Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

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  Dataset Structure





  Data Instances

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  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/samp3209/logo-dataset.

Logo-2K+:A Large-Scale Logo Dataset for Scalable Logo Classification The Logo-2K+ dataset contains a diverse range of logo classes from real-world logo images. It contains 167,140 images with 10 root categories and 2,341 leaf categories. The 10 different root categories are: Food, Clothes, Institution, Accessories, Transportation, Electronic, Necessities, Cosmetic, Leisure and Medical.

Dataset Card for "logo-dataset-v4"

This dataset consists of 803 pairs (x,y) (x, y) (x,y), where x x x is the image and y y y is the description of the image.The data have been manually collected and labelled, so the dataset is fully representative and free of rubbish.The logos in the dataset are minimalist, meeting modern design requirements and reflecting the company's industry.

  Disclaimer

This dataset is made available for academic research purposes only. All the… See the full description on the dataset page: https://huggingface.co/datasets/logo-wizard/modern-logo-dataset.

Dataset

This dataset was created by Amir Sanjani

Released under Database: Open Database, Contents: Database Contents

Contents

Cite the source of the dataset as:

List, Johann-Mattis, Thomas Mayer, Anselm Terhalle, and Matthias Urban (2014). CLICS: Database of Cross-Linguistic Colexifications. Marburg: Forschungszentrum Deutscher Sprachatlas (Version 1.0).

Line item details from purchase orders in the accounts payable subledger for Current Fiscal YearYou can view this data on the Boroughs Online Checkbook. https://msb.maps.arcgis.com/apps/opsdashboard/index.html#/12734a1be92a4a999c2349ce9dc13a2b

This dataset includes the identification and characterisation of sustainability related logos in food products in the EU market (though not limited to the region). The dataset was primarily produced based on the identification of logos present in food products in the Mintel Global New Products Database for the available EU countries (n=24) between January and December 2021. Other logos inventories were also included and duplicated excluded. Online verification for sustainability relevance and logos' characterisation conducted in logo owners' websites.

Here are a few use cases for this project:

1. Brand Monitoring: Companies can leverage this model in social media platforms to monitor their brand's reach, including analyzing advertisements effectiveness, spotting unauthorized usage of their branding materials, or tracking competitor's logo exposure.

2. Counterfeit Detection: The model could be integrated into e-commerce platforms to identify counterfeit products by detecting misrepresented logos, helping to maintain brand integrity and consumer trust.

3. Customer Behavior Analysis: Retail businesses might use the model in CCTV footage to understand customer behavior, observing which brand logos frequently attract customers, optimizing product placement, and designing more targeted marketing strategies.

4. Event Sponsorship Measurement: Sponsors of sports or entertainment events can employ this model to evaluate their brand exposure during those events by counting the number of times their logo appears in broadcast footage or photographs.

5. Automated Content Categorization: Media companies could use the model to categorize content based on the detected logos, allowing a faster search and sorting process in their databases.

Logo University

## Overview

Logo University is a dataset for object detection tasks - it contains Data annotations for 3,990 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).

These data are examples of the ShinyLogoJS (https://siyangming.shinyapps.io/ShinyLogoJS/) web app.

This dataset provides information about the number of properties, residents, and average property values for Logos Drive cross streets in Grand Junction, CO.

AutoTrain Dataset for project: logo-identifier-v2-short

  Dataset Description

This dataset has been automatically processed by AutoTrain for project logo-identifier-v2-short.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure







  Data Instances

A sample from this dataset looks as follows: [ { "image": "<100x100 RGB PIL image>", "target": 98 }, { "image": "<100x100 RGB PIL image>"… See the full description on the dataset page: https://huggingface.co/datasets/fsuarez/autotrain-data-logo-identifier-v2-short.

Context

We a group of students from a Data Science and Machine Learning Bootcamp in which we have decided to create an AI logo generator for our capstone project. We need huge amount of logos for training our deep learning model - DCGAN so we have scraped over 365k data from the Apple App Store to download the logos of the apps for training purposes.

GitHub Link of our Project - LOGO⅃ : https://github.com/jackychansky/Logo-Generator-by-DCGAN/blob/main/README.md

Content

We have used Rapid API to acquire the data we need and we have scraped over 10,000,000 apps infomation (link to the dataset: https://www.kaggle.com/fentyforte/365k-ios-apps-dataset) thus downloading all the logos from the logolink scraped from the dataset to create the current large logo dataset.

Cite the source dataset as

List, Johann-Mattis, Thomas Mayer, Anselm Terhalle, and Matthias Urban (2014). CLICS: Database of Cross-Linguistic Colexifications. Marburg: Forschungszentrum Deutscher Sprachatlas (Version 1.0).

