This is a list of 23,940 men's shoes and their product information collected from Amazon Product Database.

The dataset includes shoe name, brand, price, and more.

there are five columns in data set

1. brand_name:-- this columns show brand (company name) for eg:- asiasn,campus,reebook etc,

2.how_many_sold:-- Number of shoe sold

3.Current_Price:--price of shoe during purches

4.Product_details:--for what purpose we use this for eg:-running ,walkig,jumping etc

5.Rating:--out of 5 star how much star given by customer in form of feedback

What You Can Do with This Data

You can use this data to determine

What is the average price of each distinct brand listed?

Which brands have the highest prices?

Which ones have the widest distribution of prices?

you can apply Machine learning algorithm to detect the price of shoe based on their Brand_name, Ratings,how_much_sold

This statistic shows a trend in monthly retail sales of shoe stores in the United States from January 2017 to April 2025. In April 2025, 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.

Context

This dataset is from the StockX 2019 Data Contest.

Content

Currently the dataset consists of the single file of sales provided by StockX. ~10000 shoe sales from 50 different models (Nike x Off-White and Yeezy).

In the coming weeks more data will be added, including the estimated number of pairs released for each model and other information that might be useful for making predictions. Additionally, some of the data types will be modified to make numerical analysis easier.

Inspiration

• What shoes are most popular?
• Which shoes have the best/worst profit margins?
• What factors affect profit margin?
• Is it possible to predict the sale price of a shoe at a given time? (i.e. when should I sell?)

This timeline presents Nike's North American revenue from 2009 to 2025, by segment. Nike's North American revenue from footwear amounted to roughly 12.7 billion U.S. dollars in the year ended May 31, 2025, which was far greater than that of the apparel and equipment segments combined. That said, only the equipment segment recorded noticeable sales growth in the last year. A broad portfolio Nike offers an extremely broad array of products within the apparel and sports equipment market. As one of the leading companies, Nike tries to stay ahead of the game and create new, unique and innovative products to give their athletes, and their profit margins the edge. This can be seen in the number of patents filed by Nike. These patents cover a wide array of technology areas, primarily design, followed by footwear. Nike shoes And Nike’s investment in footwear is rewarded, as the revenue of Nike’s footwear segment compared to Adidas and Puma is far greater than that of its competitors. Nike has many lines of iconic shoes, from their air Jordan line to the extremely limited-edition Nike Air mags. These were shoes based on the film Back to the Future, which feature self-lacing technology.

Physical IQa: Physical Interaction QA, a new commonsense QA benchmark for naive physics reasoning focusing on how we interact with everyday objects in everyday situations. This dataset focuses on affordances of objects, i.e., what actions each physical object affords (e.g., it is possible to use a shoe as a doorstop), and what physical interactions a group of objects afford (e.g., it is possible to place an apple on top of a book, but not the other way around). The dataset requires reasoning about both the prototypical use of objects (e.g., shoes are used for walking) and non-prototypical but practically plausible use of objects (e.g., shoes can be used as a doorstop). The dataset includes 20,000 QA pairs that are either multiple-choice or true/false questions.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('piqa', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This dataset was acquired in the framework of the Medical Waste Treating 4.0 funded by the Tuscany Region.

The dataset aims to be a valuable resource for devising and testing computer vision methods for the primary sorting of medical waste.

Acquisition device: OAK-D camera with tech specs available here https://docs.luxonis.com/projects/hardware/en/latest/pages/BW1098OAK.html

Each sample consists of three images, namely an RGB image and a stereo pair:

RGB: 1920 x 1080 Grayscale: 640 x 400

Example: timestamp.jpg = RGB Image timestamp_r.png = Right image in the stereo pair timestamp_l.png = Left image in the stereo pair

Categories: - gauze - glove pair latex - glove pair nitrile - glove pair surgery - glove single latex - glove single nitrile - glove single surgery - medical cap - medical glasses - shoe cover pair - shoe cover single - test tube - urine bag

DeepFashion MultiModal Parts2Whole

  Dataset Details





  Dataset Description

This human image dataset comprising about 41,500 reference-target pairs. Each pair in this dataset includes multiple reference images, which encompass human pose images (e.g., OpenPose, Human Parsing, DensePose), various aspects of human appearance (e.g., hair, face, clothes, shoes) with their short textual labels, and a target image featuring the same individual (ID) in the same outfit… See the full description on the dataset page: https://huggingface.co/datasets/huanngzh/DeepFashion-MultiModal-Parts2Whole.

