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| | | * 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 |
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| | | * To process a list of images `data/train.txt` and save results of detection to `result.txt` use: |
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| | | `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` |
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| | | 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 |
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| | | ##### For using network video-camera mjpeg-stream with any Android smartphone: |
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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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| | | * 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: |
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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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