Stars
InstantID: Zero-shot Identity-Preserving Generation in Seconds 🔥
[arXiv 2023] Set-of-Mark Prompting for GPT-4V and LMMs
Generative Flow Networks - GFlowNet
Robust recipes to align language models with human and AI preferences
Interactive demos describing the main concepts behind GFlowNets
A modular, easy to extend GFlowNet library
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"
Refine high-quality datasets and visual AI models
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Track emissions from Compute and recommend ways to reduce their impact on the environment.
Segment Anything in Medical Images
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.
Fast & Simple repository for pre-training and fine-tuning T5-style models
Official Pytorch Implementation of SegViT: Semantic Segmentation with Plain Vision Transformers
The agent engineering platform. Available in TypeScript!
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Graph Neural Network Library for PyTorch
Tools to connect to and interact with the Mila cluster
This is a list of the best cheat sheets I have found for software engineering, data science and machine learning.
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
OpenMMLab Detection Toolbox and Benchmark
A powerful and flexible machine learning platform for drug discovery
Code for reproducing results of "Unsupervised embeddings is all you need for protein function prediction"