Slips, trips, and falls are a major cause of injury in the workplace. Footwear is an important factor in preventing slips. Furthermore, traction performance (friction and under-shoe fluid drainage) are believed to change throughout the life of footwear. However, a paucity of data is available for how traction performance changes for naturally worn, slip-resistant footwear. Participants wore slip-resistant footwear while their distance walked was monitored. Friction and under-shoe fluid pressures were measured using a robotic slip tester under a diluted glycerol contaminant condition after each month of wear for the left and right shoes. The wear volume and the size of the worn region was also measured. Prior to wearing shoes at work, participants completed dry walking trials during which ground reaction forces were recorded across different types of shoes. The peak normal force, shear force, and required coefficient of friction (RCOF) were calculated. Friction initially increased and then steadily decreased as the distance walked and the size of the worn region increased. Fluid pressures increased as the shoes were worn and were associated with increased walking distance and size of the worn region. Consistent with previous research, increases in the size of the worn region are associated with increased under-shoe fluid pressures and decreased traction. These trends are presumably due to reduced fluid drainage between the shoe-floor interface when the shoe becomes worn. Wear rate was positively associated with peak RCOF and with peak shear force, but was not significantly related to peak normal forces. Traction performance changes with natural wear. The distance walked in the shoe and the size of the worn region may be valuable indicators for assessing loss of traction performance. Current shoe replacement recommendations for slip-resistant shoes are based upon age and tread depth. This study suggests that tools measuring the size of the worn region and/or distance traveled in the shoes are appropriate alternatives for tracking traction performance loss due to shoe wear. The finding that shear forces and particularly the peak RCOF are related to wear suggests that a person’s gait characteristics can influence wear. Therefore, individual gait kinetics may be used to predict wear rate based on the fatigue failure shoe wear mechanism.

Methods This data set describes data from a progressive wear experiment. Shoes were tested at baseline (Month of Wear = 0) and after each month of use. Between tests, participants wore the shoe at their work environment for a period of 1 month. Multiple months of data were collected for each participant.

Progressive Wear Experiment Column Numbers

1. Column A (“Subject”) represents the assigned subject ID [1, 2].

2. Column B (“Month of Wear”) represents the completed number of months the shoes were worn [1, 2].

3. Column C (“Total Distance [km]”) represents the distance walked that the shoes were worn [1, 2].

4. Column D (“Distance per month [km]”) represents the distance walked the shoes were worn in the previous month [1].

5. Column E (“Shoe Brand”) represents the brand of the shoe.

6. Column F (“Shoe Code 3 (A/B/C)”) represents the shoe code designation between A, B, and C [1, 2].

7. Column G (“Hardness”) represents the Shore A hardness of the shoe outsole [2].

8. Column H (“Side”) represents the left or right shoe of the pair [1].

9. Column I (“ACOF”) represents the shoe average available coefficient of friction from the five slip testing trials [1].

10. Column J (“%ACOF from Baseline”) represents the shoe available coefficient of friction relative to the new condition [1].

11. Column K (“Fluid Force [N]”) represents the load supported by the fluid during slip testing [1].

12. Column L (“Peak Fluid Pressure [kPa]”) represents the peak fluid pressure measured between the shoe and flooring during slip testing.

13. Column M (“Contact Area [sq in]”) represents the measured contact area between the shoe and flooring during slip testing.

14. Column N (“Untreaded Length [mm]”) represents the length of the continuous worn region along the long axis of the shoe [1].

15. Column O (“Untreaded Width [mm]”) represents the width of the continuous worn region along the short axis of the shoe [1].

16. Column P (“Untreaded Region Location”) represents the location on the shoe heel where the measurement was acquired.

17. Column Q (“Worn Region Size [mm^2]”) represents the product of the untreaded length and width in mm2 [1]. This variable is known as the size of the worn region in [1].

References:

1. Hemler, S.L., Pliner, E.M., Redfern, M.S., Haight, J.M. and Beschorner, K.E., 2020, Traction performance across the life of slip-resistant footwear: preliminary results from a longitudinal study. Journal of Safety Research 74, 219-225.
   

2. Hemler, S.L., Sider, J, Redfern, M.S., Beschorner, K.E., 2021, Gait Kinetics Impact Shoe Tread Wear Rate. Gait & posture 86, 157-161.
   

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

We propose Safe Human dataset consisting of 17 different objects referred to as SH17 dataset. We scrapped images from the Pexels website, which offers "https://www.pexels.com/license/">clear usage rights for all its images, showcasing a range of human activities across diverse industrial operations.

