Could you share the training log of your faster rcnn(vgg16)?

[zzdang]

Hi ,I'm training gluon-cv's faster rcnn(vgg16 and VOC). But,the 7 epoch mAP is 0.52 ,I think it is lower than I expected.
Could you share the training log of your faster rcnn(vgg16)? So I can find some mistake about my training.

[WalterMa]

Currently, the best map on VOC07 tests set is 0.6190 after 11 epoch training, much lower than original. I'm still working on this.
For now, use Gloun-CV will be a better choice, since it supports faster-rcnn after v0.2.

[Ram-Godavarthi]

Hi,
I have done training on my own dataset and i got 70% accuracy after 4 epochs..
I want to visualize the output.. so i tried with demo script.. i gave 1 input image and tried with the trained model. i changed the class names in demo script.
but i got this error.. Could you please let me know whats the problem.. Thank You

Traceback (most recent call last):
File "demo_faster_rcnn.py", line 65, in
cls, scores, bboxes = net(data.as_in_context(ctx), im_info.as_in_context(ctx))
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 413, in call
return self.forward(*args)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 629, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
File "/home/ubuntu/gluon-faster-rcnn/rcnn/rcnn.py", line 69, in hybrid_forward
rois = self.proposal(rpn_cls_prob, rpn_bbox_pred, im_info)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 413, in call
return self.forward(*args)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/gluon/block.py", line 629, in forward
return self.hybrid_forward(ndarray, x, *args, **params)
File "/home/ubuntu/gluon-faster-rcnn/rcnn/proposal.py", line 32, in hybrid_forward
threshold=self.rpn_nms_threshold, rpn_min_size=self.rpn_min_size)
File "", line 82, in MultiProposal
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/mxnet/base.py", line 149, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: Cannot find argument 'cls_prob', Possible Arguments:
rpn_pre_nms_top_n : int, optional, default='6000'
Number of top scoring boxes to keep after applying NMS to RPN proposals
rpn_post_nms_top_n : int, optional, default='300'
Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold
threshold : float, optional, default=0.7
NMS value, below which to suppress.
rpn_min_size : int, optional, default='16'
Minimum height or width in proposal
scales : tuple of , optional, default=[4,8,16,32]
Used to generate anchor windows by enumerating scales
ratios : tuple of , optional, default=[0.5,1,2]
Used to generate anchor windows by enumerating ratios
feature_stride : int, optional, default='16'
The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride's prior to this layer.
output_score : boolean, optional, default=0
Add score to outputs
iou_loss : boolean, optional, default=0
Usage of IoU Loss
, in operator _contrib_MultiProposal(name="", feature_stride="16", ratios="(0.5, 1, 2)", rpn_min_size="16", scales="(8, 16, 32)", rpn_post_nms_top_n="300", rpn_pre_nms_top_n="6000", threshold="0.7", cls_prob="
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<NDArray 1x18x37x37 @gpu(0)>")