Labeled Pizza images suitable for training and evaluating computer vision and deep learning models.

About this Data

This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with this Data

You can use this data to discover how much you can expect to pay for pizza across the country. E.g.:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

Description:

This dataset contains thoroughly cleaned and transformed data of pizza sales, making it ideal for in-depth analysis and visualization. The data includes key information such as sales dates, pizza types, order quantities, prices, and more. It has been carefully structured to facilitate a wide range of analyses, including sales performance, customer preferences, and seasonal trends.

The dataset allows analysts to explore sales patterns over time, identify best-selling pizza types, and evaluate revenue trends across various periods. Additionally, it can be used to gain insights into customer ordering behavior, peak sales times, and regional preferences.

Features:

Pizza Types: Various types of pizzas sold, including detailed breakdowns of sizes, ingredients, and categories. Sales Data: Information on the number of pizzas sold, revenue generated, and sales by specific periods (days, months, etc.). Order Information: Data on order quantities and combinations, useful for identifying customer preferences and popular menu items. Price Data: Pricing details to evaluate revenue and profit margins. Use Cases:

Performing time-series analysis to discover trends in pizza sales across different seasons and days of the week. Visualizing the distribution of sales across different pizza types and identifying the most and least popular items. Analyzing sales performance to optimize inventory management, marketing strategies, and pricing models. Comparing sales across multiple outlets or regions to identify top-performing locations and customer segments. Whether you're looking to conduct exploratory data analysis, create interactive visualizations, or apply predictive models, this dataset offers a solid foundation for understanding pizza sales dynamics and enhancing business strategies.

Pizza and nonpizza jjjjjjjjjjjjjjjjjk kkkckck kdck

This dataset contain detailed information about pizza orders, including specifics about the pizza variants, quantities, pricing, dates, times, and categorization details.

pizza_id: A unique identifier assigned to each distinct pizza variant available for ordering. order_id: A unique identifier for each order made, which links to multiple pizzas. pizza_name_id: An identifier linking to a specific name of the pizza. quantity: The number of units of a specific pizza variant ordered within an order. order_date: The date when the order was placed. order_time: The time when the order was placed. unit_price: The cost of a single unit of the specific pizza variant. total_price: The aggregated cost of all units of a specific pizza variant in an order. pizza_size: Represents the size of the pizza (e.g., small, medium, large). pizza_category: Indicates the category of the pizza, such as vegetarian, non-vegetarian, etc. pizza_ingredients: Provides a list or description of the ingredients used in the pizza. pizza_name: Specifies the name of the specific pizza variant ordered.

1) Data Introduction • The Pizza or Not Pizza? dataset is a computer vision image dataset designed for binary classification to distinguish between images of pizza and non-pizza food items.

2) Data Utilization (1) Characteristics of the Pizza or Not Pizza? dataset: • The dataset is balanced, consisting of an equal number of pizza and non-pizza food images. All images are collected from real user-generated content, enhancing its practical applicability in real-world scenarios. • The images cover a wide range of food types and preparation environments, providing high visual diversity and realism.

(2) Applications of the Pizza or Not Pizza? dataset: • Binary classification model development for food images: The dataset can be used to train deep learning models that automatically classify whether an image contains pizza or not.

Sales of frozen pizza in the United States amounted to approximately *** billion U.S. dollars in 2024, up from ***** billion U.S. dollars the previous year. Frozen pizza - additional informationPizza is an Italian flatbread made with tomato sauce, cheese and an assortment of toppings such as meats, seafood and vegetables. Frozen pizzas, as well as other convenience foods, have become kitchen staples in many households in the United States. DiGiorno, Red Baron, and Totino's were the leading frozen pizza vendors in the U.S. in 2024, with DiGiorno, a subsidiary of Nestlé, reaching a market share of 16 *******. Private labels were also very popular among U.S. consumers, accounting for an ** percent share of the frozen pizza market. DiGiorno frozen pizza was first introduced into the U.S. market in 1995. All DiGiorno pizza products are manufactured in the Midwest. The company sources all of its ingredients in the United States and its pizza bakeries employ more than ***** people. Apart from DiGiorno, Nestlé owns top frozen pizza brands Tombstone, Jack’s and California Pizza Kitchen.

