Safety Shoe

## Overview

Safety Shoe is a dataset for object detection tasks - it contains Safety Shoe annotations for 317 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).

Context

This dataset was collected for Topcoder's EdgeNet Shoe Classification contest. This challenge required us to classify different types of shoes, and build a classification model that could classify the shoe types.

Content

This dataset consists of 6 classes of shoes: boots, sneakers, flip flops, loafers, sandals, and soccer shoes. It contains 249 images of each shoe type. The size of the images is variable but the format of each file is jpeg. This dataset was acquired by scraping Yahoo Images and Unsplash using a Python script created by me. I took the API which fetches the images for each query and wrote a script to get the images. The dataset contains varied angles of shoes, different scenarios in which shoes are present, and pretty much every variation that you can think of while working on a classification dataset.

Acknowledgements

I would like to thank Topcoder community for bringing in such awesome competitions for us to compete and learn new stuff everyday. I would also like to thank my college senior Yash Jain for giving me the path to follow to become at least a good machine learning engineer.

Inspiration

You can use this dataset to train a shoe type classification model which can be used for your social media ads tracker, better shoes recommendations, finding the perfect shoes for you, and many such creative ideas.

'shoe'

## Overview

'shoe' is a dataset for classification tasks - it contains Nike Adidas Converse annotations for 710 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).

Shoe-Net-10K Dataset

The Shoe-Net-10K dataset is a curated collection of 10,000 shoe images annotated for multi-class image classification. This dataset is suitable for training deep learning models to recognize different types of shoes from images.

  Dataset Details

Total Images: 10,000

Image Size: Varies (typical width range: 94 px to 519 px)

Format: Parquet

Split:

train: 10,000 images

Modality: Image

License: Apache 2.0

  Labels

The dataset includes 5… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Shoe-Net-10K.

This statistic shows a trend in monthly retail sales of shoe stores in the United States from January 2017 to October 2024. In October 2024, footwear store sales amounted to about *** billion U.S. dollars, a slight increase on the previous month. In December 2023, retail sales in shoe stores amounted to approximately *** billion U.S. dollars, the highest total over the displayed time period.

The Online Shoe Sales industry has experienced significant growth over the past five years. This growth has been driven primarily by the increasing popularity of e-commerce, as consumers seek the convenience of online shopping. Traditional brick-and-mortar retailers entering the online realm have further expanded the market. The industry's revenue has hiked at a CAGR of 8.3% over the past five years and is expected to total $44.1 billion in 2024 when revenue will hike by an estimated 4.8%. Tech-savvy millennials and Generation Z consumers largely drive the consistent surge in online shoe sales. Their purchasing behaviors, value time efficiency and the easy comparison of prices and shoe designs significantly fortify this growth. In addition, the opportunity to read customer reviews before purchasing enhances their decision-making process. Furthermore, the advent and acceptance of secure online payment options, free shipping and friendly return policies have significantly enhanced the credibility of online shopping, rendering it more reliable than ever before. As more brick-and-mortar stores expand their online presence, the number of choices for consumers will swell further. Also, technological advancements like virtual showrooms and virtual reality fittings will enhance the online shoe shopping experience, further propelling the industry's growth. However, the market is not without challenges. Concerns around counterfeit products, sizing issues and shipment delays may impede the industry's rapid growth. Despite these challenges, with growing customer comfort in online shopping and technological advancements, the industry is expected to inch at a CAGR of 4.9% through the end of 2024 through 2029 to $56.1 billion.

1) Data Introduction • The Shoe vs Sandal vs Boot Image Dataset is a computer vision dataset designed for classifying three types of footwear: shoes, sandals, and boots.

2) Data Utilization (1) Characteristics of the Shoe vs Sandal vs Boot Image Dataset: • Composed of real-world images, the dataset is well-suited for training lightweight models or designing real-time classification systems.

(2) Applications of the Shoe vs Sandal vs Boot Image Dataset: • Development of footwear classification models: This dataset can be used to train deep learning models that automatically recognize and classify three different types of footwear.

Photos of the soles of 160 pairs of shoes (“Nike Winflo 4” or “Adidas Seeley”; 4 sizes each), with associated measurements and data about shoe wear, surfaces, and wearers. The photos are divided up into 6 zip files.Each pair of shoes was worn for at least 10,000 steps per week over a 6-month period, with multiple measurements of the shoe soles taken initially and during three check-in periods spaced at approximately 5 week intervals.The images are accompanied by 3 CSV files describing the shoes, visit (information collected from surveys along with the shoes), and individual images. The codebooks contain descriptions of the variables in each of the CSV files as well as a more extensive description of the file naming scheme outlined in the README.These files can be used to examine wear pattern development, to verify the presence of randomly acquired characteristics, and to develop algorithms which match photos of shoe soles to the corresponding prints from those soles.

