Count hands in a photo using AI

Just upload your image, and our AI tool will count the number of hands in the photo - in just seconds.

hands identifier

Works best when image is not blurry or zoomed in/out. Accuracy is not guaranteed.

How this AI detector works

To start, upload your image. It will be processed by our ML/AI box detection tool, which will count the number of instances of hands found. We'll then output that number. The count will be a number, like "1", "2", "3", etc.

This model uses Nyckel's AutoML platform to test many different models at once, in order to drive the most accurate results.

Whether you're just curious or building hands detection into your application, we hope our AI counter proves helpful.

Need to count hands at scale?

Get API or Zapier access to this detector for free. It's perfect for:



  • Safety Compliance in Manufacturing: This image detector can be used to monitor worker safety in manufacturing settings by counting the number of hands present in photos. By analyzing images from various workstations, companies can ensure that proper safety protocols are adhered to, such as the use of protective gear or adherence to task assignments.

  • Crowd Management in Events: Event organizers can utilize this function to assess crowd sizes by counting hands in crowd images. This information can be crucial for managing attendee safety, ensuring compliance with venue capacities, and planning for emergency situations during events.

  • Gesture Recognition in User Interfaces: The hand-counting capability can enhance user interface interactions by recognizing gestures in real-time. By detecting how many hands are engaged with a device, it can help facilitate intuitive control in smart environments, such as home automation systems or educational tools.

  • Activity Recognition in Sports: Sports analysts can use this tool to evaluate player engagement during games or practice sessions by counting the number of hands involved in specific activities, such as shooting, passing, or blocking. This data can provide insights into player performance, teamwork dynamics, and strategic developments.

  • Health Monitoring in Telemedicine: In telehealth settings, this image detector can assist in patient monitoring by analyzing images to identify hand placements or movements indicative of specific health conditions. For example, recognizing how a patient holds or uses their hands can aid in diagnosing motor function problems or assessing recovery progress.

  • Social Media Content Analysis: Marketers can leverage this technology to analyze user-generated photos on social media platforms, counting how many instances of hands are present in various contexts. This analysis can reveal trends in user engagement, interactions during events, or popularity of products based on human interaction.

  • Training and Simulation in Robotics: By integrating the hand-counting feature into robotic training simulations, developers can create environments that require an understanding of human interaction. The technology helps robots learn to respond to human gestures more effectively by providing them with data on how many hands are present and their positions in various scenarios.

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