drcege
commented
3 days ago Thank you for your feedback.
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We are aware of the challenges involved in installing
dlib, so we have switched the face detection implementation to OpenCV, completely eliminating the dependency ondlib. Both implementations aim to provide ease of use for quick setup; however, the accuracy may not reach state-of-the-art levels. We plan to support a series of state-of-the-art face detection models through the ModelScope interface, including the mentionedMTCNN, as well asRetinaFace,MogFace, and others, which we expect to implement in the coming weeks. -
Your suggestion regarding the
image_face_counter_filteris excellent. Please feel free to submit an implementation PR, and we can work together to enhance it.
TobyJasper
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Description 描述
Hi there,
I'd like to provide some feedback on the image_face_ratio_filter operator and also propose a new feature.
image_face_ratio_filter Performance: I've been using this operator for image filtering, but I've noticed that the dlib-based face detection doesn't perform as well as expected, especially in terms of detection accuracy. Additionally, installing the CUDA-supported version of dlib is quite cumbersome on some systems, which could be a barrier for users who want to leverage GPU acceleration. Would you consider using a different model, such as MTCNN or a similar alternative, which might offer better performance and easier installation?
Suggestion for image_face_counter_filter: I'd also like to suggest adding a new operator, image_face_counter_filter, which would allow users to filter images based on the number of faces detected. This would be especially useful for tasks where only images with a specific number of faces (e.g., exactly one face, multiple faces) are needed. What are your thoughts on including this in the project? If needed, I would be happy to submit a PR to implement this feature.
Use case 使用场景
No response
Additional 额外信息
No response
Are you willing to submit a PR for this feature? 您是否乐意为此功能提交一个 PR？