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How to Implement Inventory Management in Retail Using Computer Vision

inventory management in retail

The retail industry is turning to AI and Machine Learning to try and gain an edge over competitors. Inventory management system for retail stores is one of the domains where AI and Machine learning can play an important role in optimizing business processes. From planning for the next month’s shelf supply to customer service inquiries, inventory management is going to incorporate the concept of smart automation in their daily processes over the coming years.

The new frontier of inventory management and supply chain allows companies to use an immense amount of data to reduce overhead costs, exceed customer expectations, and increase ROI. New technologies like Computer vision can utilize videos and images to extract, analyze and identify patterns that can be used for automating everyday business operations. Applications of Computer vision in retail include shelf management, easier payment process, data collection, and compliance. This application can also help retailers to improve store security by monitoring the stores, spot suspicious behavior, and avoid theft. as a Computer Vision technology platform

The goal of Computer Vision is to understand the data of digital images and interpret them beyond human vision. can help in the analysis and categorization of images in an effective and scalable manner with state-of-the-art computer vision technology, which is based on neural network architecture. We provide templatized and guided workflows for machine learning projects that are easy to understand and follow. There are two kinds of Computer Vision templates offered - Image Classification Multiclass and Image Classification Multilabel.

Image classification Multiclass

Multiclass classifies an image into one of two or more classes. It is a classification task with more than two classes, for example with a set of images including oranges, apples, and pears, Multiclass classification will assume that every sample is assigned to only one label. Hence, fruit can only be an apple or a pear, but not both at the same time.

Image classification Multilabel

This is a type of classification where an image can be categorized into more than one class. For example, an image of a cat or dog can be categorized as a cat or dog, as well as into multiple labels based on different categories and subcategories. They can be labeled as per different attributes such as color, breed, type, etc. Therefore, the image can be classified into any one of the multiple categories.

The following screenshot shows the Machine Learning templates offered by that can be selected for a project.

Screenshot 1 - border-1 is an end-to-end automated Machine Learning implementation platform that enables organizations to attain actionable insights from unstructured data such as image, text, audio, etc, using a guided workflow. helps decision-makers, data scientists and project managers to build Machine Learning models from concept to production in a matter of weeks and not months.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Key benefits of as a platform

  • It is a unified AI Platform that enables Data Collection, Data Labeling, Data Visualization, Model training, deployment, and Monitoring.
  • Guided Machine Learning workflow makes it easy even for BAs/PMs to start Machine Learning experimentation
  • No infrastructure setup required
  • Complete visibility at all stages
  • Allows you to take your experiments to production in no time with scale
  • Faster model release iteration cycles

The following screenshot shows image visualization or visual representation of data on

machine learning image classification

As shown in the screenshot, column-wise statistics are given around each column to build data intuition.

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How used Computer Vision to build a smart inventory management for retail implemented a use case that could help inventory management in retail stores. Skyl built a computer vision inventory management solution that can figure out if items on the shelf are running low. This prediction can be used to send notifications to employees for restocking thus removing the overhead of periodical checking of items on the shelves, whilst also driving revenues through high product availability. collected various images of shelves in retail stores and created a project called ‘Stock Inventory Management’ with the following steps:

Step 1: Creating a project provides multiple templates for a guided machine learning workflow that can be chosen depending on the use case being implemented. For this project, Skyl chose the Image Classification Multiclass template. An image classification Multiclass template categorizes an image into exactly one class from the given options.

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Step 2: Designing the dataset schema

We then designed the schema of the dataset through the guided flow of We provided the dataset name, description and designed our schema as per the requirements of our project. We gave “optimum_stock” and “needs_restocking” as the options for our category column.

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Step 3: Collecting Data provides various ways of data collection like Collect API, Form-Based upload and CSV upload. We used CSV upload to upload the data to the dataset. You can easily see the format in which the CSV file needs to be uploaded by downloading the schema from the button provided on the top right-hand side of the drag and drop window.

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Step 4: Creating the featureset

In no time, the data was uploaded to the dataset and we created our featureset for Machine Learning Training. A featureset is a subset of your dataset which is used as the input to your machine learning algorithm. also provides a summary of the featureset you are creating so you can analyze if your featureset is properly balanced and there are no biases or bad data. After all, your machine learning model is only as good as the data its being fed with.

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Step 5: Model Training

We initiated the model training using Skyl’s suggested algorithms and parameters. allows you to tune parameters like batch size, the number of epochs, learning rate, etc. as well as suggests the best possible optimized training parameters for your model training. After tuning the parameters, we initiated the training by clicking the ‘Train’ button.

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Step 6: Model Deployment

As soon as the training finished, a model was created which listed under ‘Model Deployment’. The model achieved an accuracy of 84%. Training reports for the model were also generated for metrics like loss, accuracy, recall, precision, etc. allows one-click model deployment for your models, thus eliminating all the work required for setting up a model deployment pipeline.

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Step 7: Inference API

Inference APIs were generated once the model was deployed which could be hooked in any application and can be used for predictions.’s inference API is easy to use and available in all major programming languages for seamless integration.

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Computer vision as a discipline is still growing and as the technology matures it is bound to become more common, not only as inventory management software for retail but in many other industries. We’re headed towards a more smoother retail experience, made possible with a combination of Computer Vision and deep learning.

At we build custom computer vision solutions to improve the future of businesses.

To understand images with industry-leading accuracy, build and deploy your own computer vision models with our risk-free trial.