ParisStreetView-RandomMasks: Large-Scale Urban Image Inpainting Dataset with Random Irregular Masks
Description
# ParisStreetView-RandomMasks Dataset ParisStreetView-RandomMasks is a research-oriented image inpainting dataset containing 22,601 urban street-view images with synthetically generated irregular random masks and corresponding corrupted images. The dataset is intended for machine learning researchers and developers working in image restoration, scene reconstruction, generative AI, and computer vision. ## Contents - Original street-view images - Binary random masks - Corrupted images for inpainting tasks - Metadata annotations - Train-validation split support ## Key Characteristics - Large-scale dataset - Urban street environments - Diverse scene composition - Irregular free-form mask generation - Suitable for supervised inpainting training ## Research Domains - Image Inpainting - Generative AI - Deep Learning - Computer Vision - Diffusion Models - Scene Understanding ## Potential Applications - Missing region reconstruction - Visual content restoration - Generative image completion - Autonomous driving scene recovery - Benchmark evaluation for inpainting algorithms ## Dataset Preparation Random masks were algorithmically generated using free-form brush stroke simulation methods. Corrupted images were created by masking image regions using generated binary masks. ## Recommended Usage The dataset can be used for: - model training - benchmarking - transfer learning - evaluation of image inpainting architectures ## File Structure Dataset/ ├── images/ ├── masks/ ├── corrupted/ └── annotations.csv