Image Recognition Software
Image recognition software allows users to classify images and identify entities within images +Show More
Products | Position | Customer satisfaction | |||
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Leader
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Satisfactory
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Achieve retail excellence by improving communication, processes and execution in-store with YOOBIC.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
5-10 case studies
Company's number of employees
200-300 employees
Company's social media followers
10k-20k followers
Total funding
$50-100m
# of funding rounds
4
Latest funding date
July 22, 2021
Last funding amount
$50-100m
Company
Type of company
private
Founding year
2014
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Leader
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Satisfactory
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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.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
50m-60m followers
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Leader
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Satisfactory
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Analyze images and extract the data you need with the Computer Vision API from Microsoft Azure.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
30m-40m followers
Company
Type of company
private
Founding year
2011
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Leader
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Satisfactory
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OpenCV is a tool that has has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android for computational efficiency and with a strong focus on real-time applications, written in optimized C/C++, the library can take advantage of multi-core processing and enabled to take advantage of the hardware acceleration of the underlying heterogeneous compute platform
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
50-100 employees
Company's social media followers
100k-1m followers
Total funding
$250,000-500,000
# of funding rounds
1
Latest funding date
May 23, 2019
Last funding amount
$250,000-500,000
Company
Type of company
private
Founding year
1999
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Challenger
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Satisfactory
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Amazon Rekognition makes it easy to add image and video analysis to your applications.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
10m-20m followers
Company
Type of company
private
Founding year
1996
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Challenger
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Satisfactory
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Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
300-400 employees
Company's social media followers
30k-40k followers
Company
Type of company
private
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Challenger
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Satisfactory
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Clarifai is a leading deep learning AI platform for computer vision, natural language processing, and automatic speech recognition. We help enterprises and public sector organizations transform unstructured images, video, text, and audio data into structured data, significantly faster and more accurately than humans would be able to do on their own.
The platform comes with the broadest repository of pre-trained, out-of-the-box AI models built with millions of inputs and context. They detect explicit content, faces as well as predict attributes such as food, textures, colors and people within unstructured image, video and text data. Our models give you a head start; extending your own custom models.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Number of case studies
20-30 case studies
Company's number of employees
100-200 employees
Company's social media followers
50k-100k followers
Total funding
$100-250m
# of funding rounds
4
Latest funding date
October 15, 2021
Last funding amount
$50-100m
Company
Type of company
private
Founding year
2013
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Challenger
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Satisfactory
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Caffe is a deep learning framework made with expression, speed, and modularity in mind.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
400-1k employees
Company's social media followers
400-1k followers
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Niche Player
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Satisfactory
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Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
10m-20m followers
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Niche Player
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Satisfactory
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The custom vision API from Microsoft Azure learns to recognize specific content in imagery and becomes smarter with training and time.
Basis for EvaluationWe made these evaluations based on the following parameters; Customer satisfaction
Average rating
Market presence
Company's number of employees
100k-1m employees
Company's social media followers
30m-40m followers
Company
Type of company
private
Founding year
2011
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“-”: AIMultiple team has not yet verified that vendor provides the specified feature. AIMultiple team focuses on feature verification for top 10 vendors.
Sources
AIMultiple uses these data sources for ranking solutions and awarding badges in image recognition software:
Image Recognition Leaders
According to the weighted combination of 4 metrics





What are image recognition
customer satisfaction leaders?
Taking into account the latest metrics outlined below, these are the current image recognition customer satisfaction leaders:





Which image recognition solution provides the most customer satisfaction?
AIMultiple uses product and service reviews from multiple review platforms in determining customer satisfaction.
While deciding a product's level of customer satisfaction, AIMultiple takes into account its number of reviews, how reviewers rate it and the recency of reviews.
- Number of reviews is important because it is easier to get a small number of high ratings than a high number of them.
- Recency is important as products are always evolving.
- Reviews older than 5 years are not taken into consideration
- older than 12 months have reduced impact in average ratings in line with their date of publishing.
What are image recognition
market leaders?
Taking into account the latest metrics outlined below, these are the current image recognition market leaders:





Which Image Recognition products published the most case studies?
We analyzed 59 Image Recognition case studies and found that these products are the top contributors:
- Anyline
- Catchoom CraftAR Image Recognition & Augmented Reality
- Image Recognition
Which one has collected the most reviews?
AIMultiple uses multiple datapoints in identifying market leaders:
- Product line revenue (when available)
- Number of reviews
- Number of case studies
- Number and experience of employees
- Social media presence and engagement
What are the most mature image recognition software?
Which one has the most employees?





Which image recognition companies have the most employees?
94 employees work for a typical company in this solution category which is 71 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 34 companies with >10 employees are offering image recognition software. Top 3 products are developed by companies with a total of 800k employees. The largest company in this domain is IBM with more than 300,000 employees. IBM provides the image recognition solution: IBM Watson Visual Recognition
Insights
What are the most common words describing image recognition software?
This data is collected from customer reviews for all image recognition companies. The most positive word describing image recognition software is “Easy to use” that is used in 11% of the reviews. The most negative one is “Difficult” with which is used in 3% of all the image recognition reviews.
What is the average customer size?
According to customer reviews, most common company size for image recognition customers is 1-50 Employees. Customers with 1-50 Employees make up 42% of image recognition customers. For an average AI Solutions solution, customers with 1-50 Employees make up 33% of total customers.
Customer Evaluation
These scores are the average scores collected from customer reviews for all image recognition software. Image Recognition Software are most positively evaluated in terms of "Overall" but falls behind in "Customer Service".
What are the benefits of Image Recognition?
The most commonly cited benefits of Image Recognition are:
- Time saving
- Faster responses to customers
- Increased visibility
- Increased security
- Improved compliance
- Increased privacy
- Reduced rework
- Scalability
- Better access to restricted content
Discover all Image Recognition benefits
Where are image recognition vendors' HQs located?
Trends
What is the level of interest in image recognition software?
This category was searched on average for 1.3k times per month on search engines in 2024. This number has decreased to 0 in 2025. If we compare with other ai solutions solutions, a typical solution was searched 13.1k times in 2024 and this decreased to 0 in 2025.
Learn more about Image Recognition Software
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
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.
An exponential increase in image data and rapid improvements in deep learning techniques make image recognition more valuable for businesses.
- 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.
- 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
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.