Stars
3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.
The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs
Various translations of OSTEP can be found here. Help the cause and contribute!
Tensors and differentiable operations (like TensorFlow) in Rust
[maintenance mode] A low-overhead Vulkan-like GPU API for Rust.
ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
Efficient GPU kernels for block-sparse matrix multiplication and convolution
Submanifold sparse convolutional networks
MNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
Datasets, Transforms and Models specific to Computer Vision
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explana…
Gram-Schmidt orthogonalization pytorch implementation.
Data and analysis for NA12878 genome on nanopore
Detecting methylation using signal-level features from Nanopore sequencing reads
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Flutter plug-in providing (a few) basic bindings to OpenCV-4.x. OpenCV methods implemented without the Core packages. WIP.
🙃 A delightful community-driven (with 2,500+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python…
State-of-the-Art Embeddings, Retrieval, and Reranking
🤗 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.
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
A curated list of deep learning resources for computer vision