Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Core deep-learning frameworks and libraries for pretraining and distributed training.
Signals: deep-learning, neural-network, pytorch, tensorflow, jax, distributed-training, training, deepspeed
2,672 results
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[ICCV 2023] Tracking Anything with Decoupled Video Segmentation
[ACM MM 20 Oral] PyTorch implementation of Self-supervised Dance Video Synthesis Conditioned on Music
Pytorch implementation of "Genie: Generative Interactive Environments", Bruce et al. (2024).
Code for Motion Representations for Articulated Animation paper
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
[CVPR 2024] Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
A lightweight, scalable, and general framework for visual question answering research
PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"
Complete-Life-Cycle-of-a-Data-Science-Project
一个简单方便的目标检测框架(PyTorch环境可直接运行，不需要cuda编译)，支持Faster_RCNN、Cascade_RCNN、Yolo系列、SSD等经典网络。
Highly Accurate and Efficient Burn detection and Classification trained with Deep Learning Model
Deep Learning sample programs using PyTorch in C++
State-of-the-art Single Shot MultiBox Detector in Pure TensorFlow, QQ Group: 758790869
yolo master 本课程主要对yolo系列模型进行介绍，包括各版本模型的结构，进行的改进等，旨在帮助学习者们可以了解和掌握主要yolo模型的发展脉络，以期在各自的应用领域可以进一步创新并在自己的任务上达到较好的效果。
YOLO 3D Object Detection for Autonomous Driving Vehicle
Omega-AI is a Java-based deep learning framework that helps you quickly build neural networks for inference and training. Its engine supports automatic differentiation, multithreading, and GPU acceleration with CUDA and cuDNN.
YOLO Magic🪄 is an extension based on Ultralytics' YOLOv5, designed to provide more powerful functionality and simpler operations for visual tasks.
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.
Open-source Monocular Python HawkEye for Tennis
Tensorflow implementation of YOLO, including training and test phase.
Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
YOLOv2 in PyTorch
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
24,540 repositories in the index in total.
