Top 9 Object Detection APIs

Top 9 Object Detection APIs

This article is brought to you by the Eden AI team. We allow you to test and use in production a large number of AI engines from different providers directly through our API and platform. You are a solution provider and want to integrate Eden AI, contact us at :


In this article, we are going to see how we can easily integrate an Object Detection engine in your project and how to choose and access the right engine according to your data.


Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow you to locate where objects are in a given scene. Object detection is different from image recognition which labels an entire image:


In the beginning of the 2000s, the first object detection engines were handmaded due to the lack of effective image representation at that time.

Originally proposed in 2005 by N. Dalal and B. Triggs, the Hog Detector is an improvement of the scale invariant feature transform and shape contexts of its time.HOG works with something called blocks, a dense pixel grid in which gradients are constituted from the magnitude and direction of change in the intensities of pixels within the block. HOGs are widely known for their use in pedestrian detection. To detect objects of different sizes, the HOG detector rescales the input image for multiple times while keeping the size of a detection window unchanged.

Between 2005 and 2015, multiple object detection evolutions were created: Deformable Part-based Model (DPM) then deep learning approaches (AlexNet, RCNN? SSPnet, FastRCNN, FPN, etc.).

Top 10 Object Detection:

Microsoft Azure - Available on Eden AI

Microsoft Azure computer vision

Object detection is similar to tagging,  but the API returns the bounding box coordinates (in pixels) for each  object found in the image. For example, if an image contains a dog, cat  and person, the Detect operation will list those objects with their  coordinates in the image. You can use this functionality to process the  relationships between the objects in an image. It also lets you  determine whether there are multiple instances of the same object in an  image.

Available on Eden AI

Chooch AI

Chooch AI interface computer vision

Chooch AI is a visual detection platform that quickly replicates human visual tasks  and processes with AI on the edge & in the cloud. With high accuracy and fast response time, Chooch AI is the leader both  in computer vision training and deployment for true Visual AI on the edge and in the cloud. Chooch provides complete artificial intelligence solutions in healthcare, geospatial, media, security, retail and industrial applications.


Imagga Image Recognition interface

Imagga is a computer vision artificial intelligence company. Imagga Image Recognition API features auto-tagging, auto-categorization, face  recognition, visual search, content moderation, auto-cropping, color  extraction, custom training and ready-to-use models. Available in the Cloud and on On-Premise. It is currently deployed in leading digital asset management solutions and personal cloud platforms and consumer  facing apps.

Google Cloud - Available on Eden AI

Google Cloud Vision API platform

The Vision API can detect and extract multiple objects in an image with Object Localization.

Object localization identifies multiple objects in an image and provides a LocalizedObjectAnnotation for each object in the image. Each LocalizedObjectAnnotation identifies information about the object, the position of the object, and rectangular bounds for the region of the image that contains the object.

Available on Eden AI


Cloudmersive Learning Image Recognition and Processing APIs

Cloudmersive brings its customers a complete portfolio of APIs across document conversion and processing, deep learning OCR, image recognition, NLP, etc. Cloudmersive powerful object detection API automatically identify the location and type of objects, and people in an image.

AWS - Available on Eden AI

Amazon Rekognition Image platform

Amazon Rekognition Image can return the bounding box for common object labels such as cars, furniture, apparel or pets. Bounding box information isn't returned for less common object labels. It provides bounding boxes to find the exact locations of objects in an image, count instances of detected objects, or to measure an object's size using bounding box dimensions.                                                                                                      

Available on Eden AI


Ximilar computer vision platform

Ximilar is a computer vision platform for Image Recognition, Visual Search and Machine Learning. Ximilar provides pre-trained tagging model with nearly 1,000 tags from areas such as animals, landscapes, cities & objects like person, weapon, camera or  luggage.


Clarifai's Computer Vision Services

Clarifai is a leading provider of artificial intelligence for unstructured image, video, and text data. We help organizations transform their images, video, and text data into structured data significantly faster and more accurately than humans would be able to do on their own. Leverage Clarifai's suite of pre-trained models to identify tens of thousands of concepts across your media. Detect the presence of logos, apparel, people, vehicles, weapons, uniforms, and  hate symbols.


Matroid object detection engine

Matroid provides a studio to create, combine, and use computer vision detectors, without programming. Matroid provides ervyday object detection engine that detects and localizes common objects like people, cars, animals, trains, etc.

Visua AI

Visua AI Visual Classification tool

Visua AI has built a visual classification tool that focuses on extracting the most relevant signals from media. Specifically built for the needs of platforms and specialist providers, the technology makes it easier for you to derive meaningful insights for clients.

Use cases:

You can use Object Detection in numerous fields, here are some examples of common use cases:

The Multi cloud approach

When you need a Object Detection engine, you have 2 options:

  • First option: multiple open source Object Detection engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use. Using an open source AI library requires data science expertise. Moreover, you will need to set up a server internally to run open source engines.
  • Second option: you can use engines from your cloud provider. Actually, cloud providers like Google Cloud, AWS, Microsoft Azure, Alibaba Cloud or IBM Watson are all providing multiple AI engines often including Object Detection. This option looks very easy because you can stay in a known environment where you might have abilities in your company and the engine is ready-to-use.

The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.

This method is the best in terms of performance and optimization but it presents many inconveniences:

  • you may not know every performant providers on the market
  • you need to subscribe and contract with all providers
  • you need to master each providers API documentation
  • you need to check their pricings
  • You need to process data in each engine to realize the benchmark

Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Object Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.

Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Object Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production.

Test and API:

Here is the code in Python (GitHub repo) that allows to test Eden AI for object detection:

Python's code
Eden AI Python SDK: Object Detection

Part of answer:

Code answer


Eden AI Object Detection platform
Eden AI platform - Object Detection

Eden AI Object Detection platform
Eden AI platform - Object Detection


There are numerous Object Detection engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Object Detection technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.

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