To extract relevant images, we used multiple queries such as manufacturing worker, industrial worker, human worker, labor, etc. The tags associated with Pexels images proved reasonably accurate. After removing duplicate samples, we obtained a dataset of 8,099 images. The dataset exhibits significant diversity, representing manufacturing environments globally, thus minimizing potential regional or racial biases. Samples of the dataset are shown below.

Paper available at Arxiv Link.

GitHub link: https://github.com/ahmadmughees/SH17dataset

Key features

• Collected from diverse industrial environments globally
• High quality images (max resolution 8192x5462, min 1920x1002)
• Average of 9.38 instances per image
• Includes small objects like ears and earmuffs (39,764 annotations < 1% image area, 59,025 annotations < 5% area)

Classes

1. Person
2. Head
3. Face
4. Glasses
5. Face-mask-medical
6. Face-guard
7. Ear
8. Earmuffs
9. Hands
10. Gloves
11. Foot
12. Shoes
13. Safety-vest
14. Tools
15. Helmet
16. Medical-suit
17. Safety-suit

The data consists of three folders, - images contains all images - labels contains labels in YOLO format for all images - voc_labels contains labels in VOC format for all images - train_files.txt contains list of all images we used for training - val_files.txt contains list of all images we used for validation

Disclaimer and Responsible Use:

This dataset, scrapped through the Pexels website, is intended for educational, research, and analysis purposes only. You may be able to use the data for training of the Machine learning models only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

Users should adhere to Copyright Notice of Pexels when utilizing this dataset.

Legal Simplicity: All photos and videos on Pexels can be downloaded and used for free.

Allowed 👌

• All photos and videos on Pexels are free to use.
• Attribution is not required. Giving credit to the photographer or Pexels is not necessary but always appreciated.
• You can modify the photos and videos from Pexels. Be creative and edit them as you like. #### Not allowed 👎
• Identifiable people may not appear in a bad light or in a way that is offensive.
• Don't sell unaltered copies of a photo or video, e.g. as a poster, print or on a physical product without modifying it first.
• Don't imply endorsement of your product by people or brands on the imagery.
• Don't redistribute or sell the photos and videos on other stock photo or wallpaper platforms.
• Don't use the photos or videos as part of your trade-mark, design-mark, trade-name, business name or service mark.

No Warranty Disclaimer:

The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.

Ethical Use:

Users are encouraged to consider the ethical implications of their analyses and the potential impact on broader community.

GitHub Page:

https://github.com/ahmadmughees/SH17dataset

Citation:

@misc{ahmad2024sh17datasethumansafety,
   title={SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry}, 
   author={Hafiz Mughees Ahmad and Afshin Rahimi},
   year={2024},
   eprint={2407.04590},
   archivePrefix={arXiv},
   primaryClass={cs.CV},
   url={https://arxiv.org/abs/2407.04590}, 
}

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2806979%2F0a24bd8b9a3f281cf924a5171db28a40%2Fpexels-photo-3862627.jpeg?generation=1720104820503689&alt=media" alt="">

This dataset contains images of garbage items categorized into 10 classes, designed for machine learning and computer vision projects focusing on recycling and waste management. It is ideal for building classification or object detection models or developing AI-powered solutions for sustainable waste disposal.

Dataset Summary

The dataset features 10 distinct classes of garbage with a total of 19,762 images, distributed as follows:

• Metal: 1020
• Glass: 3061
• Biological: 997
• Paper: 1680
• Battery: 944
• Trash: 947
• Cardboard: 1825
• Shoes: 1977
• Clothes: 5327
• Plastic: 1984

Key Features - Diverse Categories: Covers common household waste items for a wide range of applications. - Balanced Distribution: Each class is sufficiently populated, ensuring robust model training. - High-Quality Images: Clear and well-annotated images for better performance in computer vision tasks. - Real-World Applications: Ideal for building recycling solutions, waste segregation apps, and educational tools.

Academic Reference The dataset was featured in the research paper, "Managing Household Waste Through Transfer Learning", showcasing its utility in real-world applications. Researchers and developers can replicate or extend the experiments for further studies.

Applications - AI for Sustainability: Train AI models to classify garbage and promote automated waste management. - Recycling Programs: Build systems to sort garbage into recyclable and non-recyclable materials. - Environmental Education: Develop tools to teach kids and adults about proper waste disposal.

Feedbacks

Thank you for your interest in our waste dataset. Whether you have used the dataset or are considering its use, your feedback is crucial to help us understand your needs and improve the dataset. Please take a few minutes to share your thoughts and experiences through this feedback form. Your input is greatly appreciated.

We also welcome feedback and contributions to our project on GitHub. Your suggestions and collaboration can help us enhance the dataset and improve the model's performance. Let's work together to make a positive difference!