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[NeurIPS 2025] YOLOv12: Attention-Centric Real-Time Object Detectors
The main features of sunsmarterjie/yolov12 are: Computer Vision, CNN, Object Detection.
Projects with overlapping indexed features include: thu-mig/yolov10 — YOLOv10 is a PyTorch computer vision library and real-time vision framework designed for locating and identifying… ultralytics/yolov5 — YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time… megvii-basedetection/yolox — YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO… meituan/yolov6 — YOLOv6 is a single-stage deep learning framework designed for industrial object detection. It serves as a computer… ultralytics/ultralytics — Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep… wongkinyiu/yolov7 — YOLOv7 is a PyTorch vision library and real-time inference engine designed for object detection, human pose…
YOLOv6 is a single-stage deep learning framework designed for industrial object detection. It serves as a computer vision model trainer for identifying and locating objects within images, as well as an instance segmentation tool that delineates precise object boundaries using masks. The project includes a specialized mobile inference optimizer and a model quantization toolkit. These components focus on reducing model size and resolution to improve execution speed on ARM-based chipsets and converting models to low-precision formats to decrease file size. The framework covers a broad range of
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
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in