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| | | # Yolo-v3 and Yolo-v2 for Windows and Linux |
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| | | ### (neural network for object detection) |
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| | | ### (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) |
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| | | [](https://circleci.com/gh/AlexeyAB/darknet) |
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| | | |  |  mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |
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| | | * YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ |
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| | | * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf |
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| | | * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg |
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| | | * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg |
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| | | * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported |
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| | | ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): |
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| | | * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights |
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| | | * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights |
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| | | * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights |
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| | | * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights |
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| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` |
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| | | 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 data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` |
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| | | 2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`. |
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| | | 3. 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 data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3` |
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| | | https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ |
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| | | 2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll |
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| | | * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` |
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| | | **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` |
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| | | * or you can run from MSVS2015 (before this - you should copy 2 files `yolo-voc.cfg` and `yolo-voc.weights` to the directory `build\darknet\` ) |
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| | | * after launching your console application and entering the image file name - you will see info for each object: |
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| | | `<obj_id> <left_x> <top_y> <width> <height> <probability>` |
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