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MIT Indoor Scene Recognition Computer Vision Project

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Indoor Scene Recognition

Examples of Images
From the official dataset page:
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 indoor scenes recognition problem we need a model that can exploit local and global discriminative information.


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


A. Quattoni, and A.Torralba. Recognizing Indoor Scenes. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.


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)

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Last Updated

3 months ago

Project Type





airport_inside, artstudio, auditorium, bakery, bar, bathroom, bedroom, bookstore, bowling, buffet, casino, children_room, church_inside, classroom, cloister, closet, clothingstore, computerroom, concert_hall, corridor, deli, dentaloffice, dining_room, elevator, fastfood_restaurant, florist, gameroom, garage, greenhouse, grocerystore, gym, hairsalon, hospitalroom, inside_bus, inside_subway, jewelleryshop, kindergarden, kitchen, laboratorywet, laundromat, library, livingroom, lobby, locker_room, mall, meeting_room, movietheater, museum, nursery, office, operating_room, pantry, poolinside, prisoncell, restaurant, restaurant_kitchen, shoeshop, stairscase, studiomusic, subway, toystore, trainstation, tv_studio, videostore, waitingroom, warehouse, winecellar