Dataset

This dataset was created by RimaDas89

Released under CC0: Public Domain

Contents

As per Cognitive Market Research's latest published report, the Global Golf Shoe market size will be $3,353.30 Million by 2028. Golf Shoe Industry's Compound Annual Growth Rate will be 3.06% from 2023 to 2030.

The North America Golf Shoe market size will be USD 1,253.69 Million by 2028. What is Driving Golf Shoe Market?

High popularity for playing golf

Golf has an overall reach especially in United States. It has been estimated that approximately 108 million in the United States (one in three Americans) are interested in golf. In 2019, 34.2 million Americans played golf. Further, it has huge popularity in European countries followed by some Asian countries. The major parameter for most of the people choosing golf as it offers an excellent overall package. Apart from playing just golf, one can also take entire family on holiday, take trips with their buddies, or take some time away with colleagues or business associates. Golf can be mixed with other leisure activities, enjoying the sun, and having a nice evening meal or drink.

Mostly business executives chose golf as a way to network and found it more feasible to build relationships. Even though golf isn’t a team sport, there’s a great sense of camaraderie amongst players. It is also believed that golf is a fantastic tool to practice mindfulness, it reduces anxiety, and dampens the effects of depression. This isn’t just conjecture, either, studies have shown that golf has tangible positive effects on mental health.

Moreover, out-break of COVID-19 pandemic has highly stimulated more adoption towards golf. The National Golf Foundation hinted throughout the summer and fall that 2020 was showing a remarkable surge in both participation and rounds played despite the shutdowns and uncertainties of the Covid-19 pandemic. The NGF count showed 24.8 million golfers in the U.S. in 2020, an increase of 500,000 and 2 percent over 2019. The net gain in golfers also contributed to a healthy boost in rounds played, despite many states restricting or even banning play for weeks or months. The NGF estimated a loss of 20 million rounds in the spring with course closures and restrictions, but by year’s end, 502 million rounds were recorded.

As golf shoes are must require thing while playing golf. They are designed in such a way that it help to keep feet from sliding around during the golf swing. It thus provide greater traction, stability and balance which are three important keys to getting the proper contact when an individual hit the ball. Hence, high popularity for playing golf stimulates the growth of golf shoes market.

Increasing Consumers Disposable Income Fuels the Market Growth

Restraints for Golf Shoe Market

Intense competition.(Access Detailed Analysis in the Full Report Version)

Opportunities for Golf Shoe Market

Technological advancements.(Access Detailed Analysis in the Full Report Version)

What is Golf Shoe?

Golf shoes are sports shoes that are designed to keep golfers of all ability levels safe and comfortable. It is specifically required on golf courses to ensure that the turf, particularly the greens, is not damaged. Currently, the market offers a diverse selection of trendy and comfortable golf shoes, ranging from full leather snakeskin to modern athletic models.

Golf shoes are often composed of leather, which provides a stretch-free, snug-fitting, waterproof shoe. Some golf shoes include a waterproof lining. Synthetic material, on the other hand, uses a man-made top polyester layer rather than a natural substance to cover the shoe. This polyester is applied to the shoe's outer layer to protect it.

In addition, there are a variety of golf shoes on the market, including spiked, spikeless, hybrid, golf boots, and others. Spiked shoes are sturdier than other types of shoes, thus they can help in bringing the swing together. Spikeless shoes are frequently lightweight and comfortable shoes that can be worn on the golf course as well as in everyday life.

Golf shoes are designed to prevent the feet from slipping around when swinging the club. It provides stability, flexibility, strong arch support, and breathability. These shoes also include additional cushioning that supports the midfoot and absorbs shock as needed.

Furthermore, these golf shoes are gender-specific for men, women, and kids. Golf shoes are frequently shown in shoe stores according to ne...

Object Detection Of Shoe

## Overview

Object Detection Of Shoe is a dataset for object detection tasks - it contains Front Side Back annotations for 344 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).

Global Shoe Packaging Market size worth at USD 6.73 Billion in 2023 and projected to USD 9.70 Billion by 2032, with a CAGR of 3.7% between 2024-2032.

