Comments (4)
For info, there is another repository which is actively maintained by a researcher in the field: https://github.com/xinntao/facexlib
I also started by using ternaus/retinaface
(for inference only), but ended up preferring using xinntao/facexlib
in the end.
I mention that because I see that you have already found 2 bugs:
from retinaface.
Thanks a lot for mentioning it! Bummer that I didn't find this earlier, because now I think I'm way to deep into my project to switch. But I do like that this repo here is using Lightning!
Plus I dont see a training script anywhere in the facelib repo? or did I just not look closely enough?
from retinaface.
It is a good question how did this happen. I think I copied this part of the code from the original repo without changing this part of the code.
FIxed.
from retinaface.
Ah, I think you are right!
from retinaface.
Related Issues (19)
- RuntimeError during get_model HOT 1
- Validation accuracy & Mobilenet HOT 9
- Inference using GPU HOT 1
- Default parameter values HOT 1
- RGB vs. BGR HOT 2
- google-colab 1.0.0 has requirement ipykernel~=4.10 HOT 1
- Unintuitive output of predict_jsons()
- map
- Spellings mistake in repository README and PyPi description page
- Negative values in the predicted annotations HOT 2
- How to predict with batch size ? HOT 1
- Some questions about training on my custom datasets? HOT 6
- Bug: Inference on cuda results in fixed bounding box offset! HOT 2
- Does this project use focal loss?
- Suggest to loosen the dependency on albumentations
- How to train with custom dataset by using the pretrained model?
- Why def _pad_to_square do not to adjust the landmark? HOT 3
- How Syncronized BatchNorm implemented HOT 1
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from retinaface.