Computer vision & custom model: Which AI provider to choose ?

Computer vision & custom model: Which AI provider to choose ?

In this article, we expose how using AI pipeline easily permits to solve complex use cases requiring OCR and text analysis.

In recent  years, within the world of Artificial Intelligence (AI), one of the most  popular applications is computer vision. The main reason for this  success is the great diversity of the market and needs: medical imaging,  industry, transport etc. In particular, computer vision enables image  recognition for all control processes, object detection and facial detection.

What are the Computer Vision solutions?

Many companies use object detection to automate control processes for example. They are then faced with two choices:

1. Using pre-trained models (models already trained to classify a specific kind of “object”) of the major AI providers in computer vision  (Google Cloud, Amazon Web Services, IBM Watson, Microsoft Azure,  Clarifai, etc.) in this case the object they want to identify is  automatically recognized by these models.

2. If no pre-trained model is satisfactory, then the user will have to train a specific model for his own needs. In this case he has two possibilities:

  • Create a customized solution, choose the algorithm himself and build the predictive model from scratch.
  • Use an API / Console allowing the creation of a custom model automatically

What is AutoML?

The last possibility is based on a technology called Auto Machine Learning  (AutoML) applied to the Deep Learning algorithms (neural networks) used in vision. This service has been created to respond to the desire to  democratize AI and to allow developers without a real knowledge of Machine Learning to be able to train models easily.This  AI service offers the possibility to obtain fast and accurate results  at a lower cost, and without the expertise of a data scientist on a subject like computer vision which uses very complex algorithms.  

However, using automatic generation of the custom model also means  giving up total control over the technical solution and the algorithms  behind the model.Although  many of the techniques used by AutoML are known, such as Transfer  Learning (using existing pre-trained model algorithms to detect similar  objects) and Neural Architecture Search (an algorithm for building neural networks from assembled, deleted, added blocks), the algorithm is  never precisely known by the user.

Who are the Providers? for AutoML?

During  our study of AutoML Vision (name associated with Google, but considered  in this article as generalist), we projected ourselves in the role of a  company that wants to develop an image classification project for a  very specific object. We embody a company that does not have an AI  expert and wishing to obtain a high level of performance at a lower  cost, therefore without using a service provider. So the first question that comes to our minds is, “Which provider do I  choose?” A first observation appears after going through the market:  only a few providers offer an AutoML Vision service.We decide to test 4 of them:

As an early-stage company in the field of AI, we took a look at the  Forester Computer Vision New Wave. We looked at the four highest ranked providers platform and checked if they provide an AutoML Vision service. We could have chosen other providers like IBM Watson Visual Recognition or by Ximilar.

Forester Computer Vision New Wave

What are the Use Cases for AutoML?

In order to have a clear view of the market and the different providers,  we benchmarked these 4 solutions on two different projects. Two image classification projects with real differences: database size, labels, domain, database quality. So we are going to carry out these two projects to analyse the results of the four suppliers respectively on the two projects.

The first project consists in the recognition of a specific box used for the implementation of a new generation internet connection. The objective is  the creation of a model which allows you to indicate the presence or not of this box in any photo. We have a dataset of 2586 images labelled as “box” and 1013 negative images (without the box).

Image of box a used for the  implementation of a new generation internet connection

The second project aims at the recognition of melanoma. The aim is to create a model to classify melanomas according to whether they are  benign or malignant. For this project, we have a dataset composed of 460 images with the label “benign” and 462 images with the label “malignant”.

Two pictures of two different stages of melanoma

Here , we tested solutions in two different fields, with two different  problems. This can show us if there is really a need to carry out the  test for every different project.

Advantages and Disadvantages of solutions

After having apprehended and taken in hand the 4 solutions on two distinct  use cases, many differences appeared between the solutions in approach and in use. The first remark concerns the very first step: create an account (and get API token if using API). This step is laborious and already takes some time. It remains more time-consuming on Google and Amazon, assuming you have no experience on these platforms.

Then, the process leads us to the AutoML Vision platform. This is evident in  Google and Microsoft. Even more intuitive for Clarifai, which only offers Vision services. For Amazon, on the other hand, the task is much more complicated: accessing their new Amazon Rekognition Custom Label service remains a mystery. We used a direct link to this service to access it. It’s easy to get lost, much more complicated to quickly  access the interface to build a model.

Then  we come to the import of the database (images). First of all, it is  important to specify that Amazon and Google force the user to store the database in their Cloud service in order to be able to use it for the  model. Concerning the file format, the classic image formats (png, jpg)  are accepted for all providers. More specific formats may be tolerated (some only by the corresponding API). The problem is mainly for the  labeling step. Assigning to each image its own label(s) can be tedious:

  • With Google, you have to import a .csv file that associates to each image its labels
  • With  Microsoft, you have to import images by groups and assign them a label.  The functionality to add unlabeled images and label them manually is  also available.

However, Microsoft forces the user to duplicate images in several imports if you process multi-label images.

  • At  Clarifai, importing is done manually. You can only import by requests  of 128 images, you have to import images in the “concepts” directly  (possibility to import more than 128 images per request, but the  fluidity leaves something to be desired).
  • At  Amazon you can import images into folders (corresponding to labels),  you can also label the images individually if needed. A minimum of two  labels are required to start the training.

Note the availability of the Amazon SageMaker GroundTruth service which will allow you to have your data labeled “automatically” by AWS.

Please note that these remarks concern only the console of each provider,  considering that this method does not require any technical abilities. By using the APIs, it is obviously possible to generate a label by group of images via a few lines of code, and to facilitate multi labeling for all solutions.

