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 object recognition and image recognition in still images or video, we generate results for our customers.
Our image recognition and object 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.
Our team has deep expertise in scoping, planning and developing computer vision projects for global scale. Our typical workflow includes:
Please get in touch for an image recognition demo or object recognition demo, and to discuss how we can add radical efficiencies with our computer vision services.
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. Chooch AI integrates with a flexible AI API.
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 and object recognition. What’s the difference between image recognition and object recognition?
The clients of Chooch AI have seen tremendous benefits from using our image recognition and object recognition services—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 and object recognition service for yourself.
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.
As with image recognition, Chooch makes it easy to get an object recognition demo. When you request a live demo, you can use the Chooch AI dashboard to train your own object recognition models using an annotated dataset of images or videos. The Chooch AI platform can provide a highly accurate object detection demo even for smaller datasets.
Chooch AI’s image recognition API is the easiest way to get started bringing the power of AI and computer vision to your organization.
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 = ‘https://api.chooch.ai/predict/image?apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19’
files = {‘image’: open(‘local_image.jpg’, ‘rb’)}
response = requests.post(url, files=files)
print(response.content)
In response, the image recognition API will return a JSON output containing the following response fields:
As you may have noticed, Chooch AI’s image recognition API performs both image recognition (in the predictions field) and object recognition (in the coordinates field) simultaneously. You can also train your own custom object recognition model and access it through the Chooch AI object recognition API.
Like the image recognition API, querying the Chooch AI custom object recognition API is simple:
import requests import json url = ‘https://api.chooch.ai/predict/object_detection/?url=https://choochdashboard.s3.amazonaws.com/truck.jpg&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19c&model_id=88’ response = requests.post(url) json_data = json.loads(response.content) print(json_data)
A sample response from the object recognition API is below:
{
“status”: “ok”,
“prediction_type”: “object_detection”,
“predictions”: [
{
“class_title”: “person”,
“model_id”: 88,
“score”: 0.74613,
“coordinates”: {
“xmin”: 0,
“ymin”: 35,
“ymax”: 297,
“xmax”: 179
}
}
]
}
Where class_title is the model’s prediction, score is the model’s confidence, and model_id is the unique ID of the custom object recognition model that you have trained.
For more information, check out our short Computer Vision API videos.