PadenU

PadenU's Stars

• Homebrew/brew

  🍺 The missing package manager for macOS (or Linux)

  Language:Ruby42.2k9.9k

• henrypp/memreduct

  Lightweight real-time memory management application to monitor and clean system memory on your computer.

  Language:C6.5k459

• open-afc-project/openafc

  Language:C++144

• GuangLun2000/GuangLun2000.github.io

  (Minimalism Style) Powered by Jekyll, based on the Minimal Mistakes theme and Jason Ansel's website

  Language:CSS595755

• ZJU-FAST-Lab/ego-planner-swarm

  An efficient single/multi-agent trajectory planner for multicopters.

  Language:C++1.3k241

• LMissher/STWave

  [ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

  Language:Python817

• jswang/stgat_traffic_prediction

  This repository provides an open source implementation of the Spatio-Temporal GAT introduced by Zhang et al in "Spatial-Temporal Graph Attention Networks:A Deep Learning Approach for Traffic Forecasting" https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8903252

  Language:Python8521

• zhengchuanpan/GMAN

  GMAN: A Graph Multi-Attention Network for Traffic Prediction (GMAN, https://fanxlxmu.github.io/publication/aaai2020/) was accepted by AAAI-2020.

  Language:Python430109

• gordicaleksa/pytorch-GAT

  My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

  Language:Jupyter Notebook2.5k334

• Cartus/AGGCN

  Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

  Language:Python43488

• guoshnBJTU/ASTGCN-2019-pytorch

  Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version

  Language:Python624158

• open-mmlab/mmdetection

  OpenMMLab Detection Toolbox and Benchmark

  Language:Python30.1k9.5k

• SysCV/sam-hq

  Segment Anything in High Quality [NeurIPS 2023]

  Language:Jupyter Notebook3.8k228

• massimilianoviola/semantic-segmentation-cityscapes

  Semantic Segmentation on Cityscapes using Segmentation Models Pytorch

  Language:Python93

• LikeLy-Journey/SegmenTron

  Support PointRend, Fast_SCNN, HRNet, Deeplabv3_plus(xception, resnet, mobilenet), ContextNet, FPENet, DABNet, EdaNet, ENet, Espnetv2, RefineNet, UNet, DANet, HRNet, DFANet, HardNet, LedNet, OCNet, EncNet, DuNet, CGNet, CCNet, BiSeNet, PSPNet, ICNet, FCN, deeplab)

  Language:Python708163

• CoinCheung/DeepLab-v3-plus-cityscapes

  mIOU=80.02 on cityscapes. My implementation of deeplabv3+ (also know as 'Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation' based on the dataset of cityscapes).

  Language:Python17826

• GeoDI-Lab/HyGCN

  Language:Python201

• IceClear/StableSR

  [IJCV2024] Exploiting Diffusion Prior for Real-World Image Super-Resolution

  Language:Python2.3k150

• XPixelGroup/DiffBIR

  [ECCV 2024] codes of DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

  Language:Python3.5k297

• groupgets/LeptonModule

  Code for getting started with the FLIR Lepton breakout board

  Language:C319198

• XPixelGroup/HAT

  CVPR2023 - Activating More Pixels in Image Super-Resolution Transformer Arxiv - HAT: Hybrid Attention Transformer for Image Restoration

  Language:Python1.3k158

• goldbattle/pytorch_unet

  PyTorch U-Net on Cityscapes Dataset

  Language:Python5111

• hitachinsk/SAMed

  The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"

  Language:Python50948

• ultralytics/ultralytics

  Ultralytics YOLO11 🚀

  Language:Python35.7k6.9k

• CASIA-IVA-Lab/FastSAM

  Fast Segment Anything

  Language:Python7.7k713

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook48.6k5.7k

• shaunyuan22/CFINet

  The official implementation for ICCV'23 paper "Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning"

  Language:Python1428

• shaunyuan22/SODA-mmrotate

  SODA-A Small Object Detection Toolbox and Benchmark

  Language:Python397

• UX-Decoder/Semantic-SAM

  [ECCV 2024] Official implementation of the paper "Semantic-SAM: Segment and Recognize Anything at Any Granularity"

  Language:Python2.5k124

• IDEA-Research/Grounded-Segment-Anything

  Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

  Language:Jupyter Notebook15.6k1.4k