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| | | It will 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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| | | * `<object-class>` - integer object number from `0` to `(classes-1)` |
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| | | * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from (0.0 to 1.0] |
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| | | * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>` |
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| | | * atention: `<x> <y>` - are center of rectangle (are not top-left corner) |
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| | | **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` |
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| | | **Note:** 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/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4) |
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| | | ### How to train tiny-yolo (to detect your custom objects): |
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| | | Do all the same steps as for the full yolo model as described above. With the exception of: |
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