High-quality Computer Vision Dataset to Train Your AI Model

Description: The "Car Key Point Identification Dataset" is designed for applications in visual entertainment and autonomous driving, featuring a collection of internet-collected images with a resolution of 640 x 512 pixels. This dataset employs bounding boxes to identify target cars and annotates 14 key points on each vehicle, including the four top points, the four lights, the four wheels, and the glass areas on the front and left side, providing detailed data for car modeling and recognition tasks.

Description: The "Damaged Board Parts Segmentation Dataset" is a niche collection tailored for the manufacturing sector, especially in wood and board production. It features internet-collected images with high resolutions ranging from 3024 x 4032 to 2048 x 5750 pixels. This dataset focuses on semantic segmentation of various types of board damage, including cracks, insect damage, and decay, aiding in quality control and manufacturing processes.

Description: 360 degrees walk around videos of cars with damages at a normal, steady pace with top and bottom always visible Damage: a scratch, dent, ding, or crack that is larger than a golf ball in length Outer Panel Damage: bumpers, fenders, quarter panels, doors, hoods, and trunks Location: Asia, US, Canada, and Europe

Recording Device: Mobile Camera

Recording Condition: Mixed Lighting Conditions

Description: 490+ cars and 3958 car photos with annotated images (along with metadata) of damaged cars. Covers all sides of the car (8 photos for each car) - Insurance Claim Process Use Cases.

Recording Device: Mobile Camera

Recording Condition: Mixed Lighting Conditions

Description: The "Industrial Metal Smelting Flame Classification Dataset" is designed for the industry sector, featuring a collection of internet-collected images of metal smelting flames, all with a resolution of 350 x 350 pixels. This dataset is dedicated to the classification of flame images into 10 categories, including overexposure, black smoke, fire mass, sparks, and various intensities of slag jumping and spatter, providing crucial data for monitoring and optimizing smelting processes.

Description: The "Machine Part Defects Segmentation Dataset" is designed for the manufacturing industry, consisting of internet-collected images, all with a resolution of 1000 x 1000 pixels. This dataset focuses on binary segmentation to identify white defects on machine parts, providing clear annotations that highlight areas of concern for quality control and inspection processes.

Description: The "Machine Parts Segmentation Dataset" is tailored for the manufacturing sector, featuring a collection of internet-collected images with a resolution of 2048 x 1536 pixels. This dataset is specialized in semantic segmentation, polygon, and key points annotations, focusing on contour annotation of machining positions within X-ray images of machine parts, facilitating precise analysis and inspection in manufacturing processes.

Description: The "Rail Line Labeling Dataset" is tailored for industrial applications, featuring a collection of internet-collected images with a resolution of 1920 x 1080 pixels. This dataset specializes in the detailed labeling of rail lines, including their turns and merges, using polygon annotations. Additionally, trains within these images are labeled with bounding boxes. The dataset specifically focuses on rail networks collected from Wuhan, providing a localized context for rail line analysis and train detection.