Flags Detection

## Overview

Flags Detection is a dataset for object detection tasks - it contains Flags annotations for 1,439 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This data file contains details of various nations and their flags. In this file the fields are separated by spaces (not commas). With this data you can try things like predicting the religion of a country from its size and the colours in its flag.

10 attributes are numeric-valued. The remainder are either Boolean- or nominal-valued.

Attribute Information:

1. name: Name of the country concerned
2. landmass: 1=N.America, 2=S.America, 3=Europe, 4=Africa, 4=Asia, 6=Oceania
3. zone: Geographic quadrant, based on Greenwich and the Equator; 1=NE, 2=SE, 3=SW, 4=NW
4. area: in thousands of square km
5. population: in round millions
6. language: 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others
7. religion: 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others
8. bars: Number of vertical bars in the flag
9. stripes: Number of horizontal stripes in the flag
10. colours: Number of different colours in the flag
11. red: 0 if red absent, 1 if red present in the flag
12. green: same for green
13. blue: same for blue
14. gold: same for gold (also yellow)
15. white: same for white
16. black: same for black
17. orange: same for orange (also brown)
18. mainhue: predominant colour in the flag (tie-breaks decided by taking the topmost hue, if that fails then the most central hue, and if that fails the leftmost hue)
19. circles: Number of circles in the flag
20. crosses: Number of (upright) crosses
21. saltires: Number of diagonal crosses
22. quarters: Number of quartered sections
23. sunstars: Number of sun or star symbols
24. crescent: 1 if a crescent moon symbol present, else 0
25. triangle: 1 if any triangles present, 0 otherwise
26. icon: 1 if an inanimate image present (e.g., a boat), otherwise 0
27. animate: 1 if an animate image (e.g., an eagle, a tree, a human hand) present, 0 otherwise
28. text: 1 if any letters or writing on the flag (e.g., a motto or slogan), 0 otherwise
29. topleft: colour in the top-left corner (moving right to decide tie-breaks)
30. botright: Colour in the bottom-left corner (moving left to decide tie-breaks)

National Flag

## Overview

National Flag is a dataset for object detection tasks - it contains Flag annotations for 795 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).

European Flag Detection

## Overview

European Flag Detection is a dataset for object detection tasks - it contains Flags CSm6 annotations for 1,043 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

A practice dataset of images of flags of 224 countries collected by scraping the www. Contains 12000+ images in the training set, and ~6000 images in the test set. Manually scraped, high variability in terms of resolution, appearance, occlusion, etc. Good for practising computer vision algorithms such as for clustering, prediction.

Turkish Flag Detection

## Overview

Turkish Flag Detection is a dataset for object detection tasks - it contains Flags annotations for 495 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 [MIT license](https://creativecommons.org/licenses/MIT).

Checker Flag Detection

## Overview

Checker Flag Detection is a dataset for object detection tasks - it contains Flag annotations for 1,059 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).

World Nation Flags Dataset

  Dataset Description

This dataset contains images of flags (Feb 2025) for every sovereign nation in the world. It is designed for tasks such as image classification, flag recognition, and geographic education. It also include some less recognized Sovereign state, such as: Abkhazia, Cook_islands, Niue, Northern_Cyprus, Sahrawi_Arab_Democratic_Republic, Somaliland, Taiwan, Transnistria.

  Dataset Structure

The dataset is organized as… See the full description on the dataset page: https://huggingface.co/datasets/Huggbottle/WorldFlags-Every-Nation-Flag.

Banner Flag Detection

## Overview

Banner Flag Detection is a dataset for object detection tasks - it contains Banner 212F 2vRh IYMk annotations for 309 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).

Flag Detection Indivicuele Opdracht

## Overview

Flag Detection Indivicuele Opdracht is a dataset for object detection tasks - it contains Flags annotations for 538 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 Hugo huan

Released under Apache 2.0

Contents

South American Flags Dataset (YOLOv8 Format)

Created by Ishan Chauhan and Miilee Sharma

  Dataset Overview

This dataset contains labeled images of South American country flags intended for training object detection models using the YOLOv8 format. The annotations are structured for seamless integration with Ultralytics' YOLOv8 training pipeline.

  Contents

images/train/ – Training images
images/val/ – Validation images
images/test/ – Test images
labels/ –… See the full description on the dataset page: https://huggingface.co/datasets/7mgppp/south-american-flags.