🍕🍽️ Pizza Restaurant Sales

Problem: - Pizza restaurant has recently seen a decline in sales and plans to increase them by looking at customer and order data. To do this, the management plans to perform a thorough analysis of order data and consumer behaviour in order to spot important trends and areas for improvement.

Background: - An overview of pizza sales data from January 2015 to December 2015 is given in this report. To find trends and patterns in pizza sales, data was gathered from pizza joints across the United States and analysed.

About the dataset📅¶

This dataset contains 4 tables in CSV format

The Orders table contains the date & time that all table orders were placed

The Order_Details table contains the different pizzas served with each order in the Orders table, and their quantities

The Pizzas table contains the size and price for each distinct pizza in the Order Details table, as well as its broader pizza type

The Pizza_Types table contains details on the pizza types in the Pizzas table, including their name as it appears on the menu, the category it falls under, and its list of ingredients.

Qsdfqzefqf Sdf Pizza

## Overview

Qsdfqzefqf Sdf Pizza is a dataset for object detection tasks - it contains Qsdfqzefqf annotations for 644 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Pizza

## Overview

Pizza is a dataset for object detection tasks - it contains Pizza annotations for 1,000 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 [MIT license](https://creativecommons.org/licenses/MIT).

The number of pizza restaurants in the United States reached its peak in 2022, with ****** establishments across the country. Whilst the figures have fluctuated over the past decade, the number of pizza restaurants in the U.S. has increased by nearly ************* units since 2012.

NexaAIalex/Pizza dataset hosted on Hugging Face and contributed by the HF Datasets community

jairNeto/pizza dataset hosted on Hugging Face and contributed by the HF Datasets community

Discover the booming global pizza market! Explore key trends, growth drivers, and major players shaping this $150+ billion industry, projected to maintain a strong CAGR through 2033. Learn about regional market shares, innovative product launches, and the future of pizza.

Pizza Or Not Pizza

## Overview

Pizza Or Not Pizza is a dataset for classification tasks - it contains Pizza Not_pizza annotations for 1,966 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).

In 2022, the global pizza market had an estimated sales volume of over 2.4 million tons. Western Europe accounted for the majority of sales, totaling close to 1.3 mllion tons, followed by North America where the sales volume reached 845,000 tons.

The Pizza Foodservice Market is Segmented by Structure (Chained Outlets and Independent Outlets), Service Model (Delivery-Only (Ghost Kitchens), Dine-In, and Carry-Out/Take-Away), Restaurant Format (Quick-Service (QSR), Fast-Casual, and Full-Service/Casual Dining), Location (Leisure, Lodging, Retail, Standalone, and Travel), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

In 2022, there were ****** independent pizza restaurants in the United States, roughly *** thousand more than the previous year. Meanwhile, the number of U.S.-based pizza chain restaurants was ****** in the same year.

Pizza Sales Market Outlook

The global pizza sales market size was valued at approximately USD 155 billion in 2023 and is projected to reach around USD 232 billion by 2032, growing at a CAGR of 4.6% during the forecast period. One of the key growth factors driving this market is the increasing consumer preference for convenient and ready-to-eat food products. The rise in disposable incomes and changing lifestyles have considerably influenced the consumption patterns, favoring the growth of the pizza sales market.

The burgeoning urban population across the globe is one of the primary factors propelling the growth of the pizza sales market. Urban areas have witnessed a surge in the number of working professionals, leading to a higher demand for quick meal solutions. Pizzas, being a convenient and satisfying meal option, have gained immense popularity, especially among the younger demographic. Moreover, the expansion of online food delivery services has made it easier for consumers to order pizzas from the comfort of their homes, further driving market growth.

