!!INSTALL!! Download Places Dataset

PLACES provides model-based, population-level analysis and community estimates of health measures to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTAs) across the United States. Learn more about PLACES.

Convolutional neural networks (CNNs) trained on the Places2 Database can be used for scene recognition as well as generic deep scene features for visual recognition. We share the following pre-trained CNNs using Caffe and PyTorch. Github page for Places365-CNNs. List of the categories Scene hierarchy We are actively working on the version 2 of Places Database. If you need to use the legacy dataset (the original Places365 or Places205) urgently for research purposes, please sign this form, thank you.

Download Places Dataset

DOWNLOAD 🔥 https://bltlly.com/2y3Lvw 🔥

This is a table of properties listed in the National Register of Historic Places. It includes the:

reference number, property name, reference number, if it is restricted, state, county city, address, date listed, NHL designation date, architects, federal agency, other name, NPS Park Name, significance person(s), level of significance, and if the file has been scanned the there is a link to the file. You can also download this as an excel spreadsheet or click the "download dataset" below to get the file as a .csv file.

The milestone Overture 2023-07-26-alpha.0 release includes four unique data layers: Places of Interest (POIs), Buildings, Transportation Network, and Administrative Boundaries. These layers, which combine various sources of open map data, have been validated and conflated through a series of quality checks, and are released in the Overture Maps data schema which was released publicly in June 2023. The Places dataset includes data on over 59 million places worldwide and will be a foundational element of navigation, local search, and many other location-based applications. The datasets are available for download at

The Places205 dataset is a large scene-centric dataset with exactly 205 common scene categories. The dataset was created for the task of Scene Recognition. Places205 training dataset contains around 2,500,000 images, with a minimum of 5,000 and a maximum of 15,000 images per scene. The validation set comprises 100 images per category (totaling 20,500 images), and the testing set has 200 images per category (a grand total of 41,000 images).

The Places205 dataset is a large-scale scene-centric dataset with 41,000 images in 205 common scene categories. It has a validation set containing 100 images per category (a total of 20,500 images), and a testing set that contains 200 images per category.

You can stream the Places205 dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake. See detailed instructions on how to use the Places205 Dataset with PyTorch and TensorFlow in Python.

This page explains the concept of location and the different regionswhere data can be stored and processed. Pricing for storage and analysis is alsodefined by location of data and reservations. For more information about pricingfor locations, see BigQuery pricing. To learnhow to set the location for your dataset, see Create datasets. Forinformation about reservation locations, see Managing reservations in differentregions.

When loading data, querying data, or exporting data, BigQuerydetermines the location to run the job based on the datasets referenced inthe request. For example, if a query references a table in a dataset storedin the asia-northeast1 region, the query job will run in that region.

If a query does not reference any tables or other resources contained withindatasets, and no destination table is provided, the query job will run in theUS multi-region. To ensure that BigQuery queries are stored ina specific region or multi-region, specify the location with the job request toroute the query accordingly when using the global BigQueryendpoint. If you don't specify the location, queries may be temporarily storedin BigQuery router logs when the query is used for determiningthe processing location in BigQuery.

If the project has acapacity-based reservation in a region other than the US and the query doesnot reference any tables or other resources contained within datasets, then youmust explicitly specify the location of the capacity-based reservation whensubmitting the job. Capacity-based commitments are tied to a location, such asUS or EU. If you run a job outside the location of your capacity, pricingfor that job automatically shifts to on-demand pricing.

BigQuery returns an error if the specified location does not matchthe location of the datasets in the request. The location of every datasetinvolved in the request, including those read from and those written to, mustmatch the location of the job as inferred or specified.

Single-region locations don't match multi-region locations, even where thesingle-region location is contained within the multi-region location. Therefore,a query or job will fail if the location includes both a single-region locationand a multi-region location. For example, if a job's location is set to US,the job will fail if it references a dataset in us-central1. Likewise, a jobthat references one dataset in US and another dataset in us-central1 willfail. This is also true for JOIN statements with tables in both a region and amulti-region.