Transgender flag data

To prevent false detection, you probably need to feed transgender flag detector some "placeholder" images that doesn't contain transgender flag, and label them with "no".

Indian Flag Detection

## Overview

Indian Flag Detection is a dataset for object detection tasks - it contains Flag annotations for 331 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).

Flag For Carbon Fixation Data Below Detection Limit measured via Uncategorized in . Part of dataset Gradients5-TN412 - 15N Nitrogen Fixation, 13C Carbon Fixation

Data were collected at the Flag Springs Complex on the Wayne E. Kirch Wildlife Management Area in Nevada to study the seasonal movements, habitat use, and survival of White River Spinedace. The dataset comprises information on PIT tagged White River Spinedace detected passively by remote antennas, along with habitat characteristics data. Length, weight, and species data were recorded during four bi-annual tagging events that occurred in June and November from November of 2020 to June of 2022. Passive detections of PIT tagged White River Spinedace were recorded at six locations throughout the flag springs complex. Meta data for each detection site are provided, including when antennas were operational. Water temperature data collected every hour at four sites in the study area are also included.

Flag

## Overview

Flag is a dataset for object detection tasks - it contains Objects annotations for 408 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 [MIT license](https://creativecommons.org/licenses/MIT).

Team Flag Market Outlook

According to our latest research, the global team flag market size reached USD 1.21 billion in 2024, reflecting robust demand across multiple end-user segments. The market is expected to expand at a CAGR of 5.7% from 2025 to 2033, with the forecasted market size projected to hit USD 2.01 billion by 2033. This growth is driven by the increasing popularity of sports and team-based activities worldwide, the rising trend of brand visibility at events, and the growing adoption of digitally printed and customized flags. As per our latest research, the sector's expansion is underpinned by evolving consumer preferences, technological advancements in flag manufacturing, and the proliferation of e-commerce platforms facilitating easier access to a wide variety of team flags.

One of the primary growth factors in the team flag market is the surge in global sporting events, both at amateur and professional levels. The increasing participation in sports, coupled with the rising number of tournaments and leagues, has significantly boosted the demand for team flags as symbols of support and identity. Fans, organizations, and sponsors are investing more in visually impactful merchandise, including flags, to foster a sense of belonging and team spirit. Additionally, the growing influence of social media and digital marketing has amplified the visibility of team flags, making them an essential component for event branding and fan engagement. This trend is particularly pronounced in regions with a strong sports culture, such as North America and Europe, where team flags are integral to the fan experience.

Another critical driver is the advancement in flag manufacturing technologies, notably digital printing and the use of durable, eco-friendly materials. Modern production techniques have enabled manufacturers to offer high-quality, customizable flags that cater to diverse consumer needs, from intricate designs to vibrant color schemes. The adoption of materials like polyester and nylon has improved the longevity and weather resistance of team flags, making them suitable for both indoor and outdoor use. Furthermore, the increasing awareness of environmental sustainability has led to the development of recyclable and biodegradable flag materials, attracting environmentally conscious consumers and organizations. These innovations are not only enhancing product quality but also broadening the market's appeal across different application areas.

The expansion of online retail channels has also played a pivotal role in the market's growth trajectory. The proliferation of e-commerce platforms and specialty online stores has made it easier for consumers and organizations to access a wide array of team flags, compare prices, and customize orders with minimal effort. Online distribution channels offer the added advantage of reaching a global audience, thus driving international sales and brand recognition. Additionally, the convenience of doorstep delivery, secure payment options, and user-friendly interfaces has further stimulated consumer interest and purchasing behavior. This digital shift is particularly beneficial for small and medium enterprises seeking to expand their market reach without significant investments in physical retail infrastructure.

From a regional perspective, the Asia Pacific market is emerging as a significant growth engine for the team flag industry. The region's burgeoning population, increasing disposable incomes, and rising enthusiasm for both traditional and emerging sports have fueled demand for team merchandise, including flags. Countries like China, India, and Australia are witnessing a surge in sporting events, school competitions, and corporate team-building activities, all of which contribute to higher flag consumption. Moreover, local manufacturers are leveraging cost-effective production techniques and expanding distribution networks to cater to the diverse needs of consumers across urban and rural areas. The Asia Pacific region is expected to register the fastest growth rate in the coming years, supported by favorable economic conditions and a youthful demographic.

