Lists (10)
Stars
The CausalRivers benchmark package. Evaluate your Causal Discovery method on real-world data.
A family of open-sourced Mixture-of-Experts (MoE) Large Language Models
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Expandable Datasets for Earth Observation
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
TorchSpatial offers a comprehensive framework and benchmark suite designed to advance Spatial Representation Learning (SRL).
Awesome things about domain generalization, including papers, code, etc.
A hub for various industry-specific schemas to be used with VLMs.
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
GCPR 2023 - DeViL: Decoding Vision features into Language
Zoomable, animated scatterplots in the browser that scales over a billion points
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Python and JavaScript bindings for calling the Earth Engine API.
Open-source TomoSAR package for PSDSInSAR and ComSAR algorithms
framework for large-scale SAR satellite data processing
A tutorial for Synthetic Aperture Radar Tomography
The repository contains lists of papers on causality and how relevant techniques are being used to further enhance deep learning era computer vision solutions.
A simple self-balancing two-wheeled robot using Arduino and controlled via a Bluetooth Android app.
Automatically create machine learning datasets from satellite images
Deep learning models for remote sensing applications
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Open source remote sensing dataset with benchmarks
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
PyTorch code and models for the DINOv2 self-supervised learning method.
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
iBOT 🤖: Image BERT Pre-Training with Online Tokenizer (ICLR 2022)