| | |
|---|
| | | |  |  mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |
|---|
| | | |---|---| |
|---|
| | | |
|---|
| | | * YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ |
|---|
| | | * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf |
|---|
| | | * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg |
|---|
| | | * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg |
|---|
| | |
|---|
| | | * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported |
|---|
| | | |
|---|
| | | ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): |
|---|
| | | * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights |
|---|
| | | * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights |
|---|
| | | * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights |
|---|
| | | * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights |
|---|
| | |
|---|
| | | |
|---|
| | | * 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) - use as many images of negative samples as there are images with objects |
|---|
| | | |
|---|
| | | * 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 |
|---|
| | | * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) |
|---|
| | | |
|---|
| | | * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 |
|---|
| | | and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 |
|---|
| | |
|---|
| | | 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 |
|---|
| | | |
|---|
| | | * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe` |
|---|
| | | **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4` |
|---|
| | | |
|---|
| | | * 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\` ) |
|---|
| | | * after launching your console application and entering the image file name - you will see info for each object: |
|---|
| | | `<obj_id> <left_x> <top_y> <width> <height> <probability>` |
|---|
