insightface/detection/scrfd/README.md

Introduction

SCRFD is an efficient high accuracy face detection approach which initially described in Arxiv.

Performance

Precision, flops and infer time are all evaluated on VGA resolution.

ResNet family

Method	Backbone	Easy	Medium	Hard	#Params(M)	#Flops(G)	Infer(ms)
DSFD (CVPR19)	ResNet152	94.29	91.47	71.39	120.06	259.55	55.6
RetinaFace (CVPR20)	ResNet50	94.92	91.90	64.17	29.50	37.59	21.7
HAMBox (CVPR20)	ResNet50	95.27	93.76	76.75	30.24	43.28	25.9
TinaFace (Arxiv20)	ResNet50	95.61	94.25	81.43	37.98	172.95	38.9
-	-	-	-	-	-	-	-
ResNet-34GF	ResNet50	95.64	94.22	84.02	24.81	34.16	11.8
SCRFD-34GF	Bottleneck Res	96.06	94.92	85.29	9.80	34.13	11.7
ResNet-10GF	ResNet34x0.5	94.69	92.90	80.42	6.85	10.18	6.3
SCRFD-10GF	Basic Res	95.16	93.87	83.05	3.86	9.98	4.9
ResNet-2.5GF	ResNet34x0.25	93.21	91.11	74.47	1.62	2.57	5.4
SCRFD-2.5GF	Basic Res	93.78	92.16	77.87	0.67	2.53	4.2

Mobile family

Method	Backbone	Easy	Medium	Hard	#Params(M)	#Flops(G)	Infer(ms)
RetinaFace (CVPR20)	MobileNet0.25	87.78	81.16	47.32	0.44	0.802	7.9
FaceBoxes (IJCB17)	-	76.17	57.17	24.18	1.01	0.275	2.5
-	-	-	-	-	-	-	-
MobileNet-0.5GF	MobileNetx0.25	90.38	87.05	66.68	0.37	0.507	3.7
SCRFD-0.5GF	Depth-wise Conv	90.57	88.12	68.51	0.57	0.508	3.6

X64 CPU Performance of SCRFD-0.5GF:

Test-Input-Size	CPU Single-Thread	Easy	Medium	Hard
Original-Size(scale1.0)	-	90.91	89.49	82.03
640x480	28.3ms	90.57	88.12	68.51
320x240	11.4ms	-	-	-

precision and infer time are evaluated on AMD Ryzen 9 3950X, using the simple PyTorch CPU inference by setting OMP_NUM_THREADS=1 (no mkldnn).

Installation

Please refer to mmdetection for installation.

1. Install mmcv. (mmcv-full==1.2.6 and 1.3.3 was tested)
2. Install build requirements and then install mmdet.

   pip install -r requirements/build.txt
   pip install -v -e .  # or "python setup.py develop"
   

Data preparation

WIDERFace:

1. Download WIDERFace datasets and put it under data/retinaface.
2. Download annotation files from gdrive and put them under data/retinaface/

  data/retinaface/
      train/
          images/
          labelv2.txt
      val/
          images/
          labelv2.txt
          gt/
              *.mat
          

Annotation Format

please refer to labelv2.txt for detail

For each image:

# <image_path> image_width image_height
bbox_x1 bbox_y1 bbox_x2 bbox_y2 (<keypoint,3>*N)
...
...
# <image_path> image_width image_height
bbox_x1 bbox_y1 bbox_x2 bbox_y2 (<keypoint,3>*N)
...
...

Keypoints can be ignored if there is bbox annotation only.

Training

Example training command, with 4 GPUs:

CUDA_VISIBLE_DEVICES="0,1,2,3" PORT=29701 bash ./tools/dist_train.sh ./configs/scrfd/scrfd_1g.py 4

WIDERFace Evaluation

We use a pure python evaluation script without Matlab.

GPU=0
GROUP=scrfd
TASK=scrfd_2.5g
CUDA_VISIBLE_DEVICES="$GPU" python -u tools/test_widerface.py ./configs/"$GROUP"/"$TASK".py ./work_dirs/"$TASK"/model.pth --mode 0 --out wouts

Pretrained-Models

Name	Easy	Medium	Hard	FLOPs	Params(M)	Infer(ms)	Link
SCRFD_500M	90.57	88.12	68.51	500M	0.57	3.6	download
SCRFD_1G	92.38	90.57	74.80	1G	0.64	4.1	download
SCRFD_2.5G	93.78	92.16	77.87	2.5G	0.67	4.2	download
SCRFD_10G	95.16	93.87	83.05	10G	3.86	4.9	download
SCRFD_34G	96.06	94.92	85.29	34G	9.80	11.7	download
SCRFD_500M_KPS	90.97	88.44	69.49	500M	0.57	3.6	download
SCRFD_2.5G_KPS	93.80	92.02	77.13	2.5G	0.82	4.3	download
SCRFD_10G_KPS	95.40	94.01	82.80	10G	4.23	5.0	download

mAP, FLOPs and inference latency are all evaluated on VGA resolution. _KPS means the model includes 5 keypoints prediction.

Convert to ONNX

Please refer to tools/scrfd2onnx.py

Generated onnx model can accept dynamic input as default.

You can also set specific input shape by pass --shape 640 640, then output onnx model can be optimized by onnx-simplifier.

Inference

Please refer to tools/scrfd.py which uses onnxruntime to do inference.

Network Search

For two-steps search as we described in paper, we target hard mAP on how we select best candidate models.

We provide an example for searching SCRFD-2.5GF in this repo as below.

1. For searching backbones:

   python search_tools/generate_configs_2.5g.py --mode 1
   

   Where mode==1 means searching backbone only. For other parameters, please check the code.

2. After step-1 done, there will be configs/scrfdgen2.5g/scrfdgen2.5g_1.py to configs/scrfdgen2.5g/scrfdgen2.5g_64.py if num_configs is set to 64.

3. Do training for every generated configs for 80 epochs, please check search_tools/search_train.sh

4. Test WIDERFace precision for every generated configs, using search_tools/search_test.sh.

5. Select the top accurate config as the base template(assume the 10-th config is the best), then do the overall network search.

   python search_tools/generate_configs_2.5g.py --mode 2 --template 10
   

6. Test these new generated configs again and select the top accurate one(s).

Demo

1. https://github.com/nihui/ncnn-android-scrfd