Product Type Analysis

The team flag market is segmented by product type into printed flags, embroidered flags, appliqué flags, digital flags, and others. Printed flags remain the most popular category, accounting for a significant share of the market due to their affordability, versatility, an

Article Information

The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.

Please do cite the aforementioned article when using this dataset.

Abstract

The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.

ZIP Folder Content

The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.

To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.

This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.

Datasets' Content

Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.

Identified Key Features Within Bluetooth Dataset

Feature Meaning

btle.advertising_header BLE Advertising Packet Header

btle.advertising_header.ch_sel BLE Advertising Channel Selection Algorithm

btle.advertising_header.length BLE Advertising Length

btle.advertising_header.pdu_type BLE Advertising PDU Type

btle.advertising_header.randomized_rx BLE Advertising Rx Address

btle.advertising_header.randomized_tx BLE Advertising Tx Address

btle.advertising_header.rfu.1 Reserved For Future 1

btle.advertising_header.rfu.2 Reserved For Future 2

btle.advertising_header.rfu.3 Reserved For Future 3

btle.advertising_header.rfu.4 Reserved For Future 4

btle.control.instant Instant Value Within a BLE Control Packet

btle.crc.incorrect Incorrect CRC

btle.extended_advertising Advertiser Data Information

btle.extended_advertising.did Advertiser Data Identifier

btle.extended_advertising.sid Advertiser Set Identifier

btle.length BLE Length

frame.cap_len Frame Length Stored Into the Capture File

frame.interface_id Interface ID

frame.len Frame Length Wire

nordic_ble.board_id Board ID

nordic_ble.channel Channel Index

nordic_ble.crcok Indicates if CRC is Correct

nordic_ble.flags Flags

nordic_ble.packet_counter Packet Counter

nordic_ble.packet_time Packet time (start to end)