3D scans of the soles of 160 pairs of shoes (“Nike Winflo 4” or “Adidas Seeley”; 4 sizes each), with associated measurements and data about shoe wear, surfaces, and wearers.Each pair of shoes was worn for at least 10,000 steps per week over a 6-month period, with multiple measurements of the shoe soles taken initially and during three check-in periods spaced at approximately 5 week intervals. The images are accompanied by 3 CSV files describing the shoes, visit (information collected from surveys along with the shoes), and individual images. The codebooks contain descriptions of the variables in each of the CSV files as well as a more extensive description of the file naming scheme outlined in the README. These files can be used to examine wear pattern development in 3D, to compare 3D data capture to 2D capture methods, and to explore which artifacts may be present in 3D scans.

Shoe

## Overview

Shoe is a dataset for object detection tasks - it contains IT annotations for 499 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).

The growth is projected from USD 2.6 billion in 2025 to USD 3.1 billion by 2035, registering a CAGR of 3.8% over the forecast period.

Metric	Value
Industry Size (2025E)	USD 2.6 billion
Industry Value (2035F)	USD 3.1 billion
CAGR (2025 to 2035)	3.8%

Country-wise Analysis

Countries	CAGR (2025 to 2035)
USA	6.9%
UK	6.5%
France	6.4%
Germany	6.7%
Italy	6.3%
South Korea	6.8%
Japan	6.2%
China	7.5%
Australia	6.6%
New Zealand	6.4%

Segment-wise Analysis

Segment	Share (2025)
Mountain Bike Shoes	40.2%

Segment	Share (2025)
Premium	58%

Competitive Outlook

Company Name	Estimated Market Share (%)
Shimano	20-24%
Specialized	15-19%
Giro	10-14%
Sidi	8-12%
Other Companies (combined)	30-40%

On September 19, 2014 at York University, this survey was done as an introductory lesson to ecology 2050. The first 18 set of data was collected by asking the undergrad students in ecology lab what their current shoe size (US/Canada) is; their gender was observed and recorded as well. The rest of the data was collected by walking around campus and asking the first available person if they were willing to answer a question for a survey. If the answer was yes, their shoes size was asked and their gender recorded; if the answer was no, the next available person was asked. This procedure was done until the information of 25 participants was collected.

In 2010, around *** million pairs of leather shoes were produced in Russia. The minimum figure of **** million pairs was observed in 2015. In 2023, the volume was set at about *** million pairs, increasing by over ** million pairs from the previous year.

In 2024, U.S. shoe store sales amounted to around 38.2 billion U.S. dollars. According to the NAICS definition, this industry is comprised of establishments who are primarily engaged in retailing all types of new footwear, except hosiery and specialty sports footwear, such as golf shoes, bowling shoes, and spiked shoes. The U.S. footwear market Footwear is a multi-billion dollar industry. The revenue of the athletic footwear market in the United States was worth around 10.6 billion U.S. dollars in 2024. Most apparel brands manufacture and sell shoes, such as Tommy Hilfiger, Calvin Klein, and Louis Vuitton. Shoe retailers in the United States Shoe stores typically sell branded shoes. For example, Foot Locker is an American sportswear and footwear retailer that buys and sells shoes from the most popular brands such as Adidas, Nike, and Reebok. Athletic shoes account for a significant portion of shoe store sales in the United States, with wholesale sales generating over 20 billion U.S. dollars in 2023.

Graph and download economic data for Retail Sales: Shoe Stores (MRTSSM4482USS) from Jan 1992 to May 2025 about footwear, retail trade, sales, retail, and USA.

Images of shoe prints from 160 pairs of shoes (“Nike Winflo 4” or “Adidas Seeley”; 4 sizes each) made using fingerprint powder on clear adhesive film (contact paper), with associated measurements and data about shoe wear, surfaces, and wearers.Each pair of shoes was worn for at least 10,000 steps per week over a 6-month period, with multiple measurements of the shoe soles taken initially and during three check-in periods spaced at approximately 5 week intervals. The images are accompanied by 3 CSV files describing the shoes, visit (information collected from surveys along with the shoes), and individual images. The codebooks contain descriptions of the variables in each of the CSV files as well as a more extensive description of the file naming scheme outlined in the README. These files can be used to examine wear pattern development, to look for the presence of identifying characteristics among shoes with similar features, and to develop algorithms for matching shoes based on individualizing characteristics. The film and powder prints are likely to contain the most detail of any of the collection methods used in this project and are the most suitable for research on randomly acquired characteristics.