Stars
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
my little YOLO annotator with some nice automation
[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
Pythonic geodatabases for spatial data analysis
Python library for morphological cleaning of multiclass 2D numpy arrays (edge smoothing and island removal
Free and open source library for AI object detection and semantic segmentation in geospatial rasters. 🚀
Build your own Raster dynamic map tile services
The simplest, highest-throughput Python interface to S3, GCS & Azure Storage, powered by Rust.
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 th…
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
Elasticsearch backend for stac-fastapi with Opensearch support.
A tiny JavaScript library for calculating sun/moon positions and phases.
An extremely fast Python package and project manager, written in Rust.
A 360° media viewer for the modern web.
real time face swap and one-click video deepfake with only a single image
Python scripts for Metashape (former PhotoScan)
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
A curated list of resources focused on Machine Learning in Geospatial Data Science.
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.
Python bindings for H3, a hierarchical hexagonal geospatial indexing system
curated list of awesome tools, tutorials, code, helpful projects, links, stuff about Earth Observation and Geospatial stuff!
Integration of H3 with GeoPandas and Pandas
Official Implementation of CVPR24 highlight paper: Matching Anything by Segmenting Anything
Source code for paper WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion (CVPR 2024 Oral)
[CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Public Fused UDFs. Build any scale workflows with the Fused Python SDK and Workbench webapp, and integrate them into your stack with the Fused Hosted API.