cory8249
cory8249
+1 please help give some hint about how to use the code. Thanks a lot.
I want to build a real-time video object detection system, so input are fed one by one continuously. I originally used "caffe time" script command to benchmark, but I found...
I simply use im_detect() in a for loop. I found that it will create testDB (seemed not necessary for my app), so I am going to modify it myself.
I think these codes are not necessary if you follow #23. You can remove all these default number. Just let it initialized to zeros: `_boxes = np.zeros([hw, num_anchors, 4], dtype=np.float)`...
I slightly change this code (following original YOLO training procedure), and train 160 epoch on VOC07+12, test on VOC07-test, evaluated mAP with 416 x 416 resolution 0.6334, batch size 16...
@JesseYang Sorry to let you misunderstand, 0.768 mAP is not trained by me. I just mention it as reference.
I fix the [IoU bug](https://github.com/longcw/yolo2-pytorch/issues/19), and train on VOC0712 trainval. Get mAP = 0.6825 (still increase slowly) [https://github.com/cory8249/yolo2-pytorch/blob/master/darknet.py#L120](https://github.com/cory8249/yolo2-pytorch/blob/master/darknet.py#L120)
@JesseYang I think I've fix these exp() sig() bug in my experiment.
I also found something interesting: ver.A = pytorch anaconda prebuild version (cp36) ver.B = pytorch built from source code using native python (python35) In training phase ver.A is **2x slower**...
I've trained a model with mAP = 0.71 by fixing bug in #23