The simplest, fastest repository for training/finetuning medium-sized GPTs.

PyTorch implementations of KMeans, Soft-KMeans and Constrained-KMeans which can be run on GPU and work on (mini-)batches of data.

Implementation of Maskomaly: Zero-shot Anomaly Segmentation

Complete* list of autonomous driving related datasets

density peak clustering algorithm implement by python sklearn manner

A template for starting reproducible Python machine-learning projects with hardware acceleration. Find an example at https://github.com/CLAIRE-Labo/no-representation-no-trust

Python library for the differentiable hypergeometric distribution

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

Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.

Open Set Semantic Segmentation

The fastest way to develop full-stack web apps with React & Node.js.

Official code for "Decoupling Zero-Shot Semantic Segmentation"

[ICLR 2022] Open-World Semi-Supervised Learning

Release for Improved Denoising Diffusion Probabilistic Models

Noise Conditional Score Networks (NeurIPS 2019, Oral)

A latent text-to-image diffusion model

ICCV2021 - training a post-hoc lightweight GAN-discriminator for open-set recognition

Code and data for our ICCV 2021 paper "Do Different Deep Metric Learning Losses Lead to Similar Learned Features?"

Code release for ConvNeXt model

🔥 The most advanced open-source online code execution system in the world.

A clean and readable Pytorch implementation of CycleGAN

Source code to reproduce results from Panoptic Swiftnet paper.

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