Technological advancements in food preparation and delivery have also played a significant role in the growth of the pizza sales market. Innovations such as automated pizza-making machines, drone deliveries, and the use of AI-driven ordering systems have enhanced the efficiency and speed of pizza delivery services. Additionally, the development of new and unique pizza flavors and toppings has attracted a wider customer base, contributing to the market's expansion. The increasing trend of customization, where consumers can choose their own crust type, toppings, and sauces, has also boosted sales significantly.

The increasing awareness and demand for healthier food options have led to the introduction of health-conscious pizza variants. Whole wheat crusts, gluten-free options, and organic toppings are becoming more prevalent, catering to the health-conscious segment of the population. This trend is particularly prominent in developed regions such as North America and Europe, where consumers are more inclined towards maintaining a healthy diet. Additionally, the rising popularity of plant-based diets has led to an increased demand for vegetarian and vegan pizza options, further driving market growth.

Regionally, North America holds the largest share in the pizza sales market, driven by the high consumption rate of fast food in the United States and Canada. Europe follows closely, with countries like Italy, Germany, and the UK being major contributors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the increasing urbanization, rising disposable incomes, and the growing influence of Western food culture. Latin America and the Middle East & Africa are also emerging markets, showing substantial growth potential due to the expanding foodservice industry and changing dietary preferences.

Product Type Analysis

The pizza sales market can be segmented based on product type into frozen pizza, fresh pizza, and pizza kits. The frozen pizza segment holds a significant share of the market due to its long shelf life and convenience. Consumers prefer frozen pizzas as they can be stored for longer periods and cooked easily at home, making them an ideal option for quick meals. The increasing availability of a variety of frozen pizza options, including different crust types, toppings, and sizes, has further driven the growth of this segment.

Fresh pizza, on the other hand, is gaining traction due to the growing demand for freshly prepared food. Many consumers prefer fresh pizzas from restaurants and pizzerias for their superior taste and quality. The expansion of pizza chains and the increasing number of specialty pizza restaurants have significantly contributed to the growth of the fresh pizza segment. Moreover, the trend of dining out and the rising popularity of pizza as a casual dining option have bolstered the sales of fresh pizzas.

Pizza kits are an emerging segment in the pizza sales market, catering to consumers who prefer to prepare their own pizzas at home. These kits typically include pre-measured ingredients such as dough, sauce, and toppings, making the pizza-making process convenient and enjoyable. The rising interest in home cooking and the growing popularity of DIY meal kits have fueled the demand for pizza kits. Additionally, pizza kits provide a customizable option for consumers, allowing them to experiment with different ingredients and create personalized pizzas.

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The market is expected to grow from USD 22,692.2 Million in 2025 to USD 38,761.5 Million by 2035, at a CAGR of 5.5% during the forecast period. The penetration of frozen food retail chains, development of freezing technology ,availability of products through online grocery store, availability of frozen products are major factors that are expected to fuel the growth of the market. Food companies are focusing on a wider audience, and brands are striving to enhance taste, texture, and nutrition while minimizing artificial preservatives.

Market Metrics

Metric	Value
Market Size (2025E)	USD 22,692.2 Million
Market Value (2035F)	USD 38,761.5 Million
CAGR (2025 to 2035)	5.5%

Country wise Outlook

Country	CAGR (2025 to 2035)
United States	5.3%

Country	CAGR (2025 to 2035)
United Kingdom	5.4%

Region	CAGR (2025 to 2035)
European Union (EU)	5.6%

Country	CAGR (2025 to 2035)
Japan	5.5%

Country	CAGR (2025 to 2035)
South Korea	5.7%

Segmentation Outlook

Crust Type	Market Share (2025)
Thin Crust	58.3%

Toppings	Market Share (2025)
Cheese	62.7%

Competitive Outlook

Company Name	Estimated Market Share (%)
Frozen Specialties Inc.	18-22%
Jubilant Foodworks Limited	14-18%
Convenio Foods Pvt. Ltd	12-16%
Giovanni’s Frozen Pizza	10-14%
Monte Pizza Crust B.V	8-12%
Other Companies (combined)	30-40%