Likewise, when you run a job in a region, it only uses a reservation if thelocation of the job matches the location of a reservation. For example, if youassign a reservation to a project in the EU and run a query in that projecton a dataset located in the US, then that query is not run on your EUreservation. In the absence of any US reservation, the job is run ason-demand.

Single region bucket: If your BigQuery dataset is in the Warsaw (europe-central2) region, the corresponding Cloud Storage bucket must also be in the Warsaw region, or any Cloud Storage dual-region that includes Warsaw.If your BigQuery dataset is in the US multi-region,then Cloud Storage bucket can be in the US multi-region,the Iowa (us-central1) single region, or any dual-region that includes Iowa.Queries from any other single region fails, even if the bucket is in alocation that is contained within the multi-region of the dataset.For example, if the external tables are in the US multi-region and theCloud Storage bucket is in Oregon (us-west1), the job fails.

If your BigQuery dataset is in the EU multi-region,then Cloud Storage bucket can be in the EU multi-region,the Belgium (europe-west1) single region, or any dual-region that includes Belgium. Queries from any other single region fails, even if the bucket is in a location that is contained within the multi-region of the dataset. For example, if the external tables are in the EU multi-region and theCloud Storage bucket is in Warsaw (europe-central2), the job fails.

Dual-region bucket: If yourBigQuery dataset is in the Tokyo (asia-northeast1) region,the corresponding Cloud Storage bucket must be in the Tokyo region, orin a dual-region that includes Tokyo, like the ASIA1 dual-region.For more information, see Create a dual-region bucket.

If the Cloud Storage bucket is in the NAM4 dual-region or any dual-region thatincludes the Iowa(us-central1) region, the corresponding BigQuerydataset can be in the US multi-region or in the Iowa(us-central1).

If Cloud Storage bucket is in the EUR4 dual-region or any dual-region thatincludes the Belgium(europe-west1) region, the corresponding BigQuerydataset can be in the EU multi-region or in the Belgium(europe-west1).

Multi-region bucket: Using multi-regiondataset locations with multi-region Cloud Storage buckets isnot recommended for external tables, because external query performancedepends on minimal latency and optimal network bandwidth.

If your BigQuery dataset is in the US multi-region, thecorresponding Cloud Storage bucket must be in the US multi-region,in a dual-region that includes Iowa (us-central1), like the NAM4dual-region, or in a custom dual-region that includes Iowa (us-central1).

If your BigQuery dataset is in the EU multi-region, thecorresponding Cloud Storage bucket must be in the EU multi-region,in a dual-region that includes Belgium (europe-west1), like the EUR4dual-region, or in a custom dual-region that includes Belgium.

Dual-region bucket: If the Cloud Storage bucket that you want to load from is located in a dual-region bucket, then your BigQuery dataset can be located in regions that are included in the dual-region bucket, or in a multi-region that includes the dual-region. For example, if your Cloud Storage bucket is located in the EUR4 region, then your BigQuery dataset can be located in either the Finland (europe-north1) single-region, the Netherlands (europe-west4) single-region, or the EU multi-region.

Single region bucket: If your Cloud Storage bucket that you want to load from is in a single-region, your BigQuery dataset can be in the same single-region, or in the multi-region that includes the single-region. For example, if you Cloud Storage bucket is in the Finland (europe-north1) region, your BigQuery dataset can be in the Finland or the EU multi-region.

You can restrict the locations in which your datasets can be created by usingthe Organization Policy Service.For more information, see Restricting resourcelocations andResource locations supportedservices.

The Places365-Standard dataset contains 1.8 million train images from 365 scenecategories, which are used to train the Places365 CNNs. There are 50 images percategory in the validation set and 900 images per category in the testing set.

Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks. 2351a5e196