nordic_ble.phy PHY

nordic_ble.protover Protocol Version

Identified Key Features Within IP-Based Packets Dataset

Feature Meaning

http.content_length Length of content in an HTTP response

http.request HTTP request being made

http.response.code Sequential number of an HTTP response

http.response_number Sequential number of an HTTP response

http.time Time taken for an HTTP transaction

tcp.analysis.initial_rtt Initial round-trip time for TCP connection

tcp.connection.fin TCP connection termination with a FIN flag

tcp.connection.syn TCP connection initiation with SYN flag

tcp.connection.synack TCP connection establishment with SYN-ACK flags

tcp.flags.cwr Congestion Window Reduced flag in TCP

tcp.flags.ecn Explicit Congestion Notification flag in TCP

tcp.flags.fin FIN flag in TCP

tcp.flags.ns Nonce Sum flag in TCP

tcp.flags.res Reserved flags in TCP

tcp.flags.syn SYN flag in TCP

tcp.flags.urg Urgent flag in TCP

tcp.urgent_pointer Pointer to urgent data in TCP

ip.frag_offset Fragment offset in IP packets

eth.dst.ig Ethernet destination is in the internal network group

eth.src.ig Ethernet source is in the internal network group

eth.src.lg Ethernet source is in the local network group

eth.src_not_group Ethernet source is not in any network group

arp.isannouncement Indicates if an ARP message is an announcement

Identified Key Features Within IP-Based Flows Dataset

Feature Meaning

proto Transport layer protocol of the connection

service Identification of an application protocol

orig_bytes Originator payload bytes

resp_bytes Responder payload bytes

history Connection state history

orig_pkts Originator sent packets

resp_pkts Responder sent packets

flow_duration Length of the flow in seconds

fwd_pkts_tot Forward packets total

bwd_pkts_tot Backward packets total

fwd_data_pkts_tot Forward data packets total

bwd_data_pkts_tot Backward data packets total

fwd_pkts_per_sec Forward packets per second

bwd_pkts_per_sec Backward packets per second

flow_pkts_per_sec Flow packets per second

fwd_header_size Forward header bytes

bwd_header_size Backward header bytes

fwd_pkts_payload Forward payload bytes

bwd_pkts_payload Backward payload bytes

flow_pkts_payload Flow payload bytes

fwd_iat Forward inter-arrival time

bwd_iat Backward inter-arrival time

flow_iat Flow inter-arrival time

active Flow active duration

• ID - id value for tracks (presents only in the test file for the submitting purposes)
• Label - string valued observable denoting particle types. Can take values "Electron", "Muon", "Kaon", "Proton", "Pion" and "Ghost". This column is absent in the test file.
• FlagSpd - flag (0 or 1), if reconstructed track passes through Spd
• FlagPrs - flag (0 or 1), if reconstructed track passes through Prs
• FlagBrem - flag (0 or 1), if reconstructed track passes through Brem
• FlagEcal - flag (0 or 1), if reconstructed track passes through Ecal
• FlagHcal - flag (0 or 1), if reconstructed track passes through Hcal
• FlagRICH1 - flag (0 or 1), if reconstructed track passes through the first RICH detector
• FlagRICH2 - flag (0 or 1), if reconstructed track passes through the second RICH detector
• FlagMuon - flag (0 or 1), if reconstructed track passes through muon stations (Muon)
• SpdE - energy deposit associated to the track in the Spd
• PrsE - energy deposit associated to the track in the Prs
• EcalE - energy deposit associated to the track in the Hcal
• HcalE - energy deposit associated to the track in the Hcal
• PrsDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Prs
• BremDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Brem
• TrackP - particle momentum
• TrackPt - particle transverse momentum
• TrackNDoFSubdetector1 - number of degrees of freedom for track fit using hits in the tracking sub-detector1
• TrackQualitySubdetector1 - chi2 quality of the track fit using hits in the tracking sub-detector1
• TrackNDoFSubdetector2 - number of degrees of freedom for track fit using hits in the tracking sub-detector2
• TrackQualitySubdetector2 - chi2 quality of the track fit using hits in the tracking sub-detector2
• TrackNDoF - number of degrees of freedom for track fit using hits in all tracking sub-detectors
• TrackQualityPerNDoF - chi2 quality of the track fit per degree of freedom
• TrackDistanceToZ - distance between track and z-axis (beam axis)
• Calo2dFitQuality - quality of the 2d fit of the clusters in the calorimeter
• Calo3dFitQuality - quality of the 3d fit in the calorimeter with assumption that particle was electron
• EcalDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Ecal
• EcalDLLbeMuon - delta log-likelihood for a particle candidate to be muon using information from Ecal
• EcalShowerLongitudinalParameter - longitudinal parameter of Ecal shower
• HcalDLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from Hcal
• HcalDLLbeMuon - delta log-likelihood for a particle candidate to be using information from Hcal
• RICHpFlagElectron - flag (0 or 1) if momentum is greater than threshold for electrons to produce Cherenkov light
• RICHpFlagProton - flag (0 or 1) if momentum is greater than threshold for protons to produce Cherenkov light
• RICHpFlagPion - flag (0 or 1) if momentum is greater than threshold for pions to produce Cherenkov light
• RICHpFlagKaon - flag (0 or 1) if momentum is greater than threshold for kaons to produce Cherenkov light
• RICHpFlagMuon - flag (0 or 1) if momentum is greater than threshold for muons to produce Cherenkov light
• RICH_DLLbeBCK - delta log-likelihood for a particle candidate to be background using information from RICH
• RICH_DLLbeKaon - delta log-likelihood for a particle candidate to be kaon using information from RICH
• RICH_DLLbeElectron - delta log-likelihood for a particle candidate to be electron using information from RICH
• RICH_DLLbeMuon - delta log-likelihood for a particle candidate to be muon using information from RICH
• RICH_DLLbeProton - delta log-likelihood for a particle candidate to be proton using information from RICH
• MuonFlag - muon flag (is this track muon) which is determined from muon stations
• MuonLooseFlag muon flag (is this track muon) which is determined from muon stations using looser criteria
• MuonLLbeBCK - log-likelihood for a particle candidate to be not muon using information from muon stations
• MuonLLbeMuon - log-likelihood for a particle candidate to be muon using information from muon stations
• DLLelectron - delta log-likelihood for a particle candidate to be electron using information from all subdetectors
• DLLmuon - delta log-likelihood for a particle candidate to be muon using information from all subdetectors
• DLLkaon - delta log-likelihood for a particle candidate to be kaon using information from all subdetectors
• DLLproton - delta log-likelihood for a particle candidate to be proton using information from all subdetectors
• GhostProbability - probability for a particle candidate to be ghost track. This variable is an output of classification model used in the tracking algorithm.

Spd stands for Scintillating Pad Detector, Pr...