YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架

Official MegEngine implementation of CREStereo(CVPR 2022 Oral).

Efficient ML solution for long-tailed demands.

采用MegEngine实现的各种主流深度学习模型

Official MegEngine implementation of RepLKNet

ECCV2020 - Practical Deep Raw Image Denoising on Mobile Devices

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

Python package containing all custom layers used in Neural Networks (Compatible with PyTorch, TensorFlow and MegEngine)

MegEngine implementation of YOLOX

The official MegEngine implementation of the ECCV 2022 paper: Ghost-free High Dynamic Range Imaging with Context-aware Transformer

MegEngine到其他框架的转换器

This is the official implementation of the paper "Instance-conditional Knowledge Distillation for Object Detection", based on MegEngine and Pytorch.

The official MegEngine implementation of the ICCV 2021 paper: GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning

Official MegEngine implementation of ECCV2022 "D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution".

RAW-based blind denoising, 1st place in MegCup 2022 (Team Feedback)

MegEngine Official Documentation

OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration, ICCV 2021, MegEngine implementation.

Official implementation of the FST-Matching Model.

RAW-based blind denoising, 3rd place in MegCup 2022 (Team Feedforward)