Cell Phone Detection

## Overview

Cell Phone Detection is a dataset for object detection tasks - it contains Cell Phones annotations for 7,543 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Content

This database contains multiple images in different dimensions. Besides, the images were separated and categorized into two types: There is a cellphone (label = 1), there is no cellphone (label = 0). Thus, it is possible to build algorithms for the binary classification of objects or a computational model that allows locating the position of mobile phones in the image, and this will depend on your creativity to work with this dataset.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3457313%2F45a0ab95281bf9664a55406fbacfa2fe%2Fsave-cellphone.JPG?generation=1568096853341492&alt=media" alt="">

In 2019, according to the Polish IT departments, 37 percent responded that employees should have access to the employees' address book from their cell phones and employees' calendars (17 percent).

Dataset

This dataset was created by weicheng1011

Released under MIT

Contents

In 2022, the average data used per smartphone per month worldwide amounted to 15 gigabytes (GB). The source forecasts that this will increase almost four times reaching 46 GB per smartphone per month globally in 2028.

MRR Mobile Phone Inspection

## Overview

MRR Mobile Phone Inspection is a dataset for object detection tasks - it contains Mobile Phone's Defects annotations for 776 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Bilal Ahmad9593492

Contents

Dataset Card for Cellphones in the Wild

CITW is a small dataset that contains bounding box annotations of cellphones in images.

  Dataset Details







  Dataset Description

CITW (Cellphones in the Wild) is a collection of images that contain one or more cell phones in them, along with their corresponding bounding box annotations. CITW was distiled from COCO 2017, where only the images and annotations containing a cellphone were kept. The structure and… See the full description on the dataset page: https://huggingface.co/datasets/ridgerun-ai/citw-v0.1.

Snapshot img

Mobile Phone Market Size 2025-2029

The mobile phone market size is forecast to increase by USD 213.9 billion at a CAGR of 6.8% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of telecom services and the proliferation of mobile applications. Smartphones have become an integral part of daily life, with consumers using them for various purposes such as communication, entertainment, and productivity. The market is witnessing a surge in mobile phone sales from e-commerce platforms, making it more accessible to a wider audience. 
However, concerns regarding security and privacy with smartphone usage continue to pose challenges. In addition, the market is also witnessing trends such as the integration of mobile phones with data centers, mobile gaming consoles, and autonomous vehicles, providing new opportunities for market growth.

What will be the Size of the Mobile Phone Market During the Forecast Period?

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The emergence of 5G devices and test sites signifies the next phase of network development, promising faster data transfer rates and improved connectivity. Chipmakers play a crucial role in powering the smartphone market, ensuring the production of high-performance components. Handset design continues to evolve, focusing on sleeker forms, larger displays, and longer battery life. The market's size is substantial, with millions of units sold annually, reflecting the ubiquity of smartphones in today's digital world.

How is this Mobile Phone Industry segmented?

The mobile phone industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  Offline
  Online


Type

  Smartphone
  Feature phone


Geography

  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK


  North America

    Canada
    US


  Middle East and Africa



  South America

    Brazil

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period. The offline distribution channel segment in the US market caters to consumers who prefer a tangible shopping experience. Approximately two-thirds of consumers prefer purchasing mobile phones from physical retail stores, primarily due to the aging population. Offline distribution channels include independent retailers, multi-brand stores, and exclusive brand outlets. Personal interaction and the ability to test and compare devices before purchasing are significant advantages of offline retail.

Telecom infrastructure development, including 5G technology, enhances the offline buying experience by enabling instant device demos and showcasing the latest AI-powered smartphones. The integration of IoT and e-commerce platforms in offline stores further broadens the shopping experience. Semiconductor shortages and increasing mobile phone production through initiatives like Production-linked Incentives (PLI) continue to fuel the demand for mobile handsets.

Get a glance at the share of various segments. Request Free Sample

The offline segment was valued at USD 372.00 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 54% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in APAC has experienced substantial growth, with major contributors being China, Japan, India, South Korea, and Indonesia. The expanding urban population and rising disposable income have fueled the demand for smartphones. Telecom infrastructure development and the introduction of budget-centric devices are key growth drivers. Established manufacturers have set up production facilities in China, Taiwan, South Korea, Japan, and India. The market is further propelled by technological advancements such as 5G technology, artificial intelligence, and IoT integration. Semiconductor components, e-commerce, and m-commerce are significant sectors driving market expansion. Consumers increasingly adopt smartphones for digital information access, trade activities, and entertainment.

Mid-range smartphones and 5G devices are popular choices, with chipmakers addressing the semiconductor shortage. Smartphone manufacturers prioritize handset design, Android operating system, and application developers for in-app purchases and IoT applications. 5G deployment and economic development are ongoing, with security architecture, eID, and retailers adapting to the digital society.

Market Dynamics

The smar

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

1. YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.
2. H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59.
   The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License
Creative Commons Attribution 4.0 International.

Related datasets

• Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612
• Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

16878 Global import shipment records of Mobile Phone with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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Phone Defect Detection

## Overview

Phone Defect Detection is a dataset for object detection tasks - it contains Phone annotations for 210 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

4038 Global import shipment records of Cell Phone with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Brazil Number of Cell Phone User: Year of Studies: Southeast: Female: 8 to 10 Years data was reported at 5,396.613 Person th in 2017. This records a decrease from the previous number of 5,401.479 Person th for 2016. Brazil Number of Cell Phone User: Year of Studies: Southeast: Female: 8 to 10 Years data is updated yearly, averaging 5,399.046 Person th from Dec 2016 to 2017, with 2 observations. The data reached an all-time high of 5,401.479 Person th in 2016 and a record low of 5,396.613 Person th in 2017. Brazil Number of Cell Phone User: Year of Studies: Southeast: Female: 8 to 10 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TB012: Number of Cell Phone User: by Years of Studies.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This statistic shows a ranking of the estimated worldwide number of mobile cellular subscriptions per 100 inhabitants in 2020, differentiated by country. Included are only subscriptions that also allow voice communication over the Public Switched Telephone Network (PSTN). Pure data and M2M (machine-to-machine) connections are excluded.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Brazil Number of Cell Phone User: Year of Studies: South: Female: 4 to 7 Years data was reported at 2,679.004 Person th in 2017. This records an increase from the previous number of 2,509.822 Person th for 2016. Brazil Number of Cell Phone User: Year of Studies: South: Female: 4 to 7 Years data is updated yearly, averaging 2,594.413 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 2,679.004 Person th in 2017 and a record low of 2,509.822 Person th in 2016. Brazil Number of Cell Phone User: Year of Studies: South: Female: 4 to 7 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TB012: Number of Cell Phone User: by Years of Studies.

Brazil Number of Cell Phone User: Year of Studies: 11 to 14 Years data was reported at 52,223.235 Person th in 2017. This records an increase from the previous number of 50,512.885 Person th for 2016. Brazil Number of Cell Phone User: Year of Studies: 11 to 14 Years data is updated yearly, averaging 51,368.060 Person th from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 52,223.235 Person th in 2017 and a record low of 50,512.885 Person th in 2016. Brazil Number of Cell Phone User: Year of Studies: 11 to 14 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TB012: Number of Cell Phone User: by Years of Studies.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming 29 gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.