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Places 365
Classification
Scenario Recognition
|...
License: Unknown

Overview

The Places dataset is designed following principles of human visual cognition. Our goal is
to build a core of visual knowledge that can be used to train artificial systems for high-level
visual understanding tasks, such as scene context, object recognition, action and event prediction,
and theory-of-mind inference. The semantic categories of Places are defined by their function:
the labels represent the entry-level of an environment. To illustrate, the dataset has different
categories of bedrooms, or streets, etc, as one does not act the same way, and does not make
the same predictions of what can happen next, in a home bedroom, an hotel bedroom or a nursery.

In total, Places contains more than 10 million images comprising 400+ unique scene categories.
The dataset features 5000 to 30,000 training images per class, consistent with real-world
frequencies of occurrence. Using convolutional neural networks (CNN), Places dataset allows
learning of deep scene features for various scene recognition tasks, with the goal to establish
new state-of-the-art performances on scene-centric benchmarks. Here we provide the Places
Database and the trained CNNs for academic research and education purposes.

Citation

Please use the following citation when referencing the dataset:

@article{zhou2017places,
  title={Places: A 10 million Image Database for Scene Recognition},
  author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba,
Antonio},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  publisher={IEEE}
}
Data Summary
Type
Image,
Amount
10000K
Size
809.66GB
Provided by
MIT
Founded to accelerate the nation’s industrial revolution, MIT is profoundly American. Our community gains tremendous strength as a magnet for talent from around the world. Through teaching, research, and innovation, MIT’s exceptional community pursues its mission of service to the nation and the world.
| Amount 10000K | Size 809.66GB
Places 365
Classification
Scenario Recognition
License: Unknown

Overview

The Places dataset is designed following principles of human visual cognition. Our goal is
to build a core of visual knowledge that can be used to train artificial systems for high-level
visual understanding tasks, such as scene context, object recognition, action and event prediction,
and theory-of-mind inference. The semantic categories of Places are defined by their function:
the labels represent the entry-level of an environment. To illustrate, the dataset has different
categories of bedrooms, or streets, etc, as one does not act the same way, and does not make
the same predictions of what can happen next, in a home bedroom, an hotel bedroom or a nursery.

In total, Places contains more than 10 million images comprising 400+ unique scene categories.
The dataset features 5000 to 30,000 training images per class, consistent with real-world
frequencies of occurrence. Using convolutional neural networks (CNN), Places dataset allows
learning of deep scene features for various scene recognition tasks, with the goal to establish
new state-of-the-art performances on scene-centric benchmarks. Here we provide the Places
Database and the trained CNNs for academic research and education purposes.

Citation

Please use the following citation when referencing the dataset:

@article{zhou2017places,
  title={Places: A 10 million Image Database for Scene Recognition},
  author={Zhou, Bolei and Lapedriza, Agata and Khosla, Aditya and Oliva, Aude and Torralba,
Antonio},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2017},
  publisher={IEEE}
}
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