Stars
tiktoken is a fast BPE tokeniser for use with OpenAI's models.
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Mitsuba 2: A Retargetable Forward and Inverse Renderer
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
PyTorch implementation of Pointnet2/Pointnet++
[CVPR 2022] Pre-Training 3D Point Cloud Transformers with Masked Point Modeling
arxiv-sanity lite: tag arxiv papers of interest get recommendations of similar papers in a nice UI using SVMs over tfidf feature vectors based on paper abstracts.
Official implementation of Implicit Behavioral Cloning, as described in our CoRL 2021 paper, see more at https://implicitbc.github.io/
awesome grounding: A curated list of research papers in visual grounding
WIT (Wikipedia-based Image Text) Dataset is a large multimodal multilingual dataset comprising 37M+ image-text sets with 11M+ unique images across 100+ languages.
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018
Official PaddlePaddle implementation of Paint Transformer
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Library for fast text representation and classification.
Dataset API for "PhraseCut: Language-based Image Segmentation in the Wild"
Collaborative documentation for and from Jean Zay users. Official Jean Zay documentation: http://www.idris.fr/eng/jean-zay/