Cross-platform, customizable ML solutions for live and streaming media.
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Cross-platform, customizable ML solutions for live and streaming media.
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
TranslatedA knowledge graph for mathematics (primary school to high school), English (grade 3 to high school), and junior high school physics: 1,644 knowledge points, 2,403 prerequisite edges, and 6,723 questions, aligned with the curriculum standards, with an adaptive prerequisite diagnostic.
📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.
My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot
Gaining advanced insights from Git repository history.
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
A Versatile and Accurate Monocular SLAM
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Biological foundation modeling from molecular to genome scale
TRI-ML Monocular Depth Estimation Repository
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
CubeSLAM: Monocular 3D Object Detection and SLAM
Ruby gem to calculate the similarity between texts using tf*idf
Official implementation of Joint Monocular 3D Vehicle Detection and Tracking (ICCV 2019)
Module for statistical learning, with a particular emphasis on time-dependent modelling
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
The repository host gives no description.
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data
Deep Reinforcement Learning for de-novo Drug Design
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.