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License: Apache License 2.0
Lightweight Face Detector from CLOVA
License: Apache License 2.0
Hi,
I am interested in this work and want to dive in deeper.
Can you provide training code for the results?
Also, how is eresfd_16 different from eresfd?
Thank you for the work.
hi there, is in your plans to release the training code ?
The lines:
Lines 328 to 331 in 69c24b2
Creates a warning on PyTorch 1.12:
bbox_utils.py:331: UserWarning: An output with one or more elements was resized since it had shape [37], which does not match the required output shape [34]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ..\aten\src\ATen\native\Resize.cpp:24.)
Hi, I've been following your code and paper with interest!
Just to let you know, I checked the inference time with your test_wider.py code with widerface db (after resizing to vga size) and it doesn't 37.7 ms. Can you tell me how you measured it?
Below is an example of where I took the measurement points. Thanks :)
t1 = time.time()
# NOTE: modified
# det0 shape: 750, 5
det0 = detect_face(net, img, shrink, thresh=thresh)
# multi-scale
if not single_scale_test:
print('det1')
det1 = flip_test(net, img, shrink, thresh=thresh) # flip test
print('det2')
[det2, det3] = multi_scale_test(net, img, max_im_shrink, thresh=thresh)
det = np.row_stack((det0, det1, det2, det3))
dets = bbox_vote(det)
else:
dets = det0
t2 = time.time()
print("Detect %04d th image costs %.4f sec" % (counter, t2 - t1))
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