We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| deepcam-cn/yolov5-face | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| dynamic_resize | Ran | deepcam-cn/yolov5-face/test_widerface.py pointer only (licence: GPL-3.0) · get_code("445b8340051241bc") |
| scale_coords_landmarks | Ran | deepcam-cn/yolov5-face/detect_face.py pointer only (licence: GPL-3.0) · get_code("6299962009a1327a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Tremendous progress has been made on face detection in recent years using convolutional neural networks. While many face detectors use designs designated for detecting faces, we treat face detection as a generic object detection task. We implement a face detector based on the YOLOv5 object detector and call it YOLO5Face. We make a few key modifications to the YOLOv5 and optimize it for face detection. These modifications include adding a five-point landmark regression head, using a stem block at the input of the backbone, using smaller-size kernels in the SPP, and adding a P6 output in the PAN block. We design detectors of different model sizes, from an extra-large model to achieve the best performance to a super small model for real-time detection on an embedded or mobile device. Experiment results on the WiderFace dataset show that on VGA images, our face detectors can achieve state-of-the-art performance in almost all the Easy, Medium, and Hard subsets, exceeding the more complex designated face detectors. The code is available at \url{https://github.com/deepcam-cn/yolov5-face}
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.12931")
get_code_for_paper("2105.12931")
have("2105.12931")
Connect an agent — have() is free.