Dataset Card for Dataset Name

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

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="">

During an April 2023 online survey among adults in the United States, slightly more than ********* (or ** percent) of respondents reported having purchased a product because it had an interesting logo. Meanwhile, ** percent of U.S. GenZers purchased a product because it had an interesting logo.

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.

During an April 2023 online survey among adults in the United States, ** percent of respondents stated they had seen, read, or heard some or a lot about Pepsi changing its logos and visual identity. Less than ** percent said the same about Toblerone's shift. Mondelēz International, which runs the chocolate brand, had to remove the Matterhorn mountain from its logo after moving the candy's production out of Switzerland. According to the same study, around one-third of U.S. adults said they bought a product because it had an interesting logo.

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.

During an April 2023 online survey among adults in the United States, ** percent of responding GenZers (born between 1997 and 2012) and ** percent of millennials (1981-1996) reported having purchased a product because it had an interesting logo. Among GenXers (1965-1980) and baby boomers (1946-1964), the shares stood at ** and ** percent, respectively.

Comprehensive YouTube channel statistics for logo blocks, featuring 1,450,000 subscribers and 119,288,079 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Gaming category. Track 573 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

During an April 2023 online survey among adults in the United States, at least ** percent of respondents from all generations somewhat or strongly supported brands temporarily changing logos and visual identities for Christmas. Meanwhile, less than ** percent of GenZers (born between 1997 and 2012), millennials (1981-1996), and GenXers (1965-1980), as well as less than ** percent of baby boomers (1946-1964), said the same about Pride month. According to the same study, ** percent of U.S. adults thought brands should never change their logos.

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>", "target": 3… See the full description on the dataset page: https://huggingface.co/datasets/fsuarez/autotrain-data-logo-identifier-v2-short.

Financial overview and grant giving statistics of Encountering the Logos Ministry

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

In this work, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. The statistic comparison of 9 super categories from LogoDet-3K is shown as follows,

This is a mirror of https://github.com/Wangjing1551/LogoDet-3K-Dataset on Kaggle, because the original is only accessible from china.

Consumers in the UK were asked which food packaging logos they recognized and which logos were important to them when purchasing food. As of 2020, the Fairtrade logo was the most recognized logo and ** percent of consumers who recognized it said it was important to them when considering what product to buy. The ASC logo, from the Aquaculture Stewardship Council, was only recognized by **** percent of survey respondents.

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

🇺🇸 미국

Dataset

This dataset was created by Abhinav Raj111

Released under Apache 2.0

Contents

Open Checkbook BR logo in standard colors

Comprehensive dataset containing 54 verified LOGO locations in Russia with complete contact information, ratings, reviews, and location data.

Financial overview and grant giving statistics of Logos Language Institute Inc