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Validation accuracy & Mobilenet #23
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I did not. It is not that hard to add the whole set of backbones from https://github.com/rwightman/pytorch-image-models/blob/master/results/results-imagenet.csv but I do not really need it right now, hence I cannot tell when this functionality will be added. |
Thanks for a quick response and well understood regards mobilenet. Regards the Resnet50 val accuracy, is it the same as reported in https://github.com/biubug6/Pytorch_Retinaface#widerface-val-performance-in-single-scale-when-using-resnet50-as-backbone-net or do you have some numbers for that? |
I doubt it is the same since @ternaus did train his own model. A validation accuracy (and other numbers) would be nice just to compare to the baseline retinaface. |
For information, there is also another project called The Python package is available on PyPI: pip install facexlib As shown in the following code: The model checkpoints are downloaded from here: https://github.com/xinntao/facexlib/releases/tag/v0.1.0 They use the same name convention as the original model checkpoints, so I wonder if these are direct copies without re-training. |
I have run the following code on a Google Colab machine: !wget https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth
!wget https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth
!gdown --id 15zP8BP-5IvWXWZoYTNdvUJUiBqZ1hxu1
!gdown --id 14KX6VqF69MdSPk3Tr9PlDYbq7ArpdNUW !sha256sum *.pth This is the output: 2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220 detection_mobilenet0.25_Final.pth
6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d detection_Resnet50_Final.pth
2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220 mobilenet0.25_Final.pth
6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d Resnet50_Final.pth So the model checkpoints should be the same. -- If you don't have access to # Reference: https://stackoverflow.com/a/44873382/376454
import hashlib
def sha256sum(filename):
h = hashlib.sha256()
b = bytearray(128*1024)
mv = memoryview(b)
with open(filename, 'rb', buffering=0) as f:
for n in iter(lambda : f.readinto(mv), 0):
h.update(mv[:n])
return h.hexdigest() import glob
for fname in glob.glob('*.pth'):
print(f'{sha256sum(fname)} {fname}') This is the output (same as above): 2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220 mobilenet0.25_Final.pth
6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d detection_Resnet50_Final.pth
2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220 detection_mobilenet0.25_Final.pth
6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d Resnet50_Final.pth |
How does this have anything to do with this repo? If I understood you right - your claim is that FaceXLib uses same weights as biubug6 (original retinaface repo?). On FaceXLib it is even stated (README) that they do use biubug6 model/code... |
It is additional information for users like myself who:
FaceXLib is an alternative which:
|
The reason I was actually interested in this repo was - NMS. biubug6 uses its own implementation, which is slow (it could somewhat easily be parallelized), and this one uses off-the shelf NMS which seems like a less of bottleneck. I'm mainly refereing to for loop which does NMS for every element in batch. |
I see. It looks like this repository relies on: wile FaceXLib relies on:
Maybe I misunderstood and you are referring to another part of the code than the one which I mentioned. |
Hi,
Thank you for the rewrite and very intuitive repo.
Have you reported validation accuracy for the Resnet50 and have you also implemented mobilenet?
Thanks,
Johannes
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