hyaihjq's repositories
NanoDet-TensorRT
Nanodet TensorRT demo C++ version
ACELoss
Implementations of "Learning Euler's Elastica Model for Medical Image Segmentation"
active-boundary-loss
Official repository for Active Boundary Loss for Semantic Segmentation.
chineseocr_lite
超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M
EasyFace
A lightweight face recognition, high accuracy, real-time, cross-platform
EOD
Easy and Efficient Object Detector
labelbox-python
Labelbox Python Client
mmdeploy
MMDeploy is an open-source deep learning model deployment toolset
mnn-yolov5
Imported from https://gitee.com/techshoww/mnn-yolov5.
mobile-lpr
Mobile-LPR 是一个面向移动端的准商业级车牌识别库，以NCNN作为推理后端，使用DNN作为算法核心，支持多种车牌检测算法，支持车牌识别和车牌颜色识别。
multiyolov5
joint detection and semantic segmentation, based on ultralytics/yolov5,
nanodet
⚡Super fast and lightweight anchor-free object detection model. 🔥Only 1.8mb and run 97FPS on cellphone🔥
nanodet_rknn
nanodet_rknn on rk3399pro platform
OneNet
OneNet: End-to-End One-Stage Object Detection
PointPillars_MultiHead_40FPS
A REAL-TIME 3D detection network [Pointpillars] compiled by CUDA/TensorRT/C++.
pytorch-loss
label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
SegLoss
A collection of loss functions for medical image segmentation
Tengine
Tengine is a lite, high performance, modular inference engine for embedded device
TorchSSL
A PyTorch-based library for semi-supervised learning (NeurIPS'21)
Ultra_Fast_Lane_Detection_TensorRT
An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI to accelerate. our model support for int8, dynamic input and profiling. (TRT-hackathon2021)
yolov5-1
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
yolov5_ncnn_ubuntu
yolov5_ncnn in ununtu16.04
yolov7
🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
YOLOX-Slim
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
