Car segmentation dataset. 基于卷积神经网络的语义分割.

Car segmentation dataset. Created by Gianmarco Russo. Contribute to jvyou/Car-segmentation development by creating an account on GitHub. Images in the Car Segmentation dataset have pixel-level instance segmentation annotations. Long-term autonomous driving. Mar 30, 2025 · The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform segmentation on car parts. Semantics segmentation of car parts like windows, wheels, etc 603 open source car-parts images plus a pre-trained Car Parts Segmentation model and API. A forward facing frame-by-frame pixel level semantic labeled dataset captured from a moving vehicle during continuous daylight driving through a crowded city street. Person, Cyclist, Car during day and night, fine time slots (sunrise, afternoon,) Detailed labeling of cars into their component parts. CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation. The dataset consists of 211 images with 3670 labeled objects belonging to 4 different classes including car, wheel, window, and other: lights. Download Dataset If you consider our dataset useful, please cite the following. You can use the dataset for computer vision tasks. 基于卷积神经网络的语义分割. com 2255 open source car-parts images plus a pre-trained car-seg model and API. Segmentation of Car Parts Car Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Downloads This dataset is for non-commercial use only. It includes diverse visuals with detailed annotations, suitable for automotive AI applications. See full list on github. The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform segmentation on car parts. Created by Person Detector UDA-Part is a comprehensive part segmentation dataset that provides detailed annotations on 3D CAD models, synthetic images, and real test images. Various weather conditions, including heavy rain, night, direct sunlight and snow. tjvxfki iwj uirjufsa dlqx vdrd gidajotc mdeats izhbs gdsesi jsd