- The time data that shown on the following tables presents the time elapsed from preprocess (resize is excluded), to a forward pass of a network, and postprocess to get final results.
- The time data that shown on the following tables is averaged from a 100-time run.
- The time data that shown on the following tables is the median of benchmark runs.
- View [benchmark/config](./benchmark/config) for more details on benchmarking different models.
<!--
...
...
@@ -29,9 +29,10 @@ Hardware Setup:
-->
| Model | Input Size | CPU x86_64 (ms) | CPU ARM (ms) |
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
SFace is contributed by [Yaoyao Zhong](https://github.com/zhongyy/SFace). [face_recognition_sface.onnx](./face_recognition_sface.onnx) is converted from the model from https://github.com/zhongyy/SFace thanks to [Chengrui Wang](https://github.com/crywang).
Note:
- There is [a PR for OpenCV adding this model](https://github.com/opencv/opencv/pull/20422) to work with OpenCV DNN in C++ implementation.
- Support 5-landmark warp for now.
-`demo.py` requires [../face_detection_yunet](../face_detection_yunet) to run.
parser.add_argument('--save','-s',type=str,default=False,help='Set true to save results. This flag is invalid when using camera.')
parser.add_argument('--vis','-v',type=str2bool,default=True,help='Set true to open a window for result visualization. This flag is invalid when using camera.')