yolov5实时、高精度的人脸检测

## What's New **2023.04** [yolov8-face](https://github.com/derronqi/yolov8-face) (🔥🔥🔥↑) **2022.10** [yolov7-face](https://github.com/derronqi/yolov7-face) (🔥🔥🔥↑) **2022.8**: yolov7 | Method | Test Size | Easy | Medium | Hard | FLOPs (B) @640 | | -----------------| ---------- | ----- | ------ | ----- | -------------- | | yolov7-lite-t | 640 | 88.7 | 85.2 | 71.5 | 0.8 | | yolov7-lite-s | 640 | 92.7 | 89.9 | 78.5 | 3.0 | | yolov7-tiny | 640 | 94.7 | 92.6 | 82.1 | 13.2 | | yolov7s | 640 | 94.8 | 93.1 | 85.2 | 16.8 | | yolov7 | 640 | 96.9 | 95.5 | 88.0 | 103.4 | | yolov7+TTA | 640 | 97.2 | 95.8 | 87.7 | 103.4 | **2021.12**: Yolov5-face to TensorRT. | Backbone | Pytorch(ms) | TensorRT_FP16(ms) | | :----------: | :---------: | :---------------: | | yolov5n-0.5 | 7.7 | 2.1 | | yolov5n-face | 7.7 | 2.4 | | yolov5s-face | 5.6 | 2.2 | | yolov5m-face | 9.9 | 3.3 | | yolov5l-face | 15.9 | 4.5 | > Pytorch=1.10.0+cu102 TensorRT=8.2.0.6 Hardware=rtx2080ti **2021.11**: BlazeFace | Method | multi scale | Easy | Medium | Hard | Model Size(MB) | Link | | -------------------- | ----------- | ----- | ------ | ----- | -------------- | ----- | | BlazeFace | Ture | 88.5 | 85.5 | 73.1 | 0.472 | https://github.com/PaddlePaddle/PaddleDetection | | BlazeFace-FPN-SSH | Ture | 90.7 | 88.3 | 79.3 | 0.479 | https://github.com/PaddlePaddle/PaddleDetection | | yolov5-blazeface | True | 90.4 | 88.7 | 78.0 | 0.493 | https://pan.baidu.com/s/1RHp8wa615OuDVhsO-qrMpQ pwd:r3v3 https://drive.google.com/file/d/1adi6ke2vCLQFcpbvFqWo_J4wZIfPqSMG| | yolov5-blazeface-fpn | True | 90.8 | 89.4 | 79.1 | 0.493 | - | **2021.08**: Add new training dataset [Multi-Task-Facial](https://drive.google.com/file/d/1Pwd6ga06cDjeOX20RSC1KWiT888Q9IpM/view?usp=sharing),improve large face detection. | Method | Easy | Medium | Hard | | -------------------- | ----- | ------ | ----- | | ***YOLOv5s*** | 94.56 | 92.92 | 83.84 | | ***YOLOv5m*** | 95.46 | 93.87 | 85.54 | ## Introduction Yolov5-face is a real-time,high accuracy face detection.  ## Performance Single Scale Inference on VGA resolution（max side is equal to 640 and scale). ***Large family*** | Method | Backbone | Easy | Medium | Hard | \#Params(M) | \#Flops(G) | | :------------------ | -------------- | ----- | ------ | ----- | ----------- | ---------- | | DSFD (CVPR19) | ResNet152 | 94.29 | 91.47 | 71.39 | 120.06 | 259.55 | | RetinaFace (CVPR20) | ResNet50 | 94.92 | 91.90 | 64.17 | 29.50 | 37.59 | | HAMBox (CVPR20) | ResNet50 | 95.27 | 93.76 | 76.75 | 30.24 | 43.28 | | TinaFace (Arxiv20) | ResNet50 | 95.61 | 94.25 | 81.43 | 37.98 | 172.95 | | SCRFD-34GF(Arxiv21) | Bottleneck Res | 96.06 | 94.92 | 85.29 | 9.80 | 34.13 | | SCRFD-10GF(Arxiv21) | Basic Res | 95.16 | 93.87 | 83.05 | 3.86 | 9.98 | | - | - | - | - | - | - | - | | ***YOLOv5s*** | CSPNet | 94.67 | 92.75 | 83.03 | 7.075 | 5.751 | | **YOLOv5s6** | CSPNet | 95.48 | 93.66 | 82.8 | 12.386 | 6.280 | | ***YOLOv5m*** | CSPNet | 95.30 | 93.76 | 85.28 | 21.063 | 18.146 | | **YOLOv5m6** | CSPNet | 95.66 | 94.1 | 85.2 | 35.485 | 19.773 | | ***YOLOv5l*** | CSPNet | 95.78 | 94.30 | 86.13 | 46.627 | 41.607 | | ***YOLOv5l6*** | CSPNet | 96.38 | 94.90 | 85.88 | 76.674 | 45.279 | ***Small family*** | Method | Backbone | Easy | Medium | Hard | \#Params(M) | \#Flops(G) | | -------------------- | --------------- | ----- | ------ | ----- | ----------- | ---------- | | RetinaFace (CVPR20 | MobileNet0.25 | 87.78 | 81.16 | 47.32 | 0.44 | 0.802 | | FaceBoxes (IJCB17) | | 76.17 | 57.17 | 24.18 | 1.01 | 0.275 | | SCRFD-0.5GF(Arxiv21) | Depth-wise Conv | 90.57 | 88.12 | 68.51 | 0.57 | 0.508 | | SCRFD-2.5GF(Arxiv21) | Basic Res | 93.78 | 92.16 | 77.87 | 0.67 | 2.53 | | - | - | - | - | - | - | - | | ***YOLOv5n*** | ShuffleNetv2 | 93.74 | 91.54 | 80.32 | 1.726 | 2.111 | | ***YOLOv5n-0.5*** | ShuffleNetv2 | 90.76 | 88.12 | 73.82 | 0.447 | 0.571 | ## Pretrained-Models | Name | Easy | Medium | Hard | FLOPs(G) | Params(M) | Link | | ----------- | ----- | ------ | ----- | -------- | --------- | ------------------------------------------------------------ | | yolov5n-0.5 | 90.76 | 88.12 | 73.82 | 0.571 | 0.447 | Link: https://pan.baidu.com/s/1UgiKwzFq5NXI2y-Zui1kiA pwd: s5ow, https://drive.google.com/file/d/1XJ8w55Y9Po7Y5WP4X1Kg1a77ok2tL_KY/view?usp=sharing | | yolov5n | 93.61 | 91.52 | 80.53 | 2.111 | 1.726 | Link: https://pan.baidu.com/s/1xsYns6cyB84aPDgXB7sNDQ pwd: lw9j,https://drive.google.com/file/d/18oenL6tjFkdR1f5IgpYeQfDFqU4w3jEr/view?usp=sharing | | yolov5s | 94.33 | 92.61 | 83.15 | 5.751 | 7.075 | Link: https://pan.baidu.com/s/1fyzLxZYx7Ja1_PCIWRhxbw Link: eq0q,https://drive.google.com/file/d/1zxaHeLDyID9YU4-hqK7KNepXIwbTkRIO/view?usp=sharing | | yolov5m | 95.30 | 93.76 | 85.28 | 18.146 | 21.063 | Link: https://pan.baidu.com/s/1oePvd2K6R4-gT0g7EERmdQ pwd: jmtk, https://drive.google.com/file/d/1Sx-KEGXSxvPMS35JhzQKeRBiqC98VDDI | | yolov5l | 95.78 | 94.30 | 86.13 | 41.607 | 46.627 | Link: https://pan.baidu.com/s/11l4qSEgA2-c7e8lpRt8iFw pwd: 0mq7, https://drive.google.com/file/d/16F-3AjdQBn9p3nMhStUxfDNAE_1bOF_r | ## Data preparation 1. Download WIDERFace datasets. 2. Download annotation files from [google drive](https://drive.google.com/file/d/1tU_IjyOwGQfGNUvZGwWWM4SwxKp2PUQ8/view?usp=sharing). ```shell cd data python3 train2yolo.py /path/to/original/widerface/train [/path/to/save/widerface/train] python3 val2yolo.py /path/to/original/widerface [/path/to/save/widerface/val] ``` ## Training ```shell CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models' ``` ## WIDERFace Evaluation ```shell python3 test_widerface.py --weights 'your test model' --img-size 640 cd widerface_evaluate python3 evaluation.py ``` #### Test  #### Landmark Visulization  First row: RetinaFace, 2nd row: YOLOv5m-Face **YOLO5Face was used in the 3rd place standard face recogntion track of the [ICCV2021 Masked Face Recognition Challenge](https://www.face-benchmark.org/challenge.html).** #### AXera demo https://github.com/AXERA-TECH/ax-samples/blob/main/examples/ax_yolov5s_face_steps.cc #### Android demo https://github.com/FeiGeChuanShu/ncnn_Android_face/tree/main/ncnn-android-yolov5_face #### OpenCV DNN demo https://github.com/hpc203/yolov5-face-landmarks-opencv-v2 #### ONNXRuntime/MNN/TNN/NCNN C++ demo https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolo5face.cpp https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolo5face.cpp https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolo5face.cpp https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ncnn/cv/ncnn_yolo5face.cpp #### References https://github.com/ultralytics/yolov5 https://github.com/Da