Comments (3)
I have not run AdaFace in Glint360k. One potential improvement comes from AdaFace capable of handling strong augmentations unlike other margin based works (augmentations which may cause unidentifiable images and helpful for low quality dataset) as noted in Table 2 of the main paper. So when using the InsightFace, incorporating the training data augmentation in this repo into your training code may help.
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Thanks for your reply. I have added the training data augmentation in this repo into insightFace project. And obtain the above experimental results.
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@Phil-Lin Great work! Can you share your code which use AdaFace and InsightFace? Thank you <3
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Related Issues (20)
- How do I convert AdaFace to onnx format? Will it have increased performance?
- Where to find image_list_mtcnn_fail_skipped_v1 HOT 1
- How to train without NCCL in Windows
- Can you provide ./image_list_mtcnn_fail_skipped_v1.csv file
- Error when trying to install the requirements HOT 3
- Error while loading a pretrained model to resume training. HOT 1
- About evaluating Adaface with other datasets
- ArcFace <-> AdaFace transcoder
- How to use pretrained model to finetune? HOT 6
- What's the dataset 'Webface 12M'?
- Face alignment during training HOT 2
- Can I get the confidence value for the image?
- 有没有**的大佬了解这个模型的,能辅导一下么 HOT 4
- Are there any plans to test performance on MFR-ongoing?
- The speed issue of validation stage when run two or more same model in different datasets
- PyTorch issue in requirements.txt
- Are there any alternatives to AdaFace? Or is it still the best face recognition model?
- inference.py is not working with large dataset. HOT 1
- Mismatch value of momentum alpha between the code and paper
- Can’t replicate the relation between feature norm and image quality
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