This dataset contains images of Television, Sofas, Jeans and T-shirt. It Actual raw and unstructured image data extracted from online sites.

All images are of different sites. You may also find some junk images in data for example in television dataset you will find the television remote images.

This dataset is not refined intentionally to make sure practitioners should get taste of What kind of data ML/Data Science Engineer get when they start working on any project in industry.

This dataset is composed for ecommerce product categorization regarding their images. This is a part of an experimental study aiming to classify products into their correct categories using computer vision techniques. Accurate categorization is crucial for e-commerce companies to enhance customer satisfaction and optimize sales.

We have collected ecommerce product images from various resources such as Amazon, Walmart, Google and other sources through scraping. Also Amazon Berkeley Objects (ABO) project used to enhance images in some categories (limited). Finally we have reached a dataset of 18K images, properly resized into 224*224 pixels which is suitable for many pretrained CNN models. We used PILLOW for image processing and selenium for web scraping.

18,175 images categorized in 9 classes mostly due to their major categories in Amazon. Furthermore, train-val-check sets arranged appropriately and train-val sets used for model training and a small check set reserved for model deployment to evaluate visually the model performance. The correct categorization is a big issue since there are various categories on different ecommerce marketplaces, and there is a clear trade-off among category quantities, number of images, usability and accuracy. However, we have found an optimal balance with 9 classes accross 18K images.

Dataset Card for Turkish E-Commerce Product Captions

  Dataset Details





  Dataset Description

This dataset consists of Turkish captions for product images collected from publicly accessible pages on Trendyol. The captions were generated using a captioning model and curated manually by the dataset author for academic and research purposes. The dataset includes approximately 200K samples and is intended for image captioning tasks in Turkish.

Curated by: Mustafa… See the full description on the dataset page: https://huggingface.co/datasets/forza61/e-commerce-image-captioning.

This file contains the ABO Product Images Dataset, consisting of 398,212 product images organized into 256 folders. Each folder represents a unique product with multiple images capturing different angles and views. The dataset covers a wide range of product categories and is suitable for research in product recognition, visual similarity analysis, and large-scale e-commerce inventory management.

Categories

The dataset spans a wide variety of product types commonly found in online retail, such as:

Apparel and accessories

Home and kitchen items

Electronics and gadgets

Personal care and beauty products

Tools and hardware

Sports and outdoor equipment

Toys and hobby items

Attributes

Products and their images exhibit a range of real-world visual attributes, including:

Color variations (neutral, vibrant, patterned)

Material types (plastic, metal, textile, glass, wood)

Shape and structural differences (cylindrical, box-shaped, curved, flat)

Angles of capture (front, back, side, top, perspective)

Scale differences between images of the same product

Varied lighting conditions

Background types (studio-style, neutral backgrounds, real-world capture)

The eCommerce product photography services market is experiencing robust growth, fueled by the booming e-commerce sector and the increasing demand for high-quality visuals to enhance online sales. While precise market sizing data is unavailable, considering the average CAGR for similar visual services and the rapid expansion of online retail, we can reasonably estimate the 2025 market size to be around $2.5 billion. This figure is projected to grow steadily, driven by factors such as the rising adoption of social commerce, the proliferation of marketplaces, and the increasing sophistication of consumer expectations regarding product imagery. Key trends shaping this market include the rise of AI-powered photo editing tools, the increasing use of 3D and augmented reality (AR) product visualization, and a growing demand for sustainable and ethical photography practices. Constraints on growth could include pricing pressures from emerging competitors, fluctuations in advertising spend by e-commerce businesses, and the ongoing need for skilled photographers to meet the rising demand for high-quality content. Market segmentation includes services ranging from basic product shots to sophisticated lifestyle photography, with variations tailored to specific product categories and e-commerce platforms. This diverse range of services caters to businesses of all sizes, from individual entrepreneurs to large multinational corporations. The competitive landscape is fragmented, with a variety of companies offering diverse services and price points. Established players like Squareshot and Pencil One compete alongside smaller, specialized studios, highlighting the opportunities for both large-scale providers and niche businesses. Geographic variations in market size reflect the uneven distribution of e-commerce activity globally, with North America and Europe currently holding significant shares but considerable potential for growth in developing economies with expanding online retail markets. Further growth is expected throughout the forecast period (2025-2033), driven by continuous innovation in photography technology and evolving consumer preferences towards visually compelling online shopping experiences. The market is dynamic and adaptable, constantly responding to technological advancements and shifting consumer demands. A sustained focus on quality, innovation, and customer satisfaction will be critical for success in this competitive and rapidly expanding market.

Ecommerce Product Photography Market size was valued at USD 163.91 Million in 2023 and is projected to reach USD 342.27 Million by 2031, growing at a CAGR of 11.1% during the forecast period 2024-2030.

