Comments (1)
YonghaoHe
commented on August 18, 2024
@lufi1 Reducing the size of feature maps as early as possible is the easiest way to decrease the inference latency. As we know, reducing the size of feature maps will harm the detection of small objects. So our direction is to reducing the size while keeping the accuracy as high as possible. Of cause, you can try MobileNet or other light backbones. The code is easy to modify and play with if you are familiar with MXNet.
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Related Issues (20)
- 头部侦测问题 HOT 13
- Four point detection HOT 4
- 头部检测问题 HOT 6
- Nan loss while training face detection model on custom dataset HOT 2
- How to run inside Nvidia Deepstream?
- face detection model failed in the CPU environment HOT 3
- Error in face detection code while testing over WIDER_val dataset
- Error in validation over WIDER_val
- Difference on accuracy between your results and my reproduction HOT 4
- LFFD使用给的pytorch模型无法测试出论文的wider结果
- Please update the repo for Vehicle Detection HOT 6
- 人脸检测结果不符合预期
- 请问运行效率fps是在什么数据集上得到的
- Not realtime when run face_detection program on GPU
- Detecting Tiny Faces HOT 3
- face_detect-python-opencv-dnn
- Basketball tracking and Intel Movidius Myriad X support
- Cuda 10.2+TensorRT 7.0.0.1, Inference issue, ValueError: could not broadcast input array from shape (3,4,3) into shape (3,4,1)
- Slowly when run 2 cameras on 1 machine
- AttributeError: module 'mxnet' has no attribute 'module'
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from lffd-a-light-and-fast-face-detector-for-edge-devices.