Logos Tash Llc Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

🇺🇸 미국

Clothes Brand Logos Dataset

  Description:

The Clothes Brand Logos Dataset is a collection of images featuring logos from four popular clothing brands: Adidas, Allen Solly, Puma, and US Polo. The dataset includes both real and fake versions of these logos, providing a comprehensive resource for training and evaluating logo detection models.

  Categories:

Adidas Allen Solly Puma US Polo

  Dataset Structure:

Training Data:

The training data consists of… See the full description on the dataset page: https://huggingface.co/datasets/ravikagitha/ClothesBrandLogos.

Brands are based on corporate Trademark data which IPqwery aggregates across multiple Intellectual Property (IP) registries, including USPTO, CIPO, EUIPO and WIPO (USA, Canada, Europe). Brand data is updated weekly and can be delivered as a raw data feed, or customized feeds, or one-off reports. Full bibliographic data can be provided for each trademark record; such as filing date, registration date, NICE classification, Trademark name, type, etc. Ownership/entity relationships are mapped, as are stock tickers, ISIN, Crunchbase uuid, and Crunchbase domain. Logo/images are identified in the main record and compiled into a separate data file.

IPqwery's Brand data is available for public or private companies.

PredictLeads Key Customers Data provides real-time visibility into business relationships, partnerships, and vendor affiliations across industries. Using advanced web scraping and logo recognition technology, this dataset offers a competitive edge for B2B sales, lead scoring, and company data enrichment.

Use Cases: ✅ Lead Scoring – Prioritize leads based on their key customer network and market influence. ✅ Cold Outreach – Personalize outreach by referencing shared customers or partners. ✅ B2B Data Enrichment – Enhance CRM data with business relationship insights. ✅ B2B Sales – Understand company partnerships to craft tailored sales strategies. ✅ Competitive Analysis – Identify companies working with competitors to refine positioning.

Key API Attributes:

• id (string, UUID) – Unique identifier for the key customer relationship.
• category (string) – Type of relationship (e.g., vendor, partner, customer).
• source_url (string, URL) – Web page where the key customer was identified.
• context (string) – Extracted text describing the business relationship.
• first_seen_at (ISO 8601 date-time) – When the key customer was first detected.
• last_seen_at (ISO 8601 date-time) – Most recent detection of the key customer.
• company1 (object) – Primary company in the relationship, including domain and company name.
• company2 (object) – Associated key customer, vendor, or partner details.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/connections_dataset

This dataset is designed for detecting fake and real logos, providing a valuable resource for machine learning and computer vision applications. It comprises image files, which have been uniformly resized to 70x70 pixels to optimise computational efficiency during model training. An accompanying CSV file maps various labels and references to filenames, crucially categorising logos as either genuine or fake, thereby supporting the development of robust logo authentication systems.

Columns

• Filename: The name of the specific logo image file.
• Brand Name: The brand or company name associated with each logo.
• Path to the logo: The file path indicating where the logo image can be located.
• Label: This column serves as an identifier for the brand or company name.
• Tagline: The marketing slogan or tagline associated with the brand.
• Class specifying whether logo is genuine or fake: This crucial column classifies each logo as either genuine or fake.

Distribution

The dataset primarily includes a CSV file used for mapping information, alongside the image files themselves. All images have been pre-processed and resized to a consistent 70x70 pixel shape. The mapping file contains details relating to 825 unique values for various labels and taglines. Analysis indicates that approximately 67% of the logos are classified as fake, with the remaining 33% being genuine. The sources do not specify the total number of rows or the exact file size of the dataset.

Usage

This dataset is highly suitable for developing and training computer vision models focused on logo detection and authentication. Ideal applications include object detection, image classification, and brand protection, especially within the fields of artificial intelligence and machine learning. It provides essential data for creating systems that can distinguish between genuine and counterfeit brand imagery.

Coverage

The dataset is intended for global application. The sources do not provide specific geographic or demographic scope for the logos included, nor do they detail a time range for the data's collection. The dataset was listed on 11 June 2025.

License

CCO

Who Can Use It

• Data scientists and machine learning engineers looking to build and test models for detecting fake or genuine logos.
• Researchers in the fields of computer vision, image processing, and artificial intelligence.
• Organisations and businesses with an interest in brand monitoring, intellectual property protection, and counterfeit detection.

Dataset Name Suggestions

• Logo Authenticity Detector Dataset
• Genuine vs. Fake Logos Collection
• Brand Identity Verification Dataset
• Image-Based Logo Classification Data
• AI Logo Security Dataset

Attributes

Original Data Source: Fake/Real Logo Detection Dataset