Manuel-Z

Manuel-Z's Stars

• muskie82/MonoGS

  [CVPR'24 Highlight & Best Demo Award] Gaussian Splatting SLAM

  Language:Python1.2k106

• robodrive-24/toolkit

  Official Toolkit for The RoboDrive Challenge

  Language:Python719

• graphdeco-inria/gaussian-splatting

  Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

  Language:Python13.5k1.7k

• zhangyin1996/ISP-Teacher

  [AAAI24] ISP-Teacher: Image Signal Process with Disentanglement Regularization for Unsupervised Domain Adaptive Dark Object Detection

  Language:Python10

• AIR-THU/DAIR-V2X-Seq

  Language:Python13415

• mli/paper-reading

  深度学习经典、新论文逐段精读

  26.1k2.4k

• 594422814/TransformerTrack

  Language:Python25345

• microsoft/Swin-Transformer

  This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

  Language:Python13.6k2k

• MCG-NJU/AdaMixer

  [CVPR 2022 Oral] AdaMixer: A Fast-Converging Query-Based Object Detector

  Language:Jupyter Notebook23624

• zhaoxin94/awesome-domain-adaptation

  A collection of AWESOME things about domian adaptation

  5k867

• dongnana777/Bridging-Non-Co-occurrence

  Language:Python72

• lucidrains/vit-pytorch

  Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

  Language:Python19.5k3k

• facebookresearch/Ego4d

  Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset

  Language:Jupyter Notebook34047

• TianhaoFu/Awesome-3D-Object-Detection

  Papers, code and datasets about deep learning for 3D Object Detection.

  56154

• zhaozhengChen/ReCAM

  The official code of CVPR 2022 paper (Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation).

  Language:Python14019

• Manuel-Z/pytorch-image-models

  PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

  1

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  15.8k3k

• NVIDIA/flownet2-pytorch

  Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

  Language:Python3.1k739

• tensorflow/docs

  TensorFlow documentation

  Language:Jupyter Notebook6.1k5.3k

• o0o0o0o0o0o0o/image-processing-from-scratch

  This project contains some interesting image processing algorithms that were wrote in python and c++ from scratch.

  Language:C++1.1k303

• eastmountyxz/ImageProcessing-Python

  该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现，希望该资源对您有所帮助，一起加油。

  Language:Jupyter Notebook1.8k447

• weiaicunzai/awesome-image-classification

  A curated list of deep learning image classification papers and codes

  2.8k594

• scikit-image/skimage-tutorials

  skimage-tutorials: a collection of tutorials for the scikit-image package.

  Language:Jupyter Notebook492372

• facebookresearch/votenet

  Deep Hough Voting for 3D Object Detection in Point Clouds

  Language:Python1.7k376

• handspeaker/RandomForests

  a 1500 lines simple C++ implementation of RandomForests with detailed comments

  Language:C++10549

• parrt/random-forest-importances

  Code to compute permutation and drop-column importances in Python scikit-learn models

  Language:Jupyter Notebook596130

• doubleplusplus/incremental_decision_tree-CART-Random_Forest

  incremental CART decision tree, based on the hoeffding tree i.e. very fast decision tree (VFDT), which is proposed in this paper "Mining High-Speed Data Streams" by Domingos & Hulten (2000). And a newly extended model "Extremely Fast Decision Tree" (EFDT) by Manapragada, Webb & Salehi (2018). Added new implementation of Random Forest

  Language:Python9827

• LiteOS/LiteOS

  code and manual

  Language:C4.8k1.6k

• kjw0612/awesome-random-forest

  Random Forest - a curated list of resources regarding random forest

  1.2k333

• glouppe/phd-thesis

  Repository of my thesis "Understanding Random Forests"

  Language:TeX526151