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| | | 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: |
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| | | `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;` |
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| | | **Note:** CUDA must be installed only after that MSVS2015 had been installed. |
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| | | ### How to compile (custom): |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` |
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| | | then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file |
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| | | * 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 |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds |
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| | | * 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) |
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| | | |
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| | | * 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 |
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| | | * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 |
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| | | and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 |
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| | | * General rule - you should keep relative size of objects in the Training and Testing datasets roughly the same: |
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| | | * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` |
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| | | * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` |
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| | | * 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 |
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| | | 2. After training - for detection: |
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