Global Ecommerce Product Photography Market Drivers

The market drivers for the Ecommerce Product Photography Market can be influenced by various factors. These may include:

Growth of E-commerce: The industry's explosive growth is primarily due to the need for high-quality product photographs to draw in and convert online shoppers.

Rising Customer Expectations: The need for professional photography services is being driven by consumers' growing expectations of high-resolution photographs, multiple viewpoints, and precise close-ups.

Global Ecommerce Product Photography Market Restraints

Several factors can act as restraints or challenges for the Abc. These may include:

High Costs: Investing in high-quality photos can be limited for small and medium-sized enterprises due to the high cost of professional product photography.

Technological Barriers: Despite the advancements in technology, some organizations lack the necessary resources or experience to utilize the newest photography tools and software, resulting in a lapse in quality.

Market Analysis Report for AI Product Photography Tools for E-commerce The market for AI product photography tools for e-commerce is projected to expand significantly, with a CAGR of XX% during the forecast period (2025-2033). This growth is driven by the increasing demand for high-quality product images in online marketplaces, the rising popularity of e-commerce, and the technological advancements in AI and computer vision. The market is segmented by application, type, and region, with fashion and apparel, cloud-based tools, and North America being notable segments. Key trends in the market include the adoption of cloud-based tools, the rise of AI-powered image editing features, and the integration with e-commerce platforms. Cloud-based tools offer flexibility and scalability, allowing businesses to access advanced image editing capabilities without the need for expensive hardware investments. AI-powered features automate time-consuming tasks such as background removal, object manipulation, and image optimization, enabling efficient and cost-effective product image production. The integration with e-commerce platforms streamlines the workflow and allows businesses to seamlessly publish high-quality product images on their online stores.

ECommerce Product 3 Products

## Overview

ECommerce Product  3 Products is a dataset for object detection tasks - it contains Televisions Phones Laptops annotations for 1,017 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).

Discover the booming automated product photography system market! This comprehensive analysis reveals a $2 billion market in 2025, projected to grow at a 15% CAGR through 2033. Learn about key drivers, trends, and leading companies shaping this dynamic industry.

The automated product photography system market is experiencing robust growth, driven by the escalating demand for high-quality product images across e-commerce and marketing channels. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors: the increasing adoption of e-commerce platforms, the rising need for consistent and professional product imagery to enhance online sales conversions, and the growing preference for automated solutions to streamline workflow and reduce production costs. Key players like Orbitvu, PhotoRobot, and Packshot Creator are leading the innovation, constantly improving the speed, efficiency, and image quality of automated systems. The market's segmentation, although not fully detailed here, likely encompasses various system types (e.g., turntable-based, robotic arms), imaging technologies, and target industries (e.g., fashion, consumer electronics, food). Further market penetration is anticipated in emerging economies, as businesses increasingly recognize the value proposition of automated product photography in scaling their online operations. Continued growth in the automated product photography system market is anticipated due to advancements in artificial intelligence (AI) and machine learning (ML) integration, enabling features like automated background removal, image optimization, and even 3D model generation. This enhanced automation will further reduce costs and improve turnaround times, making the technology more accessible to smaller businesses. However, the initial investment cost for these systems remains a barrier to entry for some companies. Furthermore, the ongoing evolution of consumer expectations regarding image quality and the increasing prevalence of augmented and virtual reality (AR/VR) applications will continue to drive innovation and expansion within the automated product photography sector. The market’s success hinges on the continuous development of user-friendly software and affordable hardware options, alongside ongoing education and support for businesses adopting this increasingly vital technology.

Discover the booming eCommerce Product Still Photography Equipment market! Learn about its $1.5 billion valuation, 12% CAGR, key drivers, leading companies, and future trends. Get insights into market segmentation and regional analysis for informed business decisions.

Here are a few use cases for this project:

1. eCommerce photography management: The model can categorize and identify various product photographs and generate suggestions for visually appealing product compositions, based on the objects and backgrounds present in the images. This can greatly improve product photography and help the e-commerce site to gain more customers.

2. Assisting food bloggers and social media influencers: The model can be used to identify popular photography trends in the food and drink industry, such as background colors, ingredient arrangements, and styling. It can provide creative suggestions for food bloggers and influencers looking to improve their photos and grow their audiences.

3. Product packaging design: Design agencies can utilize the model's insights to create visually appealing and unique product packaging designs, based on the categorized objects and elements present in the model. Designers can draw inspiration from current trends to create new packaging ideas that will ideally attract consumers.

4. Visual marketing campaigns: The model can assist in identifying successful visual marketing strategies by identifying and analyzing photography and styling trends, particularly those related to consumer products. The information gathered from the model can be used to guide the creation of compelling visual content for advertising campaigns, social media, and product promotions.

