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kmdr-cli

🧠 The CLI tool for learning commands from your terminal

knowledge-distillation-papers

knowledge distillation papers

learngit

learning-cpp

c++ learning

lee-slam-source

SLAM 开发学习资源与经验分享

lq-nets

LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks

lstr

This is an official repository of End-to-end Lane Shape Prediction with Transformers.

matools

Matpool Toolset

minkowskiengine

Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mmcv

OpenMMLab Computer Vision Foundation

mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

mobilenetv3

mobilenetv3 with pytorch，provide pre-train model

modern-cmake-for-cpp

《Modern CMake for C++》的非专业个人翻译

modern-cpp-tutorial

📚 Modern C++ Tutorial: C++11/14/17/20 On the Fly

multi-object-tracking-paper-code-list

multi-object-tracking-paper-list

Paper list and source code for multi-object-tracking

mvision

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

neural-networks-on-silicon

This is a collection of works on neural networks accelerators.

neuralsim

neuralsim: 3D surface reconstruction and simulation based on 3D neural rendering.

nn-cuda-example

Several simple examples for popular neural network toolkits calling custom CUDA operators.

nn_playground

Experimental keras implementation of novel neural network structures

nncf_pytorch

PyTorch*-based Neural Network Compression Framework for enhanced OpenVINO™ inference

nnfusion

A flexible and efficient deep neural network (DNN) compiler that generates high-performance executable from a DNN model description.

obj_det_progress_tracker

Object Detection Progress Tracker

objectron

Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes

onenet

OneNet: End-to-End One-Stage Object Detection

onnx-simplifier

Simplify your onnx model

onnx-surgery

openoccupancy

OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception