Our team can help you to quickly create computer vision solutions with visual AI in weeks, not years. From inventory to authentication, microscopic to satellite, actions to radiology, by training our AI for image recognition in still images or video, we generate results for our customers.

Our image recognition increase accuracy and speed of visual tasks, reduce risk and costs – and create radical time savings. Please contact us no matter your industry – healthcare, media, geospatial, security, industrial, media or beyond.

Concept to Visual AI Deployment

Our team has deep expertise in scoping, planning and developing computer vision projects for global scale. Our typical workflow includes:

  • Solutions Design
  • Data Collection
  • Annotation & Labelling
  • Model Development
  • Prototype Testing
  • Integration
  • Support & Growth

Please get in touch for an image recognition demo and to discuss how we can add radical efficiencies with our computer vision services.

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Chooch AI provides rapid computer vision development, as well as AI models that are ready to deploy as edge AI or AI in the cloud.

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Chooch AI’s Image Recognition Services

Our team of AI experts has deep knowledge of, and experience with, computer vision projects for industry clients—from scoping and planning to development and deployment. We’ve used this expertise to build a powerful yet user-friendly AI solution for the masses: the Chooch AI platform.
The services of the Chooch AI platform include data collection, AI training, model deployment, and inference. By leveraging the Chooch AI platform within your business, you can get split-second, highly accurate results from your images and videos, both in the cloud and on edge devices.

The Chooch AI platform is capable of performing both image recognition. What’s the difference between image recognition?

  • The goal of image recognition is to classify an entire image into a particular category. For example, if we have a dataset that contains images of dogs, an image recognition model might classify these images into categories based on the dog breed depicted. Each image in the dataset on which the model is trained is given a label that denotes its contents. 

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How to Get a Demo of Chooch AI’s Image Recognition Service

The clients of Chooch AI have seen tremendous benefits from using our image recognition service—so how can you find out if our AI platform is right for you? Keep reading to find out how to try Chooch AI’s image recognition service service for yourself.

Chooch AI Image Recognition Demo

It’s easy to get an image recognition demo through the Chooch AI platform: our web-based AI demo gives you an immediate demonstration of the platform’s capabilities. After you upload an image, the pre-trained Chooch AI model will try to match its content with the tags stored within its vast Perception Library. From types of leaves to bacteria under a microscope, the possibilities with the Chooch AI platform are nearly endless.

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The Chooch AI Image Recognition API

Chooch AI’s image recognition API is the easiest way to get started bringing the power of AI and computer vision to your organization.

Image Recognition API

Given an input URL that links to an image or video, the image recognition API will return its best guesses as to the contents of that image. Below is a simple example of how to query the image recognition REST API using Python:

import requests
import json
url = ‘’
files = {‘image’: open(‘local_image.jpg’, ‘rb’)}
response =, files=files)

In response, the image recognition API will return a JSON output containing the following response fields:

  • Url: The URL of the given image file.
  • Status: The status of the API request. When successful, the status is “ok”.
  • Predictions: The model’s predictions made on the image. Predictions are provided as a list and have class_title and order fields, where class_title is the name of the class predicted, and order is the order of relevancy of the particular class.
  • Sub_predictions: Predictions made under dense classification, where the image or frame is segmented into parts and classified based on the segments.
  • Text_value: Predictions for text contained in the image (optional).
  • Face_name: Predictions for the names of faces recognized in the image (optional).
  • Face_count: The number of faces detected in the image (optional).
  • Coordinates: The pixel coordinates of an object in the format x1, x2, y1, y2.
  • Count: The number of times an object or concept appears in an image or a frame.
  • File_ID: A unique ID created for the image to identify it for future reference.

For more information, check out our short Computer Vision API videos.

More Information

Facial API

Read documentation about our AI API

Watch our Computer Vision API videos