5. Interior and home decor inspiration: The model can identify popular interior and home decor elements in various contexts, helping to inspire decorators, designers, and homeowners in creating stylish living spaces. By analyzing the trends in the model's dataset, users can find new and exciting ways to arrange and style specific rooms, such as a bathroom or dining area.

As the fashion e-commerce markets rapidly develop, tens of thousands of products are registered daily on e-commerce platforms. Individual sellers register products after setting up a product category directly on a fashion e-commerce platform. However, many sellers fail to find a suitable category and mistakenly register their products under incorrect ones. Precise category matching is important for increasing sales through search optimization and accurate product exposure. However, manually correcting registered categories is time-consuming and costly for platform managers. To resolve this problem, this study proposes a methodology for fashion e-commerce product classification based on multi-modal deep learning and transfer learning. Through the proposed methodology, three challenges in classifying fashion e-commerce products are addressed. First, the issue of extremely biased e-commerce data is addressed through under-sampling. Second, multi-modal deep learning enables the model to simultaneously use input data in different formats, which helps mitigate the impact of noisy and low-quality e-commerce data by providing richer information.Finally, the high computational cost and long training times involved in training deep learning models with both image and text data are mitigated by leveraging transfer learning. In this study, three strategies for transfer learning to fine-tune the image and text modules are presented. In addition, five methods for fusing feature vectors extracted from a single modal into one and six strategies for fine-tuning multi-modal models are presented, featuring a total of 14 strategies. The study shows that multi-modal models outperform unimodal models based solely on text or image. It also suggests the optimal conditions for classifying e-commerce products, helping fashion e-commerce practitioners construct models tailored to their respective business environments more efficiently.

Boost your e-commerce sales with high-quality product photography! Explore the latest market trends in eCommerce product photography equipment & software. Discover key drivers, restraints, and leading companies shaping this rapidly growing $1.5B market (2025 estimate). Learn how to leverage professional visuals for increased conversions.

Context

Product images are a core part of e-commerce. They contain rich information about the product’s appearance, packaging, labeling, and category. These images are widely used for building computer vision systems in online retail, such as automated product classification, recommendation engines, and visual search.

This dataset provides a large collection of 15,000+ product images across multiple categories (healthcare, beauty, supplements, electronics, household items, etc.). It is intended as a benchmark resource for computer vision research and e-commerce applications.

Content

Images: 15,000+ Amazon product images.

Format: JPEG

Categories: Mixed — includes consumer goods, healthcare, beauty, electronics, etc.

Resolution: Varies, mostly medium to high quality.

(Dataset may contains the duplicate images.)

Potential Uses

This dataset can be applied in:

Computer Vision Research

Product classification - Object detection / segmentation - Image retrieval & similarity search

E-commerce Applications

Automated catalog management - Visual product recommendation - Duplicate/near-duplicate product detection

Deep Learning

Transfer learning with CNNs or Vision Transformers - Generative tasks (synthetic product image generation, augmentation) - Multimodal research (images + potential text labels)

Notes

Dataset contains images only (no metadata such as title, description, or price).

Provided for educational and research purposes only.

Original product rights remain with their respective owners.

Acknowledgements

The Dataset Images belong to Amazon sellers.

Unlock the power of Flipkart's extensive product catalog with our meticulously curated e-commerce dataset. This dataset provides detailed information on a wide range of products available on Flipkart, including product names, descriptions, prices, customer reviews, ratings, and images. Whether you're working on data analysis, machine learning models, or conducting in-depth market research, this dataset is an invaluable resource.

With our Flipkart e-commerce dataset, you can easily analyze trends, compare products, and gain insights into consumer behavior. The dataset is structured and high-quality, ensuring that you have the best foundation for your projects.

Flipkart is largest E-commerce website based out india. Pre crawled dataset having more than 5.7 million records.

Where to use dataset

• Train machine learning algorithms
• Check discounts of various categories fields
• Find out which brand and categories having best discounts

The automated product photography system market is booming, projected to reach $7 billion by 2033, driven by e-commerce growth and demand for high-quality visuals. Learn about market trends, key players (Orbitvu, PhotoRobot), and regional insights in this comprehensive analysis.

Discover the booming Shopify photography service market! This comprehensive analysis reveals a $500M market (2025) projected to reach $1.8B by 2033 (15% CAGR). Learn about key trends, regional growth, top companies, and opportunities in this lucrative sector.

Discover the booming AI product photo generator market! This comprehensive analysis reveals key trends, market size projections (reaching ~$3.7B by 2033), leading companies, and regional breakdowns. Learn how AI is revolutionizing product visuals for e-commerce and beyond.