Image Recognition Software

Image recognition software allows users to classify images and identify entities within images

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Compare Image Recognition Software
Results: 73

AIMultiple is data driven. Evaluate 73 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

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73.50000003435504
97
100
100
0
50.00000006871008
top5 , top10
5star
OpenCV
4.60
100%
0%
100%
= 1 review
= 20 employees
= 100,000 visitors

69.83694895841556
91.43254739166356
0
96.16051140957114
34.72222222222222
48.241350525167576
top5 , top10
4star
Microsoft Computer Vision API
4.00
100%
100%
0%
= 1 review
= 20 employees
= 100,000 visitors

Analyze images and extract the data you need with the Computer Vision API from Microsoft Azure.

60.48255270451894
78.71050984659941
16.216216004714433
80.9247462358469
71.82539682539682
42.25459556243848
top5 , top10
top5 , top10
4star
Amazon Rekognition
4.30
100%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

Amazon Rekognition makes it easy to add image and video analysis to your applications.

55.50489563172996
73.0245697258547
19.819818691810315
77.05254450187986
0.019444444444444445
37.98522153760521
top5 , top10
5star
Clarifai
4.70
100%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

The problems that your business encounters don't change very often. The way you can solve those problems just has, with Clarifai.

53.999855414287765
71.19192190983661
0.4054073565093469
72.53165924376738
100
36.80778891873893
top5 , top10
4star
IBM Watson Visual Recognition
4.30
100%
100%
15%
= 1 review
= 20 employees
= 100,000 visitors

53.72990386069419
69.79383294105678
0.9459465857388372
74.21844201687826
0.003968253968253968
37.6659747803316
4star
Torch
4.40
100%
22%
35%
= 1 review
= 20 employees
= 100,000 visitors

48.01503710046947
62.28900996504854
1.1711702863887157
66.22752644303924
0
33.7410642358904
5star
SimpleCV
4.50
100%
0%
44%
= 1 review
= 20 employees
= 100,000 visitors

47.15257856616876
61.04613107554409
5.855855344726549
64.75580363319393
0
33.25902605679343
top10
4star
scikit-image
4.40
100%
0%
100%
= 1 review
= 20 employees
= 100,000 visitors

47.07238521075432
61.293547322186896
0
64.09774537821302
34.72222222222222
32.85122309932175
top5 , top10
4star
Microsoft Video API
3.80
100%
100%
0%
= 1 review
= 20 employees
= 100,000 visitors

Quickly extract insights from videos using artificial intelligence

46.71630006690005
60.92118400649319
2.657656101357043
63.530226230623214
37.43472222222222
32.51141612730691
top5 , top10
4star
Google Cloud Vision API
4.00
100%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

Derive insights from images in the cloud or at the edge with AutoML Vision, or use pre-trained Vision API models to detect emotion, text, and more.

Market Presence Metrics

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, image recognition software is less concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 65% (9% less than average) of search queries in this area.

Web Traffic

Image recognition software is a less concentrated than average solution category in terms of web traffic. Top 3 companies receive 61% (12% less than average solution category) of the online visitors on image recognition software company websites.

Satisfaction

Image recognition software is less concentrated than average in terms of user reviews. Top 3 companies receive 38% (20% less than average solution category) of the reviews on image recognition software company websites. Product satisfaction tends to be slightly lower for more popular image recognition software products. Average rating for top 3 products is 4.3 vs 4.4 for average image recognition software product review.

Leaders Average Review Score Number of Reviews

Maturity

IBM
Amazon Web Services (AWS)
Google
Microsoft

Number of Employees

Median number of employees that provide image recognition software is 68 which is 14 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 42 companies (7 less than average solution category) with >10 employees are offering image recognition software. Top 3 products are developed by companies with a total of 0.5-1M employees. However, 2 of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

Learn More About Image Recognition Software

Why is image recognition software important now?

An exponential increase in image data and rapid improvements in deep learning techniques make image recognition more valuable for businesses.

  1. Image data is increasing every day: As camera hardware becomes smaller and integrated into daily life through mobile devices and sensors, the increase in image generation continues. The rapid increase in image data increases the demand for processing this data and making it useful. There are image processing applications in e-commerce, supply chain, retail, automotive and other industries.
  1. Increased effectiveness of deep learning: Deep learning enables fast and accurate image processing. Deep learning is becoming more powerful thanks to both advances in hardware and algorithms. As it gets cheaper and faster, businesses can integrate image recognition solutions into their business. According to MarketsandMarkets “image recognition market is estimated to grow from USD 16 billion in 2016 to USD 39 billion by 2021, at the CAGR of 20% during the forecast period.”. For more on why deep learning is impactful, feel free to check out our article on the topic

How does image recognition software work?

Latest image recognition software uses deep learning networks. The most used deep learning model is an artificial neural network model called convolutional neural networks (CNN).

Before the image is recognized, it must first be preprocessed and the useless features (i.e. noise) must be filtered. The preprocessed images are evaluated pixel by pixel.

The numerical value of each pixel is associated with another pixel using an operator called convolution. The objects in the image are represented by mathematical vectors and classified as a result of this method. For example, in order to identify pictures containing cars, a set of images that contains cars is processed. Then a vector which is describing the car in images is obtained. The first set of data is called training data. Then new pictures are tested on the model to understand its accuracy. This set of data is called the test data. Check out our research to learn more about how image recognition technology works

What are image recognition software use cases?

Image recognition technology can be applied in all areas where image acquisition is possible. Our research analyzed the industries and business functions where image recognition software is used frequently:

  • Automotive Industry
  • Security Industry
  • Healthcare
  • Retail
  • Visual search and e-commerce
  • Marketing

For example, image recognition technology is used to enable autonomous driving from cameras integrated in cars. Another example is the diagnosis in healthcare. The software enables faster and accurate medical imaging. For an in-depth analysis of AI-powered medical imaging technology, feel free to read our research.

What are the things to pay attention to while choosing image recognition solutions?

While choosing image recognition software, the software's accuracy rate, recognition speed, classification success, continuous development and installation simplicity are the main factors to consider.

  • Accuracy: Most of the times, this is the most important factor. However, in real time usage, speed can be as important. We have explained a few ways to measure accuracy of machine learning models.
  • Continuous learning: Every AI vendor boasts of continuous learning but few achieve it. The ideal solution should be learning from its incorrect predictions (inferences in machine learning jargon). The necessary volume for learning is also important. A model that requires thousands of examples for improving its model would be slow to improve itself.
  • Speed:The solution must be fast enough for the necessary application. While a customer-facing solution may require a response within milliseconds, a solution for internal use can be OK to be produced within a few hours or even days.
  • Flexibility: It is important to foresee the constraints of the future and adaptability of the solution for the future needs is important.
  • Ease of setup and integration: The solution should be easy to setup and use. Since most solutions will be API endpoints, they tend to be easy-to-setup.