MIT Indoor Scenes

MIT Indoor Scenes

This is the Original data provided by MIT Indoor Scenes.

Indoor scene recognition is a challenging open problem in high-level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the MIT Indoor Scenes recognition problem we need a model that can exploit local and global discriminative information.

Content

The database contains 67 Indoor categories and a total of 15620 images. The number of images varies across categories MIT Indoor Scenes, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.

1. Diverse Environments: The dataset includes images of various indoor settings like kitchens, bedrooms, and offices.
2. High-Quality Images: Features high-resolution images suitable for detailed analysis and computer vision tasks.
3. Large Scale: Contains thousands of images, Indoor Scenes providing a robust dataset for training and testing models.
4. Annotations Included: Each image is annotated with labels, aiding in supervised learning and accurate model training.
5. Widely Used: Recognized and utilized in numerous academic and commercial research projects, enhances its credibility and reliability.

Acknowledgments

Thanks to MIT Indoor Scenes
Thanks to Aude Oliva for helping to create the database of indoor scenes.
Funding for this research was provided by NSF Career Award (IIS 0747120)