joshuacortez

joshuacortez's Stars

• Clay-foundation/model

  The Clay Foundation Model (in development)

  Language:Python34644

• ibis-project/ibis

  the portable Python dataframe library

  Language:Python5.1k591

• narwhals-dev/narwhals

  Lightweight and extensible compatibility layer between dataframe libraries!

  Language:Python43176

• koaning/embetter

  just a bunch of useful embeddings

  Language:Python45915

• prefix-dev/pixi

  Package management made easy

  Language:Rust3k168

• TutteInstitute/thisnotthat

  A visual labeling system implemented in Jupyter widgets.

  Language:Python14014

• Lightning-AI/pytorch-lightning

  Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

  Language:Python28.1k3.4k

• cvxgrp/pymde

  Minimum-distortion embedding with PyTorch

  Language:Python53627

• isciences/exactextract

  Fast and accurate raster zonal statistics

  Language:C++25833

• marimo-team/marimo

  A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git.

  Language:Python6.6k225

• MAIF/shapash

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

  Language:Jupyter Notebook2.7k331

• microsoft/satclip

  PyTorch implementation of SatCLIP

  Language:Python22622

• ChenDelong1999/RemoteCLIP

  🛰️ Official repository of paper "RemoteCLIP: A Vision Language Foundation Model for Remote Sensing" (IEEE TGRS)

  Language:Jupyter Notebook27718

• Renumics/spotlight

  Interactively explore unstructured datasets from your dataframe.

  Language:TypeScript1.1k83

• geopolars/geopolars

  Geospatial extensions for Polars

  Language:Rust60224

• pola-rs/polars

  Dataframes powered by a multithreaded, vectorized query engine, written in Rust

  Language:Rust29.6k1.9k

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.2k725

• thinkingmachines/ph-open-buildings

  Building footprints dataset for 12 Philippine cities. Supported by the Lacuna Fund.

  31

• microsoft/farmvibes-ai

  FarmVibes.AI: Multi-Modal GeoSpatial ML Models for Agriculture and Sustainability

  Language:Jupyter Notebook676117

• scikit-learn-contrib/MAPIE

  A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

  Language:Jupyter Notebook1.3k102

• earth-chris/elapid

  Species distribution modeling tools, including a python implementation of Maxent

  Language:Python507

• microsoft/ClimaX

  Foundation model for weather & climate

  Language:Python60683

• opengeos/segment-geospatial

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

  Language:Python3k302

• facebookresearch/dinov2

  PyTorch code and models for the DINOv2 self-supervised learning method.

  Language:Jupyter Notebook8.9k783

• Hedlen/awesome-segment-anything

  Tracking and collecting papers/projects/others related to Segment Anything.

  1.5k130

• yizhangliu/Awesome-Anything

  AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask

  2

• fudan-zvg/Semantic-Segment-Anything

  Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).

  Language:Python2.1k135

• pymc-labs/pymc-marketing

  Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

  Language:Python668184

• 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 Notebook47k5.6k

• moonshinelabs-ai/moonshine-remote-sensing

  Pretrained remote sensing models for the rest of us.

  Language:Python1291