Search Results for "draw-io"

FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. The project provides implementations of FlashAttention, FlashAttention-2, and newer iterations optimized for modern GPU architectures such as NVIDIA Hopper and AMD accelerators. By improving both forward and backward pass efficiency, it enables training and inference of large language models with longer sequence lengths and higher throughput. ...

...It supports labeling tasks across images and videos and enables developers to prepare training datasets for tasks such as object detection, segmentation, classification, tracking, and pose estimation. The tool is built with an interactive graphical interface that simplifies annotation workflows and allows users to draw and edit labels directly on visual data. It also supports a wide range of export formats compatible with popular machine learning pipelines, making it easier to integrate with training frameworks.

We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us.

...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions.

...Convert keypoints to distance maps, extract pixels within bounding boxes from images, clip polygon to the image plane, etc. Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them). Draw heatmaps, segmentation maps, keypoints, bounding boxes, etc.

nunn is a collection of ML algorithms and related examples written in modern C++17.

Near the surface (even horizon) of a black hole, there is maximum information density in units of squared plancks (and some translation to qubits). Similarly, our imagination is the set of all possible things we can draw onto our most dense layer of visual cortex in electricity patterns. Bigger layers have more neurons to handle those possibilities. A Black Hole Cortex is a kind of visual cortex that has density of neuron layers similar to density at various radius from a black hole. What we think our eyes see, the imagination, is the densest and smallest layer. ...