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followers: 63.0 following: 114.0 repos: 932.0 gists: 16.0

Name: Curtis Yung

Type: User

Curtis Yung's Projects

segformer_segmentation

Semantic Segmentation with SegFormer on Drone Dataset.

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.

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

segment-geospatial

A Python package for segmenting geospatial data with the Segment Anything Model (SAM)

segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

semantic-segmentation-of-landcover.ai-dataset

An implementation of Deeplabv3plus in TensorFlow2 for semantic land cover segmentation

semantic-segmentation-pytorch

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

sensaturban

🔥Urban-scale point cloud dataset (CVPR 2021 & IJCV 2022)

serverless_course

Udemy course

setup-ipsec-vpn

Scripts to build your own IPsec VPN server, with IPsec/L2TP, Cisco IPsec and IKEv2

sghr

[CVPR 2023] Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting

shap

A game theoretic approach to explain the output of any machine learning model.

sheetjs

📗 SheetJS Spreadsheet Data Toolkit -- New home https://git.sheetjs.com/SheetJS/sheetjs

shell_gpt

A command-line productivity tool powered by AI large language models like GPT-4, will help you accomplish your tasks faster and more efficiently.

ship-detection

Detecting ships from the satellite images using the YOLO algorithm

ship-detection-in-satellite-images

The project aims to detect ships in million pixels satellite images using different object detection algorithms. This makes use of various libraries and wrappers for object detection frameworks such as UNET, YOLO, Mask R-CNN, SSD, Faster R-CNN, RETINA-NET etc which might help beginners get started with object detection and computer vision easily.