InsightFace-REST

This repository aims to provide convenient, easy deployable and scalable REST API for InsightFace face detection and recognition pipeline using FastAPI for serving and NVIDIA TensorRT for optimized inference.

Code is heavily based on API code in official DeepInsight InsightFace repository.

This repository provides source code for building face recognition REST API and converting models to ONNX and TensorRT using Docker.

Key features:

• Ready for deployment on NVIDIA GPU enabled systems using Docker and nvidia-docker2.
• Automatic model download at startup (using Google Drive).
• Up to 3x performance boost over MXNet inference with help of TensorRT optimizations, FP16 inference and batch inference of detected faces with ArcFace model.
• Support for older Retinaface detectors and MXNet based ArcFace models, as well as newer SCRFD detectors and PyTorch based recognition models (glintr100,w600k_r50, w600k_mbf).
• Up to 2x faster SCRFD postprocessing implementation.
• Batch inference supported both for recognition and detection models (currently SCRFD family only)
• Inference on CPU with ONNX-Runtime.

Contents

List of supported models

• Detection
• Recognition
• Other

Prerequesites

Running with Docker

API usage

• /extract

Work in progress

Known issues

Changelog

List of supported models:

Detection:

Model	Auto download	Batch inference	Detection (ms)	Inference (ms)	GPU-Util (%)	Source	ONNX File
retinaface_r50_v1	Yes*		12.3	8.4	26	official package	link
retinaface_mnet025_v1	Yes*		8.6	4.6	17	official package	link
retinaface_mnet025_v2	Yes*		8.8	4.9	17	official package	link
mnet_cov2	Yes*		8.7	4.6	18	mnet_cov2	link
centerface	Yes		10.6	3.5	19	Star-Clouds/CenterFace	link
scrfd_10g_bnkps	Yes*	Yes	3.3	2	16	SCRFD	link
scrfd_2.5g_bnkps	Yes*	Yes	2.2	1.1	13	SCRFD	link
scrfd_500m_bnkps	Yes*	Yes	1.9	0.8	13	SCRFD	link
scrfd_10g_gnkps	Yes*	Yes	3.3	2.2	17	SCRFD**	link
scrfd_2.5g_gnkps	Yes*	Yes	2.3	1.2	14	SCRFD**	link
scrfd_500m_gnkps	Yes*	Yes	2.1	1.3	14	SCRFD**	link
yolov5s-face	Yes*	Yes				yolov5-face	link
yolov5m-face	Yes*	Yes				yolov5-face	link
yolov5l-face	Yes*	Yes				yolov5-face	link

Note: Performance metrics measured on NVIDIA RTX2080 SUPER + Intel Core i7-5820K (3.3Ghz * 6 cores) for api/src/test_images/lumia.jpg with force_fp16=True, det_batch_size=1 and max_size=640,640.

Detection time include inference, pre- and postprocessing, but does not include image reading, decoding and resizing.

Note 2: SCRFD family models requires input image shape dividable by 32, i.e 640x640, 1024x768.

Recognition:

Model	Auto download	Batch inference	Inference b=1 (ms)	Inference b=64 (ms)	Source	ONNX File
arcface_r100_v1	Yes*	Yes	2.6	54.8	official package	link
r100-arcface-msfdrop75	No	Yes	-	-	SubCenter-ArcFace	None
r50-arcface-msfdrop75	No	Yes	-	-	SubCenter-ArcFace	None
glint360k_r100FC_1.0	No	Yes	-	-	Partial-FC	None
glint360k_r100FC_0.1	No	Yes	-	-	Partial-FC	None
glintr100	Yes*	Yes	2.6	54.7	official package	link
w600k_r50	Yes*	Yes	1.9	33.2	official package	link
w600k_mbf	Yes*	Yes	0.7	9.9	official package	link

Other:

Model	Auto download	Inference code	Source	ONNX File
genderage_v1	Yes*	Yes	official package	link
mask_detector	Yes*	Yes	Face-Mask-Detection	link
mask_detector112	Yes*	Yes	Face-Mask-Detection***	link
2d106det	No	No	coordinateReg	None

* - Models will be downloaded from Google Drive, which might be inaccessible in some regions like China.

** - custom models retrained for this repo. Original SCRFD models have bug (deepinsight/insightface#1518) with detecting large faces occupying >40% of image. These models are retrained with Group Normalization instead of Batch Normalization, which fixes bug, though at cost of some accuracy.

Models accuracy on WiderFace benchmark:

Model	Easy	Medium	Hard
scrfd_10g_gnkps	95.51	94.12	82.14
scrfd_2.5g_gnkps	93.57	91.70	76.08
scrfd_500m_gnkps	88.70	86.11	63.57

*** - custom model retrained for 112x112 input size to remove excessive resize operations and improve performance.

Requirements:

1. Docker
2. Nvidia-container-toolkit
3. Nvidia GPU drivers (470.x.x and above)

Running with Docker:

1. Clone repo.
2. Execute deploy_trt.sh from repo's root, edit settings if needed.
3. Go to http://localhost:18081 to access documentation and try API

If you have multiple GPU's with enough GPU memory you can try running multiple containers by editing n_gpu and n_workers parameters in deploy_trt.sh.

By default container is configured to build TRT engines without FP16 support, to enable it change value of force_fp16 to True in deploy_trt.sh. Keep in mind, that your GPU should support fast FP16 inference (NVIDIA GPUs of RTX20xx series and above, or server GPUs like TESLA P100, T4 etc. ).

Also if you want to test API in non-GPU environment you can run service with deploy_cpu.sh script. In this case ONNXRuntime will be used as inference backend.

For pure MXNet based version, without TensorRT support you can check depreciated v0.5.0 branch

API usage:

For example of API usage example please refer to demo_client.py code.

Work in progress:

• Add examples of indexing and searching faces (powered by Milvus).
• Add Triton Inference Server as execution backend

Known issues:

• When glintr100 recognition model is used genderage model returns wrong predictions.

Changelog:

2021-11-06 v0.7.0.0

Since a lot of updates happened since last release version is updated straight to v0.7.0.0

Comparing to previous release (v0.6.2.0) this release brings improved performance for SCRFD based detectors.

Here is performance comparison on GPU Nvidia RTX 2080 Super for scrfd_10g_gnkps detector paired with glintr100 recognition model (all tests are using src/api_trt/test_images/Stallone.jpg, 1 face per image):

Num workers	Client threads	FPS v0.6.2.0	FPS v0.7.0.0	Speed-up
1	1	56	103	83.9%
1	30	72	128	77.7%
6	30	145	179	23.4%

Additions:

• Added experimental support for msgpack serializer: helps reduce network traffic for embeddings for ~2x.
• Output names no longer required for detection models when building TRT engine - correct output order is now extracted from onnx models.
• Detection models now can be exported to TRT engine with batch size > 1 - inference code doesn't support it yet, though now they could be used in Triton Inference Server without issues.

Model Zoo:

• Added support for WebFace600k based recognition models from InsightFace repo: w600k_r50 and w600k_mbf
• Added md5 check for models to allow automatic re-download if models have changed.
• All scrfd based models now supports batch dimension/

Improvements:

• 1.5x-2x faster SCRFD re-implementation with Numba: 4.5 ms. vs 10 ms. for lumia.jpg example with scrfd_10g_gnkps and threshold = 0.3 (432 faces detected)).
• Move image normalization step to GPU with help of CuPy (4x lower data transfer from CPU to GPU, about 6% inference speedup, and some computations offloaded from CPU).
• 4.5x Faster face_align.norm_crop implementation with help of Numba and removal of unused computations. (Cropping 432 faces from lumia.jpg example tooks 45 ms. vs 205 ms.).
• Face crops are now extracted only when needed - when face data or embeddings are requested, improving detection only performance.
• Added Numba njit cache to reduce subsequent starts time.
• Logging timings rounded to ms for better readability.
• Minor refactoring

Fixes:

• Since gender/age estimation model is currently not supported exclude it from models preparing step.

2021-09-09 v0.6.2.0

REST-API

• Use async httpx lib for retrieving images by urls instead of urllib3 (which caused performance drop in multi-GPU environment under load due to excessive usage of opened sockets)
• Update draft Triton Infernce Server support to use CUDA shared memory.
• Minor refactoring for future change of project structure.

2021-08-07 v0.6.1.0

REST-API

• Dropped support of MXNet inference backend and automatic MXNet->ONNX models conversion, since all models are now distributed as ONNX by default.

