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.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

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

Comprehensive dataset containing 12 verified Logos locations in Mexico with complete contact information, ratings, reviews, and location data.

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).

The logos have been resized to a uniform shape of 70x70 to make it less demanding on computational resources and model complexity when training. Though the original files can be found on one of my github repos, the link can be found below. GitHub Repo. The code used to mine and process the data can also be found on the repo.

The dataset comprises a total of 510 logo images categorized into five classes, representing different brands: Gojek, Grab, Uniqlo, Miniso, and CircleCI. Specifically, the dataset is divided into 83 training images and 19 testing images. Each image is standardized to a resolution of 300x300 pixels in PNG format. Additionally, the dataset includes a CSV file that labels each image as either positive (original) or negative (forged), which is crucial for tasks involving image verification or detection of counterfeits.

This dataset is particularly suited for applications in machine learning models that employ similarity metrics or triplet loss functions, which are common in tasks such as image comparison and identity verification. The data structure and labeling facilitate training models to discern subtle differences between genuine and forged logos, which is vital in the field of trademark protection.

Researchers interested in utilizing this dataset for academic purposes, particularly in studies involving data augmentation techniques or deep learning for logo recognition and verification, should cite the following source:

Suyahman, S., Sunardi, S., Murinto, M., & Khusna, A. N. (2024). Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection. Indonesian Journal of Artificial Intelligence and Data Mining, 7(2).

Khusna, A. N. (2024). Siamese Neural Networks with Chi-square Distance for Trademark Image Similarity Detection. Scientific Journal of Informatics, 11(2), 429-438.

This citation provides acknowledgment to the original creators and their contribution to the development of methodologies in image processing and artificial intelligence.

Traffic analytics, rankings, and competitive metrics for logos-world.net as of September 2025

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

The Brand Logos and Icons Dataset consists of a collection of 1,991 logos and icons images collected in two formats: 1810 images in PNG format and 145 images in JPG format. These logos represent a diverse array of designs from various industries, including brand logos, corporate symbols, and graphic elements.

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

This dataset contains the predicted prices of the asset LOGOS AI over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Traffic analytics, rankings, and competitive metrics for logos.com as of September 2025

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.

Comprehensive dataset containing 3 verified Logos locations in State of São Paulo, Brazil with complete contact information, ratings, reviews, and location data.

View Logos group inc import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

MB I do logos financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

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

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

Visuomeninė organizacija Logos financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

The "Logo-2K+" dataset, published in the paper "Logo-2K+: Discriminative Region Navigation and Augmentation Network for Scalable Logo Classification", is a collection of 167,140 images of logos belonging to 2,341 sub-classes across 10 root-categories. The images were crawled from the Google and Baidu search engines.

Before making the dataset available for public use, I have carefully cleaned the dataset to ensure that it can be loaded and used without any errors. I have removed all folders with special characters and spaces in their names, and only kept alphanumeric characters and underscores. This makes the dataset more accessible and easier to use for researchers and developers working on logo classification.

The cleaned dataset is provided in three parts:

"Logo-2K+.rar" contains the original 167,140 logo images, grouped into 10 root-categories and 2,341 sub-classes.

a. "Logo-2K+classes.txt" provides labels for all sub-classes.

b. "train_images_root.txt" lists the paths of training images starting with the root-category.

c. "test_images_root.txt" lists the paths of testing images starting with the root-category.

d. "train_images.txt" lists the relative paths of training images starting with the sub-class.

e. "test_images.txt" lists the relative paths of testing images starting with the sub-class.

The "Logo-2K+" dataset is a valuable resource for researchers and developers working on logo classification, as it contains a large and diverse set of logo images with well-defined sub-class labels. The provided training and testing images, along with the label files, can be used to train and evaluate logo classification models.

The statistic comparison of 10 root categories from Logo-2K+ is shown as follows.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1885485%2F9473b590a6a770cf5b3cd42e9b66a13b%2FScreenshot%202023-03-23%20at%208.50.41%20PM.png?generation=1679575860470498&alt=media" alt="">