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⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.

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⚡️FastDeploy

Installation | Documents | API Docs | Release Notes

⚡️FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with 📦out-of-the-box and unified experience, 🔚end-to-end optimization for over 🔥150+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, image segmentation, face detection, face recognition, keypoint detection, matting, OCR, NLP, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform.

Image Classification Object Detection Semantic Segmentation Potrait Segmentation
Image Matting Real-Time Matting OCR Face Alignment
Pose Estimation Behavior Recognition NLP Speech

input:Life was like a box
of chocolates, you never
know what you're
gonna get.

output:

📣 Recent Updates

Contents

🖥️ Server-side and Cloud Deployment

A Quick Start for Python SDK(click to expand)

Installation

Prerequisites
  • CUDA >= 11.2 、cuDNN >= 8.0 、 Python >= 3.6
  • OS: Linux x86_64/macOS/Windows 10
Install FastDeploy SDK with both CPU and GPU support
pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
Install FastDeploy SDK with only CPU support
pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

Python Inference Example

  • Prepare model and picture
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
  • Test inference results
# For deployment of GPU/TensorRT, please refer to examples/vision/detection/paddledetection/python
import cv2
import fastdeploy.vision as vision

im = cv2.imread("000000014439.jpg")
model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                 "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                 "ppyoloe_crn_l_300e_coco/infer_cfg.yml")

result = model.predict(im)
print(result)

vis_im = vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("vis_image.jpg", vis_im)
A Quick Start for C++ SDK(click to expand)

Installation

C++ Inference Example

  • Prepare models and pictures
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
  • Test inference results
// For GPU/TensorRT deployment, please refer to examples/vision/detection/paddledetection/cpp
#include "fastdeploy/vision.h"

int main(int argc, char* argv[]) {
  namespace vision = fastdeploy::vision;
  auto im = cv::imread("000000014439.jpg");
  auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                          "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                          "ppyoloe_crn_l_300e_coco/infer_cfg.yml");

  vision::DetectionResult res;
  model.Predict(&im, &res);

  auto vis_im = vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_image.jpg", vis_im);
  return 0;
 }

For more deployment models, please refer to Vision Model Deployment Examples .

Server-side and Cloud Model List🔥🔥🔥🔥🔥

Notes: ✅: already supported; ❔: to be supported in the future; N/A: Not Available;

Server-side and Cloud Model List(click to fold)
Task Model Linux Linux Win Win Mac Mac Linux Linux Linux Linux Linux
--- --- X86 CPU NVIDIA GPU Intel CPU NVIDIA GPU Intel CPU Arm CPU AArch64 CPU Phytium D2000CPU NVIDIA Jetson Graphcore IPU Serving
Classification PaddleClas/ResNet50
Classification TorchVison/ResNet
Classification ltralytics/YOLOv5Cls
Classification PaddleClas/PP-LCNet
Classification PaddleClas/PP-LCNetv2
Classification PaddleClas/EfficientNet
Classification PaddleClas/GhostNet
Classification PaddleClas/MobileNetV1
Classification PaddleClas/MobileNetV2
Classification PaddleClas/MobileNetV3
Classification PaddleClas/ShuffleNetV2
Classification PaddleClas/SqueeezeNetV1.1
Classification PaddleClas/Inceptionv3
Classification PaddleClas/PP-HGNet
Classification PaddleClas/SwinTransformer
Detection PaddleDetection/PP-YOLOE
Detection PaddleDetection/PicoDet
Detection PaddleDetection/YOLOX
Detection PaddleDetection/YOLOv3
Detection PaddleDetection/PP-YOLO
Detection PaddleDetection/PP-YOLOv2
Detection PaddleDetection/Faster-RCNN
Detection PaddleDetection/Mask-RCNN
Detection Megvii-BaseDetection/YOLOX
Detection WongKinYiu/YOLOv7
Detection WongKinYiu/YOLOv7end2end_trt
Detection WongKinYiu/YOLOv7end2end_ort_
Detection meituan/YOLOv6
Detection ultralytics/YOLOv5
Detection WongKinYiu/YOLOR
Detection WongKinYiu/ScaledYOLOv4
Detection ppogg/YOLOv5Lite
Detection RangiLyu/NanoDetPlus
KeyPoint PaddleDetection/TinyPose
KeyPoint PaddleDetection/PicoDet + TinyPose
HeadPose omasaht/headpose
Tracking PaddleDetection/PP-Tracking
OCR PaddleOCR/PP-OCRv2
OCR PaddleOCR/PP-OCRv3
Segmentation PaddleSeg/PP-LiteSeg
Segmentation PaddleSeg/PP-HumanSegLite
Segmentation PaddleSeg/HRNet
Segmentation PaddleSeg/PP-HumanSegServer
Segmentation PaddleSeg/Unet
Segmentation PaddleSeg/Deeplabv3
FaceDetection biubug6/RetinaFace
FaceDetection Linzaer/UltraFace
FaceDetection deepcam-cn/YOLOv5Face
FaceDetection insightface/SCRFD
FaceAlign Hsintao/PFLD
FaceAlign Single430FaceLandmark1000
FaceAlign jhb86253817/PIPNet
FaceRecognition insightface/ArcFace
FaceRecognition insightface/CosFace
FaceRecognition insightface/PartialFC
FaceRecognition insightface/VPL
Matting ZHKKKe/MODNet
Matting PeterL1n/RobustVideoMatting
Matting PaddleSeg/PP-Matting
Matting PaddleSeg/PP-HumanMatting
Matting PaddleSeg/ModNet
Video Super-Resolution PaddleGAN/BasicVSR
Video Super-Resolution PaddleGAN/EDVR
Video Super-Resolution PaddleGAN/PP-MSVSR
Information Extraction PaddleNLP/UIE
NLP PaddleNLP/ERNIE-3.0
Speech PaddleSpeech/PP-TTS --

