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MIT Places 2
2D Classification
许可协议: Unknown

Overview

The Places2 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 Places2 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, Places2 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), Places2 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 Places2 Database and the trained CNNs for academic research and education purposes.

数据概要
数据格式
image,
数据量
10000K
文件大小
--
| 数据量 10000K | 大小 --
MIT Places 2
2D Classification
许可协议: Unknown

Overview

The Places2 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 Places2 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, Places2 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), Places2 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 Places2 Database and the trained CNNs for academic research and education purposes.

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