
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| | | * http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar |
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| | | * http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar |
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| | | 2.1 Download file `voc_label.py` to dir `build\darknet\x64\data\voc`: http://pjreddie.com/media/files/voc_label.py |
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| | | 3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe |
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| | | 1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: |
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| | | * change line `classes=20` to your number of objects |
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| | | * change line `filters=425` to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | * change line #224 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines: |
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| | | 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` |
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| | | 5. Create `.txt`-file for each `.jpg`-image-file - with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>` |
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| | | 5. Create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>` |
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| | | Where: |
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| | | * `<object-class>` - integer number of object from `0` to `(classes-1)` |
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