Concerning the pricing, the offers are as follows:

Computer vision solutions costs

As  you can see, these price indications make it more than complex to estimate a final cost that will be charged to you. Nevertheless, this table gives an overview of the most cost-effective solutions according to your needs.

If you just want to try training multiple custom vision models, then Amazon and Clarifai will have reasonable cost. On the contrary, if you’re a company and you want to use your model to predict a large set of images, training cost is negligible. You will have to focus on Model Usage costs.

We can therefore already define a financial strategy on the choice of supplier, without even knowing the exact cost of operations.

However, beware of additional charges, especially for data storage, which can cost without you even noticing!

The next step concerns the launch of the automatic model drive and the parameters over which the user has control.

Model Training and results

Once the dataset has been imported and tagged, it’s time to start model  training. The two parameters that the user can control are the training time and the train set / test set distribution.

Google allows the user to define the number of nodes for the training (8 nodes  ~ 1 hour of computation), and to define in the .csv file, for each image if it is part of the train set or the test set (if the user doesn’t fill in the distribution, it will be set by default by Google : 80% train, 10% val, 10% test).

Clarifai does not allow any user intervention on these parameters: the automatic distribution is 80% train, 20% test.

Microsoft  offers the choice to the user either to define himself the training  time, or to let Microsoft do it for him (Quick train / Advanced Train).  However, it is not possible to change the Train / Test distribution.

Finally Amazon does not allow you to set a personalized training time, but it offers a customization of the train / test distribution quite advanced:

  • either the user imports a train dataset and a test dataset
  • or the user creates a test data set
  • or he chooses Amazon’s automatic 80/20 split

Evaluation of the model

One of the most important steps is the evaluation of the model. This allows the user to determine, according to performance criteria linked to the user’s expectations, whether the model is reliable or not. Several indicators allow this. All the services tested give as metrics:  precision and recall, and offers the possibility to manually consult the test dataset in order to observe where the model was wrong.

The precision answers the question : What proportion of positive identifications was actually correct?

The recall answers the question : What proportion of actual positive results were correctly identified?

We  can also use the confusion matrix, on the Google and Clarifai service,  to characterize the type of error of the model and the proportion.

Overall, Clarifai and Google offers a more thorough evaluation of the model,  with interesting metrics and statistics. Amazon and Microsoft, on the other hand, keep to the bare minimum by highlighting only the basic metrics.

All the providers give a general metric supposed to represent the general  accuracy of the model, but this metric is not the same for all the  providers. Moreover, they do not really expose how it is calculated. This does not seem to be a good reference to use to compare the models.

Evaluation for Internet box model

Table of the evaluation for Internet box model

Evaluation for Melanoma model

Table of the evolution for the Melanoma model

Here, we can see that for the Internet box use case, if we want the best  precision, we choose Google, if we want the best recall, we choose Amazon.

For the Melanoma use case, we would choose Microsoft for the best precision and Amazon for the best recall. In the melanoma project, we must choose the provider with the best recall  because we want a model which misses as little malignant melanoma as possible. In the internet box project, we should look at the precision because we want the model not to predict that there is a box if there is not.

As we can see, depending on your database, and your project, providers do not perform with the same accuracy. Testing many providers must be the only way to choose which one you are going to use. First of all, performances are not regular depending on the project, you can look for the best precision, or the best recall, and there is never one provider which is the best for every project, for every database.

Once the model has been trained, it can finally be used. Each provider  offers somewhat different services. Microsoft and Google allow you to test the model online on the console by importing images individually.

With  Clarifai, we can create a workflow in the Explorer (console) and we can  use our models to predict. One request is limited to 32 inputs.

All providers allow the user to use online prediction via a REST API.

User Experience

During the test of all these solutions, we obviously encountered problems specific to our use as a normal user.

Google’s platform took us a long time to master, but once taken in hand, it is pretty ergonomic. The problem with the error management is that an error occurred during the training and there is no way to know the cause. Also, some data were not labelled as indicated in the .csv file. The only solution to these errors, whose source is unknown, is to contact the technical support, which is not free of charge!

Same observation for Amazon, some problems when importing the dataset for  the model training, as well as problems preventing to visualize the  model evaluation, and no indication as to the cause of these problems. Two solutions, investigate these errors yourself at the risk of losing a  lot of time, or contact a support once again paying. The lack of user control over the threshold also severely handicapped us when we had to evaluate the model and compare it to others.

The  user experience for Clarifai was quite laborious. A few problems of  fluidity and clarity when importing images in particular, disrupted the  process, as well as the process itself.

that  some graphical bugs, especially in the evaluation of results. From our point of view, it would be better for Clarifai to judge them on their API, which is much more advanced, than their console.

Finally, Microsoft offers a very intuitive interface. It is the interface on  which we spent the least time and encountered no notable problems. The  oversimplified and accessible side is assumed, perhaps a little too much  to our liking when we move on to the evaluation of the model.

For each project, each use case, an analysis is necessary in order to evaluate costs, uses and performance. It was observed during this study  that each case is specific and we cannot be certain of the choice of  solution until we have tested multiple solutions available on the market. Some solutions can bring very low results, and others excellent ones, and this logic can totally change for another use case. Also, depending on the project, priority will be given to costs, results, calculation times and number of queries per second, or ease of use and  handling. These are all criteria that can impact the decision, and allows the user to choose the solution that best suits the project, the most relevant solution.

It is on this basis that our Eden AI offer comes into play. Thanks to our in-depth expertise in the use of these different Artificial Intelligence solutions, we are able to  provide the recommendation most suited to your problem, and save you a lot of time and money.

You are a solution provider and wish to integrate Eden AI, contact us at :

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.

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