ayushdabra

awesome-deep-learning-papers

The most cited deep learning papers

gpt-migrate

Easily migrate your codebase from one framework or language to another.

segment-geospatial

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

USBuildingFootprints

Computer generated building footprints for the United States

open-sustainable-technology

A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources.

JoJoGAN

Official PyTorch repo for JoJoGAN: One Shot Face Stylization

keras-core

A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.

AdaptSegNet

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

STEGO

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

DAFormer

[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

RGBD_Semantic_Segmentation_PyTorch

[ECCV 2020] PyTorch Implementation of some RGBD Semantic Segmentation models.

DualGAN

DualGAN-tensorflow: tensorflow implementation of DualGAN

CBST

Code for <Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training> in ECCV18

awesome-vision-language-models-for-earth-observation

A curated list of awesome vision and language resources for earth observation.

FADA

(ECCV 2020) Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

DACS

Code from the paper "DACS: Domain Adaptation via Cross-domain Mixed Sampling"

Seg2Sat

Using StableDiffusion and ControlNet to generate synthetic aerial images

Label-Pixels

Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.

SyntheWorld

[WACV 2024] Official repository of SyntheWorld

GreenExp_R

This is a R toolkit and developer version package to estimate multidimensional aspects of greenness and nature exposure, such as availability, accessibility and visibility using various geospatial data and models

UniDA

Code for TGRS paper "Universal Domain Adaptation for Remote Sensing Image Scene Classification"

GLSS

Code for the paper 'Skin Segmentation from NIR Images using Unsupervised Domain Adaptation through Generative Latent Search'. Accepted in ECCV2020 (Spotlight). Preprint: https://arxiv.org/abs/2006.08696

UDA_for_RS

The implementations of "Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer"

DAST_segmentation

The source code of DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training

img2svg

GreenspaceAccessibility

ResiDualGAN-DRDG

Implementation of ResiDualGAN and DRDG

building_height

rainforest-segmentation

A comparison of deep learning segmentation models for deforestation monitoring using multispectral satellite imagery. Research paper and project work completed as part of LSE's ST456 Deep Learning course.

Large-Scale-Semantic-Segmentation-with-MAML

Memory Efficient Large Scale Semantic Segmentation with Model Agnostic Meta Learning with Tensorflow. It uses SegNet Architecture for classification.