MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
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,具备训练推理一体、全平台高效支持和动静结合的训练能力 3 大核心优势,可帮助企业与开发者大幅节省产品从实验室原型到工业部署的流程,真正实现小时级的转化能力。
Created by Megvii
#计算机科学#YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
翻译 - YOLOX是一款高性能的无锚YOLO,超越yolov3~v5,支持ONNX、TensorRT、ncnn、OpenVINO。
#计算机科学#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
#计算机科学#Official MegEngine implementation of CREStereo(CVPR 2022 Oral).
#计算机科学#Efficient ML solution for long-tailed demands.
翻译 - 满足长尾需求的高效机器学习解决方案。
采用MegEngine实现的各种主流深度学习模型
#计算机科学#Official MegEngine implementation of RepLKNet
ECCV2020 - Practical Deep Raw Image Denoising on Mobile Devices
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
#自然语言处理#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
Insertion sequences (Insertion Element) [https://en.wikipedia.org/wiki/Insertion_sequence] collected from ISfinder (https://isfinder.biotoul.fr/)
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)
#计算机科学#OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration, ICCV 2021, MegEngine implementation.
MegEngine Official Documentation
Official implementation of the FST-Matching Model.