2021-06-16 v0.6.0.0

REST-API

• Added support for newer InsightFace face detection SCRFD models: scrfd_500m_bnkps, scrfd_2.5g_bnkps, scrfd_10g_bnkps
• Released custom trained SCRFD models: scrfd_500m_gnkps, scrfd_2.5g_gnkps, scrfd_10g_gnkps
• Added support for newer InsightFace face recognition model glintr100
• Models auto download switched to Google Drive.
• Default models switched to glintr100 and scrfd_10g_gnkps

2021-05-08 v0.5.9.9

REST-API

• Added JPEG decoding using PyTurboJPEG - increased decoding speed for large JPEGs for about 2x.
• Support for batch inference of genderage model.
• Support for limiting number of faces for recognition using limit_faces parameter in extract endpoint.
• New /multipart/draw_detections endpoint, supporting image upload using multipart form data.
• Support for printing face sizes and scores on image by draw_detections endpoints.
• More verbose timings for extract endpoint for debug and logging purposes.

2021-03-27 v0.5.9.8

REST-API

• Added v2 output format: more verbose and more suitable for logging. Use 'api_ver':'2' in request body. In future versions this parameter will be moved to path, like /v2/extract, and will be default output format.

REST-API & conversion scripts:

• MXNet version in dockerfiles locked to 1.6.0, since version 1.8.0 causes missing libopenblas.0 exception.

2021-03-01 v0.5.9.7

REST-API & conversion scripts:

• Fixed issue with building TensorRT engine with batch > 1 and FP16 support, which caused FP32 inference instead of FP16.
• Moved to tensorrt:21.02 base image and removed workarounds for 20.12 image.
• Changed behaviour of force_fp16 flag. Now model with FP16 precision is build only when set to True. Otherwise FP32 will be used even on GPUs with fast FP16 support.

2021-03-01 v0.5.9.6

REST-API:

• Add flag embed_only to /extract endpoint. When set to true input images are processed as face crops, omitting detection phase. Expects 112x112 face crops.
• Added flag draw_landmarks to /draw_detections endpoint.

2021-02-13

REST-API:

• Added Dockerfile for CPU-only inference with ONNXRuntime backend.
• Added flag to return landmarks with /extract endpoint

2020-12-26

REST-API & conversion scripts:

• Added support for glint360k_r100FC_1.0 and glint360k_r100FC_0.1 face recognition models.

2020-12-26

REST-API:

• Base image updated to TensorRT:20.12-py3.
• Added temporary fixes for TensortRT 7.2.2 ONNX parsing.
• Added support for r50-arcface-msfdrop75 face recognition model.

Conversion scripts:

• Same updates as for REST-API

2020-12-06

REST-API:

• Added draft support for batch inference of ArcFace model.

Conversion scripts:

• Added draft support for batch inference of ArcFace model.

2020-11-20

REST API:

• Pure MXNet version removed from master branch.
• Added models bootstrapping before running workers, to prevent race condition for building TRT engine.
• Applied changes from conversion scripts (see below)

Conversion scripts:

• Reshape ONNX models in memory to prevent writing temp files.
• TRT engine builder now takes input name and shape, required for building optimization profiles, from ONNX model intself.

2020-11-07

Conversion scripts:

• Added support for building TensorRT engine with batch input.
• Added support for RetinaFaceAntiCov model (mnet_cov2, must be manually downloaded and unpacked to models/mxnet/mnet_cov2)

REST API:

• Added support for RetinaFaceAntiCov v2
• Added support for FP16 precision (force_fp16 flag in deploy_trt.sh)

2020-10-22

Conversion scripts:

• Minor refactoring

REST API:

• Added TensorRT version in src/api_trt
• Added Dockerfile (src/Dockerfile_trt)
• Added deployment script deploy_trt.sh
• Added Centerface detector

TensorRT version contains MXNet and ONNXRuntime compiled for CPU for testing and conversion purposes.

2020-10-16

Conversion scripts:

• Added conversion of MXNet models to ONNX using Python
• Added conversion of ONNX to TensorRT using Python
• Added demo inference scripts for ArcFace and Retinaface using ONNX and TensorRT backends

REST API:

• no changes

2020-09-28

• REST API code refactored to FastAPI
• Detection/Recognition code is now based on official Insightface Python package.
• TensorFlow MTCNN replaced with PyTorch version
• Added RetinaFace detector
• Added InsightFace gender/age detector
• Added support for GPU inference
• Resize function refactored for fixed image proportions (significant speed increase and memory usage optimization)