📱 Mobile and Edge Device Deployment

Mobile and Edge Model List 🔥🔥🔥🔥

Mobile and Edge Model List(click to fold)
Task Model Size (MB) Linux Android Linux Linux Linux Linux TBD...
--- --- --- ARM CPU ARM CPU Rockchip-NPU
RK3568/RK3588
Rockchip-NPU
RV1109/RV1126/RK1808
Amlogic-NPU
A311D/S905D/C308X
NXP-NPU
i.MX 8M Plus
TBD...|
Classification PaddleClas/ResNet50 98
Classification PaddleClas/PP-LCNet 11.9 -- -- --
Classification PaddleClas/PP-LCNetv2 26.6 -- -- --
Classification PaddleClas/EfficientNet 31.4 -- -- --
Classification PaddleClas/GhostNet 20.8 -- -- --
Classification PaddleClas/MobileNetV1 17 -- -- --
Classification PaddleClas/MobileNetV2 14.2 -- -- --
Classification PaddleClas/MobileNetV3 22 --
Classification PaddleClas/ShuffleNetV2 9.2 -- -- --
Classification PaddleClas/SqueezeNetV1.1 5 -- -- --
Classification PaddleClas/Inceptionv3 95.5 -- -- --
Classification PaddleClas/PP-HGNet 59 -- -- --
Classification PaddleClas/SwinTransformer_224_win7 352.7 -- -- --
Detection PaddleDetection/PP-PicoDet_s_320_coco_lcnet 4.9 --
Face Detection deepinsight/SCRFD 2.5 -- -- -- --
Keypoint Detection PaddleDetection/PP-TinyPose 5.5 --
Segmentation PaddleSeg/PP-LiteSeg(STDC1) 32.2 -- -- -- --
Segmentation PaddleSeg/PP-HumanSeg-Lite 0.556 -- -- -- --
Segmentation PaddleSeg/HRNet-w18 38.7 -- -- -- --
Segmentation PaddleSeg/PP-HumanSeg 107.2 -- -- -- --
Segmentation PaddleSeg/Unet 53.7 -- -- -- --
Segmentation PaddleSeg/Deeplabv3 150
OCR PaddleOCR/PP-OCRv2 2.3+4.4 -- -- -- --
OCR PaddleOCR/PP-OCRv3 2.4+10.6 --

🌐 Browser-based Model List

Browser-based Model List(click to fold)
Task Model web_demo
--- --- Paddle.js
Detection FaceDetection
Detection ScrewDetection
Segmentation PaddleSeg/HumanSeg
Object Recognition GestureRecognition
Object Recognition ItemIdentification
OCR PaddleOCR/PP-OCRv3

Community

  • If you have any question or suggestion, please give us your valuable input via GitHub Issues
  • Join Us👬:
    • Slack:Join our Slack community and chat with other community members about ideas
    • WeChat:join our WeChat community and chat with other community members about ideas

Acknowledge

We sincerely appreciate the open-sourced capabilities in EasyEdge as we adopt it for the SDK generation and download in this project.

License

FastDeploy is provided under the Apache-2.0.

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⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.

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