Showing 141-160 of 369 projects
Foundation Architecture for (M)LLMs, a powerful toolkit for building large language models.
A computer vision and machine learning library optimized for Arm CPUs and GPUs using SIMD technologies.
A PyTorch implementation of a fast and accurate 3D face alignment model for computer vision applications.
OpenMMLab's model deployment framework for optimizing and deploying computer vision models across various backends.
A comprehensive interview prep resource for AIGC, LLM, and AI-related engineering roles.
A computer vision-assisted tool to extract numerical data from plot images for data visualization and analysis.
OpenVSLAM is a versatile visual SLAM framework for computer vision and robotics applications.
A Python library for evaluating the performance of neural networks for object detection.
A collection of Python tutorials covering a wide range of topics from computer vision to network security.
An automatic model compression framework for developing smaller and faster AI applications.
A curated list of the latest AI research papers from 2021 with explanations and code samples
This GitHub repository contains projects for the Udacity Self-Driving Car Engineer Nanodegree, focusing on computer vision, deep learning, and autonomous vehicle technology.
A PyTorch tutorial for building an image captioning model using the Show, Attend, and Tell technique.
A curated collection of survey papers summarizing advances in deep learning, NLP, CV, and other ML domains.
A high-performance, auto-diff neural network library for 3D and 4D sparse tensor computations.
A real-time dense SLAM system with 3D reconstruction priors, built for computer vision and robotics applications.
Official implementation of the DETR object detection paper with improved DeNoising Anchor Boxes
An open-source computer vision framework for building machine vision applications in Python.
Deep learning software for colorizing black and white images with a few clicks.
An AI-powered tool for training supervised models without manual labeling, using foundation models and multimodal learning.
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