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

Examples and guides for using the Gemini API

Deep Learning Specialization by Andrew Ng on Coursera.

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

Segment Anything in High Quality [NeurIPS 2023]

Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Gallery of OSMnx tutorials, usage examples, and feature demonstrations.

A curated list of Best Artificial Intelligence Resources

Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".

statistics and classes for exploratory spatial data analysis

Integration of H3 with GeoPandas and Pandas

TerraMind is the first any-to-any generative foundation model for Earth Observation, built by IBM and ESA.

GeoWombat: Utilities for geospatial data

Road network extraction from satellite imagery, with speed and travel time estimates

CNN models for Solar Panel Detection and Segmentation in Aerial Images.

Code for running baseline models/experiments with the Fields of The World dataset

Some data analysis tools for working with historical PV solar time-series data sets.

OEDI Public Dataset Documentation

PyTorch EO aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike.

🌟 Code for ACL 2023 paper "GloFE: Gloss-Free End-to-End Sign Language Translation" (Oral)

Time-series

Automatic Detection of Solar Panels in High-Resolution Aerial Imagery.

A ML-oriented generic patcher for xarray data structures.

Code repository for creating and maintaining the Ground-Mounted Solar Energy in the United States (GM-SEUS) spatiotemporal dataset of solar arrays and panel-rows using existing datasets, machine le…

Things in Python, Pandas, GeoPandas and Jupyter that I've had to look up or weren't obvious in the documentation.