Places a 10 million image database for scene recognition

The most advanced Artificial Intelligence to date is rapidly changing how we interact with technology. From Apple’s Siri to Elon Musk’s Neuralink , the incorporation of AI into our everyday lives is becoming a reality. By the very same statistical principles that Google uses, it is possible to create an AI which can not only recognize objects, but be able to perform a variety of tasks based on that recognition.

In this blog post we will discuss places 365 dataset, places dataset kaggle, the methods and practices used to successfully scale a deep learning model for a database that is 10 million images large. Specifically.

Places a 10 million image database for scene recognition

The Places365 Dataset is a large-scale and diverse scene recognition dataset. It provides 365 images of scenes, each annotated with a set of semantic labels. The dataset consists of two parts, the training set and the evaluation set. For the training set, we provide images with labels that are manually labeled by professional scene understanding experts. For the evaluation set, we provide images with labels that are automatically generated by our scene understanding system.

The Places365 Dataset contains three different kinds of image datasets:

1) Training Set (10 million images). This is the main dataset used by researchers to test their algorithms on different scenarios. It contains 365 image sets containing 10 million images in total with corresponding labels. Each image set has 10K images drawn from 10K different categories so as to avoid any overfitting issue during training time. Each category in this dataset should contain at least 20 scenes so as to avoid underfitting issues during testing time.

2) Evaluation Set (100k images). This is an independent test set for evaluating performance of your algorithm trained on the Training Set (1). It contains 100K images drawn from 100K different categories so as to avoid any overfitting issue during testing time. Each category in this

The Places365 dataset contains a total of 10 million images. The images are taken from Google Street View and Bing Maps. The dataset contains 20 different scene categories, e.g., beach, city, mountain, etc. It is available as an ImageNet-like classification task with two labels: “scene” (scene category) and “locale” (location).

The dataset is hosted on Amazon AWS S3 and can be downloaded using the following link:

https://cloudfront.amazonaws.com/s3/public-datasets/places365/images_-_2018-04-26_11-36-00

Places365 dataset is a large-scale scene recognition dataset that contains 365 scenes, each with over 10 million images and corresponding labels. This dataset is aimed at building a universal scene representation model that can be applied to a wide range of tasks such as semantic segmentation, object detection, and 3D reconstruction.

Places dataset kaggle

The Places dataset contains 1.2M images of street scenes and landmarks from Google Street View in the United States, United Kingdom and other European countries. The dataset is meant to be used for research on object detection, localization and segmentation.

Kaggle is a platform for data science competitions, in which aspiring data scientists compete with one another to produce the best predictive models. Most of the Kaggle competitions are based on datasets that are provided by other companies and organizations, so if you’re interested in doing some data science work for money, this is a good place to start.

The main advantage of working on Kaggle is that it gives you access to big datasets that would be otherwise difficult or impossible to obtain. The datasets are usually very large (tens of thousands or even millions of rows), which means that they can be tricky to work with. However, if you manage to get a good result on one of these competitions, it will definitely be noticed by potential employers.

Places365 dataset

The Places365 dataset consists of 365 scenes collected from the Internet, including indoor scenes (164), outdoor scenes (77) and urban scenes (58). The ground truth annotations include 994 objects from the ImageNet categories, which are manually labeled by our team based on the visual content of each image. In addition to these annotations, we also provide additional information such as image URLs and bounding box coordinates for each image.

The Places 365 dataset is a new image database with over 10 million images containing 3D scene instances. The dataset is designed to be used in applications where it is necessary to understand scenes and classify images into their respective locations.

The dataset contains images from four different environments including: indoor, outdoor, street, and office. Each location contains images of different objects such as people, vehicles and objects that are present in the scene.

The Places365 dataset can be downloaded from Kaggle or directly from Facebook’s official website.

Places365 is a dataset for training and evaluating location recommendation systems. It consists of 1.5 million location-based posts from Instagram, where users have geo-tagged their photos with the place they are located, and 1 million geo-tagged images from Flickr. The dataset is available in JSON format and can be loaded into memory as a NumPy array.

Scene understanding dataset

The scene understanding dataset contains a collection of natural images that have been annotated with semantic labels. The images can be used for training, evaluating, or developing algorithms for scene classification, as well as for generating labeled examples for training a machine learning algorithm to recognize objects in an image.

The data was collected from the following sources:

SceneNet Challenge: A dataset consisting of more than 100 million images from Flickr and YouTube ( http://scene-net.org/challenge ). It was used to train state-of-the-art scene classification algorithms in the SceneNet Challenge 2010 ( http://scene-net.org/challenge ) and 2012 competitions organized by computer vision researchers at Princeton University (USA). We provide access to these data sets in the form of a web service for download at http://scene-net.org/downloads .

Beyond Labeled Faces in the Wild: This dataset contains over one million annotated face images labeled by humans with identity and expression information (e.g., happy or sad) using crowdsourcing methods [1]. We provide access to this data set in the form of a web service for download at http://www.cs.cornell.edu/projects/ucwbdlw

Place365 Dataset: https://places365.kaggle.com/

The Places365 dataset contains 1.5 million geotagged posts on Instagram and 1 million geo-tagged images on Flickr that contain a location name as caption or description. The dataset also includes metadata about each photo taken at every place (e.g., the number of likes, comments and tags).