Vehicle Type Recognition
This is a vehicle image classification dataset containing images of four different types of vehicles: Car, Truck, Bus, and Motorcycle.
This is a vehicle image classification dataset containing images of four different types of vehicles: Car, Truck, Bus, and Motorcycle.
Vehicle Attributes and Emissions Dataset Vehicle Attributes and Emissions Dataset Datasets Vehicle Attributes and Emissions Dataset File Vehicle Attributes and
the Car Damage Dataset provides ample opportunities for researchers, data scientists, and industry experts to explore and innovate.
A Traffic Sign Dataset for classification is a collection of images or data representing various types of traffic signs, with the primary goal of training machine learning models to classify these signs correctly. This dataset is essential for developing and evaluating computer vision systems used in autonomous vehicles, driver assistance systems, and traffic management applications.
Using the YOLO algorithm to detect cars is a strong tool in AI and machine learning. But what really sets YOLO apart is its ability to quickly and accurately analyze images and videos in real-time.
Globose Technology Solutions excels in identifying vehicles with the Road Vehicle Images Dataset. Our smart technology helps improve traffic management and road safety
The Highway Traffic Videos Dataset is a valuable asset for anyone working with AI and traffic analysis. By choosing our dataset, you gain access to a wealth of information that can elevate your AI projects to new heights.
Semantic segmentation is a crucial computer vision technique used in the context of self-driving cars to understand and interpret the environment surrounding the vehicle. It involves the process of assigning semantic labels to each pixel in an image or a series of images, effectively segmenting the image into various meaningful objects or regions.
We invite the Kaggle community to engage with this dataset, utilize it in developing innovative solutions, and contribute to the enhancement of license plate detection technology.