Image Annotation for Computer Vision

Improve machine learning object recognition with quality training data

Computer vision solutions are becoming accessible to almost every industry, from autonomous vehicles and medical research to retail and agriculture, all thanks to dramatic advances in machine learning scalability and open-source modeling approaches. Attempted deployments of these models oftentimes fail accuracy standards because of the same fundamental problem: inadequate case-specific training data at scale.

With CrowdFlower’s human-in-the-loop platform, you can annotate images specific to your business needs — generating high-quality training data to ensure your model is always as accurate as possible.

Bounding Boxes

Detect areas that correspond to objects, such as cars or pedestrians, in varied settings

Bounding Boxes

Semantic Segmentation

Label an image down to the pixel, such as footage from drones, planes, or satellites

Semantic Segmentation


Outline the shape of an object, such as the pixel-area of abnormal cells



Define the pixel coordinate of product inventory, or other points of reference


Why CrowdFlower?

Train your algorithms: CrowdFlower’s human-in-the-loop platform enables data science teams to collect, clean or label annotations of raw images for specific cases where models need help. Whether it’s a new project that lacks training data or an existing pipeline that needs improvement, just set up your workflow, define your quality standards, and CrowdFlower does the rest.

Quality results at scale: CrowdFlower handles large volumes of images efficiently and cost-effectively, ensuring you have high-quality results back in no time. With numerous built-in quality control mechanisms, you can rest assured your data will be accurate and ready to be implemented.

We have the experience: We have years of expertise in generating quality annotations of image-based media. Take advantage of our proven best practices in retail environments, social media, and self-driving vehicles — just 3 of the many verticals we serve.

How It Works

Upload your data with a simple drag and drop.

Step 1

Next, build your job. You can customize any part of the experience, from categories to follow up questions to nested logic.

Step 2

Choose your settings and press launch! You’ll be able to monitor the job in real time.

Step 3

Choose a Template and Get Started

Learn More

If you'd like to dive deeper into how Image Annotation on CrowdFlower works, here are a few resources to get you started:

CrowdFlower Success Center

Guide To: Bounding Box Aggregation