July 2, 2025

Data collection: Where to find the image dataset?

You must be wondering, where do companies and organizations get these images from in the first place? 

This foundational work sets the stage for creating powerful models that can transform how we interact with technology.

Acquiring these datasets isn’t just a walk in the park. Due to their quantity and sheer size, managing these massive collections can be a daunting task. Here are some of the ways companies and organizations can obtain high-quality images. 

Specialized Platforms and Dataset Marketplaces

Some companies and platforms offer curated, high-quality image datasets or services to create custom datasets. 

OORT DataHub 

OORT DataHub is a decentralized cloud platform where people across the world can collect and pre-process data, including images, audio, or video, to improve AI and machine learning models. Aims to become the powerhouse for future AI development by feeding trusted data.

The platform leverages a global network of over 200,000 contributors from 136 countries, enabling the collection of region-specific and diverse datasets that reflect real-world scenarios. Businesses can create specific data collection tasks tailored to their AI model requirements. You define the parameters, and the platform’s contributors collect the relevant images, ensuring the dataset fits your use case precisely.

By using OORT DataHub, companies can efficiently access large-scale, ethically sourced, high-speed, high-quality, and flexible image datasets that reflect real-world diversity and meet strict quality standards, accelerating AI development with confidence.

Open Datasets 

Open datasets are large collections of images that have already been curated and often come with annotations. They are widely used in academia and industry to train and benchmark AI models. Some of the best-known open datasets include:

ImageNet

Over 14 million images are organized into thousands of categories, widely used for image classification tasks.

COCO (Common Objects in Context)

Around 328,000 images with detailed annotations for object detection, segmentation, and human pose estimation.

Places365

Contains 1.8 million images categorized by scene types, useful for scene recognition.

Self-Collected and Self-Annotated Data

If your project requires very specific or proprietary images (e.g., medical scans, satellite images, drone footage), collecting your own raw images is an option. You can capture images using cameras or sensors and then annotate them using annotation tools. This approach gives you full control over data quality and relevance, but requires more resources for collection and annotation.

Web Scraping and Open-Source Image Repositories

Web Scraping 

Automated scripts can gather images from the internet based on specific search queries. However, scraped images are raw and require cleaning and annotation. Also, be mindful of copyright and privacy laws when using scraped images.

Open-Source Image Websites

Platforms like Unsplash, Flickr (with Creative Commons licenses), and Wikimedia Commons offer large collections of free-to-use images that can serve as raw data for annotation. These sources reduce the workload of dataset creation and provide diverse image types.

Real-World Uses of Image Annotation

With high-quality images and well-labeled data, Image annotation powers computer vision in many technologies we interact with daily:

  • Healthcare: Annotated medical images help AI detect diseases like cancer early by recognizing patterns invisible to the naked eye.
  • Transportation: Self-driving cars rely on annotated images to identify pedestrians, traffic lights, and other vehicles to navigate safely.
  • Agriculture: AI uses annotated images to monitor crop health and detect pests.
  • As we explained in the previous blog article, high-quality images are crucial; hence, where the companies gather their image datasets is equally important. For a computer vision model to learn and perform accurately, it needs access to a vast collection of high-quality images—think hundreds of thousands, or even millions!  Facial recognition systems use annotated facial landmarks to identify individuals accurately.

These practical examples showcase the true value of image annotation: turning raw data into actionable intelligence that drives innovation across every sector.

Image annotation is much more than just labeling pictures; this guide has highlighted how annotated images are the key ingredient powering today’s most advanced AI applications. The types of annotation and image dataset providers you choose directly impact the performance and accuracy of computer vision systems. So, whether you’re just starting out or looking to deepen your expertise, understanding image annotation is key to unlocking the full potential of computer vision technology. 

Explore more information regarding tools and resources available for image annotation in our blog articles, and be part of the exciting journey toward smarter, more perceptive AI.