faster_rcnn_pytorch
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Faster RCNN with PyTorch
Hi there, I got some problem extracting feature from a given roi. The code I wrote is ```python def _im_exfeat(self, image, roi): """ image: ( ndarray ) (H x W...
It seems that the network uses `x1,y1,x2,y2` format for bounding boxes instead of `x,y,w,h` used in the paper. I think this is a pretty major difference that can affect training...
I am trying to train the model with my own dataset. Sometimes , I got this error ```Traceback (most recent call last): File "train.py", line 127, in net(im_data, im_info, gt_boxes,...
when I run the train.py, the error `ImportError: /usr/share/Anaconda2/lib/python2.7/site-packages/torch/lib/libgomp.so.1: version `GOMP_4.0' not found (required by /opt/OpenBLAS/lib/libopenblas.so.0) ` happens. How can I fix it?
Hello, How to modify the repo to support multi gpu training?
The paper use as training method called "Alternating Training", but the code just train the model end to end.
Hi, @longcw , could you pls provide any hint of adapting the ROI pooling function for fast rcnn training? Thanks a lot !
I've seen other implementations allow random transformations to train images like rotations, horizontal shifts and zooms. Can you help me with this or direct me to some thread or example...
**The reg layer in RPN has 4k outputs encoding the coordinates of k boxes.**(from the paper) So what actually this layer predicts?Is it predicting the 4 coordinates of boundary boxes...
mAP
Hi, Would you mind to show the mAP of the provided model file? Thanks a lot