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| | | |  |  https://arxiv.org/abs/1612.08242 | |
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| | | # Yolo-Windows v2 |
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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)](#how-to-train-to-detect-your-custom-objects) |
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| | | 5. [When should I stop training](#when-should-i-stop-training) |
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| | | 6. [How to improve object detection](#how-to-improve-object-detection) |
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| | | 7. [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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| | | |  |  https://arxiv.org/abs/1612.08242 | |
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| | | # Yolo-Windows v2 |
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| | | # "You Only Look Once: Unified, Real-Time Object Detection (version 2)" |
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| | | A yolo windows version (for object detection) |
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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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| | | ### How to compile: |
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| | | 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
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| | | 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc12\lib` or `vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
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| | | 1.1. Find files `opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` in `C:\opencv_2.4.9\opencv\build\x64\vc12\bin` or `vc14\bin` and put it near with `darknet.exe` |
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| | | 2. If you have other version of CUDA (not 8.0) then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1 |
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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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| | | * After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` |
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| | | * Also you can get result earlier than all 45000 iterations, for example, usually sufficient 2000 iterations for each class(object). I.e. for 6 classes to avoid overfitting - you can stop training after 12000 iterations and use `yolo-obj_12000.weights` to detection. |
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| | | * Also you can get result earlier than all 45000 iterations. |
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| | | ## When should I stop training: |
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| | | Usually sufficient 2000 iterations for each class(object). But for a more precise definition when you should stop training, use the following manual: |
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| | | 1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.060730 avg**: |
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| | | > Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 |
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| | | > Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8 |
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| | | > |
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| | | > **9002**: 0.211667, **0.060730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images |
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| | | > Loaded: 0.000000 seconds |
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| | | * **9002** - iteration number (number of batch) |
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| | | * **0.060730 avg** - average loss (error) - **the lower, the better** |
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| | | When you see that average loss **0.060730 avg** enough low at many iterations and no longer decreases then you should stop training. |
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| | | 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: |
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| | | For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**: |
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| | | 2.1. At first, you should put filenames of validation images to file `data\voc.2007.test` (format as in `train.txt`) or if you haven't validation images - simply copy `data\train.txt` to `data\voc.2007.test`. |
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| | | 2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands: |
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| | | * `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` |
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| | | * `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights` |
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| | | * `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights` |
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| | | And comapre last output lines for each weights (7000, 8000, 9000): |
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| | | > 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00% |
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| | | * **IOU** - the bigger, the better (says about accuracy) - **better to use** |
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| | | * **Recall** - the bigger, the better (says about accuracy) |
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| | | For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. |
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| | | ### Custom object detection: |
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| | | Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_3000.weights` |
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| | | Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` |
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| | | |  |  | |
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| | | ## How to improve object detection: |
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| | | 1. Before training: |
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| | | * set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L244) |
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| | | 2. After training - for detection: |
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| | | * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L4) |
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| | | * you do not need to train the network again, just use `.weights`-file already trained for 416x416 resolution |
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| | | * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L3) |
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| | | ## How to mark bounded boxes of objects and create annotation files: |
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| | | Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2: https://github.com/AlexeyAB/Yolo_mark |
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