THU-MIG/yolov10
Yolov10
YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying multiple objects in images and video streams. It functions as an end-to-end object detector that optimizes for high-speed deployment and detection precision.
The project is distinguished by an NMS-free detection architecture that predicts a single bounding box per object, eliminating the need for non-maximum suppression post-processing to reduce inference latency. It further optimizes for edge hardware through scalable weights and a quantization-friendly structure that facilitates conversion into low-precision formats.
The library covers a broad capability surface including custom vision model training, performance validation against benchmark datasets, and model format conversion. It also provides utilities for publishing fine-tuned weights to remote model hubs for versioning and sharing.
Features
- NMS-Free Object Detection - Provides an end-to-end detection architecture that eliminates non-maximum suppression to reduce inference latency.
- YOLO Object Detectors - Implements an end-to-end YOLO object detection architecture optimized for real-time identification and location of objects.
- Computer Vision Libraries - Ships a comprehensive computer vision library for training, validating, and exporting real-time detection models.
- Real-Time Object Detection - Enables real-time object detection in images and video streams with minimized post-processing latency.
- Detection Model Training - Provides tools to train object detection models on custom datasets using specified epochs and batch sizes.
- NMS-Free Object Detectors - Functions as an end-to-end object detector that eliminates NMS to maximize inference speed.
- Inference Latency Optimizers - Reduces computational time and overhead to optimize inference latency for real-time visual processing.
- Edge AI Model Deployment - Optimizes and deploys machine learning models for efficient execution on local and edge devices.
- Edge Hardware Optimizations - Provides edge hardware optimizations including quantization to ensure high-speed deployment on constrained devices.
- Custom Vision Training - Supports custom vision training by optimizing neural networks to recognize specific object classes.
- Neural Network Architectures - Utilizes a specialized neural network architecture designed to minimize inference latency through a reduced backbone.
- PyTorch Computer Vision Pipelines - Ships a complete PyTorch-based computer vision pipeline for training and optimizing object detection models.
- PyTorch Training Frameworks - Integrates PyTorch training frameworks to optimize model weights on custom datasets across scalable sizes.
- Efficient Neural Architectures - Employs a lightweight neural network structure as a high-performance backbone to reduce computational overhead.
- Real-Time Model Inference on Frames - Provides a framework designed for high-speed ML inference on live video frames for real-time detection.
- Spatial Attention Weighting - Integrates spatial attention weighting to emphasize relevant features and improve accuracy in complex scenes.
- NMS-Free Inference Workflows - Implements an inference workflow that predicts a single box per object, bypassing non-maximum suppression.
- Multi-Scale Feature Aggregation - Merges feature maps across diverse spatial scales to accurately identify objects of varying sizes.
- Label Assignment Strategies - Implements dual-label assignment strategies using hard and soft labels to optimize training convergence and detection precision.
- Detection Model Validation - Includes capabilities to evaluate detection accuracy using metrics like mean average precision against benchmark datasets.
- Precision Quantization - Provides precision quantization of weights to enable high-speed execution on edge hardware.
- Weight Quantization - Organizes model weights using quantization techniques to optimize execution for resource-constrained edge hardware.
- Model Weight Conversions - Provides utilities for converting model weights into optimized formats compatible with various hardware accelerators.
- 计算机视觉 - Real-time object detection with NMS-free training.
- Foundational AI References - Innovations in computer vision and object detection.
- CNN - Listed in the “CNN” section of the Ailia Models awesome list.
- Object Detection - Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.
