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PyTorch implementation of Complex-YOLO paper with YoloV3

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![image](https://user-images.githubusercontent.com/8176780/70886546-ace0bb80-2016-11ea-834e-059b4044cf2d.png)

Anyone can help me? @ghimiredhikura

@ghimiredhikura its an amazing work you have done here , i had few queries 1. What is the mAP of ur model 2. since yolov3 is preferred for faster inference...

Anchor boxes are choosen in complex_yolov3.cfg anchors = 11,14,-3.14, 11,14,0, 11,14,3.14, 11,25,-3.14, 11,25,0, 11,25,3.14, 23,51,-3.14, 23,51,0, 23,51,3.14 Is there is any basis on the top of which anchors are designed....

First, I noticed we need to permute `x` and `y` label in `build_yolo_target` in [kitti_bev_utils.py,](https://github.com/ghimiredhikura/Complex-YOLOv3/blob/master/utils/kitti_bev_utils.py#L125) which confuses me. ``` one_target.append(cl) one_target.append(y1) one_target.append(x1) ``` Besides, in [test_detection.py,](https://github.com/ghimiredhikura/Complex-YOLOv3/blob/master/test_detection.py#L152), `w` and `l` need...

@ghimiredhikura do u have any thoughts of changing the yolov3 architecture to obtain better accuracy for pedestrian and cyclist classes

I need to change FOV from [0,50] and [-25,25] to [0,40] and [-40,40]. In trhis case iam assuming BEV RGB images are of size 480*960, then YOLO modules crashes. KIndly...

@ghimiredhikura Is it possible to share your trained model ???

I noticed my RGB maps were oddly split down the middle and flipped. It's due to the negative indices rolling over. A fix would involve adding `+ Height / 2`...