Mapillary Vistas Dataset is a diverse street-level imagery dataset with pixel‑accurate and instance‑specific human annotations for understanding street scenes around the world.

The Mapillary Vistas Dataset is a large-scale street-level image dataset containing 25,000 high-resolution images annotated into 66/124 object categories of which 37/70 classes are instance-specific labels (v.1.2 and v2.0, respectively). Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects.Our dataset contains images from all around the world, captured at various Condition regarding weather, season and daytime. Images come from different imaging devices (mobile phones, tablets, action cameras, professional capturing rigs) and differently experienced photographers.

valentinamihalescu/mapillary-vistas-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The Vistas-NP dataset is an out-of-distribution detection dataset based on the Mapillary Vistas dataset. The original Vistas dataset consists of 18,000 training images and 2,000 validation images with 66 classes. In Vistas-NP the human classes are used as outliers due to their dispersion across scenes and visual diversity from other objects. The dataset is created by excluding all images with class person and the three rider classes to the test subset. Consequently, the dataset has 8,003 train images and 830 validation images. The test set contains 11,167.

Shrid-28/Mapillary-Vistas-with-augmentatiom dataset hosted on Hugging Face and contributed by the HF Datasets community

REAP is a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, the benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign.

This dataset contains synthetic images extracted from the CARLA simulator along with rich information extracted from the deferred rendering pipeline of Unreal Engine 4. The main purpose of this dataset is the training of the state-of-the-art image-to-image translation model proposed by Intel Labs "Enhancing Photorealism Enhancement" (EPE). Translation results derived from the model targeting the characteristics of Cityscapes, KITTI, and Mapillary Vistas are also provided. Computer vision-based models trained on these data are expected to perform better when deployed in the real world.