6 Steps Labeling for Image Classification
By: Sahil Mishra
Tags: Image Labeling, Image Classification, Deep Learning, Data Annotation Services, Machine Learning

Have you ever wondered how Instagram takes care of the abusive and inappropriate images shared by some of its users? How Google Photos image tagging works? Or how companies like Twitter, Facebook, Pinterest, Reddit etc. take care of copyrighted content? This classification of images into different categories is what is known as image classification

These are just some of the examples of what image classification can do. Multi-class Image classification has become an important computer vision problem in today’s world.

Introduction

Image classification is the process of identifying and classifying what is present in the images. As human beings, we see a lot of objects everyday and identify most of them very easily and that too non-deliberately. This capability of object identification develops over the age as we gain experience.

As we learn with experience, computers tend to learn with the help of data — lots of data. So, we feed the computer enough data so that it can learn to identify objects quickly. This data is then used to train deep learning models also called artificial neural networks (ANNs). In case of image classification, we use special types of ANNs called convolutional neural networks.

Challenges faced while performing image classification

It looks easy that what we need is just data and neural networks trained on that data and we get a model that classifies images conveniently. But in reality there are a lot of complications.

Lack of labeled data First and the foremost challenge faced is the lack of structured and labeled data. For tasks like image classification, we need labeled data.

First and the foremost challenge faced is the lack of structured and labeled data. For tasks like image classification, we need labeled data. Fine-tuning models While training neural networks for real world applications, we may not get expected performance. This usually happens because either there might be some errors in data preprocessing or inconsistencies in selection of hyperparameters.

How we do it at Labellerr

Labellerr is a data annotation tool powered by artificial intelligence. It is aimed at bridging the gap between layman users and artificial intelligence providing features for a wide range of users from complete techy to beginners. Labellerr provides a lot of such features like:

ML assisted labeling Labellerr provides machine learning assisted labeling features also known as auto labeling.

Labellerr supports Pytorch and TensorFlow

User friendly interface Labellerr’s user interface has been developed in such a way that even a beginner in ML field can train their models with ease.

Labellerr’s user interface has been developed in such a way that even a beginner in ML field can train their models with ease. Faster labeling speed Labellerr supports multiple annotators at a time to increase the productivity in the labeling process.

Labellerr supports multiple annotators at a time to increase the productivity in the labeling process. Multiple data connectors to import data Labellerr provides you with support of all these data storage options like Google Drive, Google Cloud Storage, AWS S3, Dropbox, local storage, as csv of file links etc. Labellerr recently launched a feature to extract image data by scraping from google.

Connectors available on Labellerr

Easy export of labelled data in various formats. Labellerr supports multiple labelled data export options like csv, json, xml for different use cases.

Now let’s understand how Labellerr has become a breakthrough in the machine learning annotation world with an image classification example:

Problem Statement

Let us take a look at the medical image classification problem- Detection of fractures in X-Rays. For a diagnostician or a doctor, sometimes it becomes very difficult to identify whether there is any evidence of fracture in X-Ray or not. If fracture is not identified on time, then it may lead to bone deformity, permanent nerve damage, and muscle and ligament damage. So, here comes deep learning into the picture. We train neural networks on X-Ray data and then it will identify fractures within a fraction of second.

Dataset Preparation

A model need not be as great if we have a sufficient amount of data. But where does this data come from? It is obvious that a lot of such scans are conducted every day in India and these scans are stored in digital formats. So, the data for this problem statement can be collected from diagnostic centres all across the country. Now, even after collecting the data, it is unstructured and unlabeled. So, we need some kind of labeling tool which should provide some kind of interface to ease the labeling process.

Labellerr provides a seamless interface with a variety of features to label the datasets and structure them in the best possible way.

Soon we are planning to add Tensorflow Datasets and Kaggle Datasets connectors as well.

Sign Up On Labellerr

You can sign up on Labellerr by visiting Labellerr.

Creating new project on Labellerr and import data

When you sign in in your tool for the first time you will automatically be redirected to the project creation page. Or you can go to the settings screen to create a new project.

Creating new project and importing data in Labellerr.

Start Labeling

To start labeling just click on the label navigation tab or click the Start labeling button in the top right corner and you will be redirected to the labeling screen

You can select the labels on the left side of the screen and then click submit to save the labels. You can also skip a file if you are not sure about any particular labels and want to label it later or the image is not clear enough to label it.

Labeling process on Labellerr

Labeling analytics

You can view labeling stats on the dashboard on the home screen.

Statuses and their meanings:

Total — Total number of files linked to the project Submitted — Number of files that have been labelled and reviewed by the reviewer. Reviewed — Number of files that have been labelled and pending for review. Remaining — Number of files yet to be labelled Assigned — Number of files that have been assigned to different users but still pending to get labelled. Skipped — Number of files skipped by annotators.

Labeling trends — Dashboard graph labeling trends on different dates

Labeling trends

ML Assisted Labeling

How does it (training) work? — ML assisted labeling needs little amount of data to be labeled beforehand so that it can use that data to train models. These models are then used for labeling rest of the data automatically. Models are continuously retrained if and when required by Labellerr depending upon the performance they give.

ML assisted labeling needs little amount of data to be labeled beforehand so that it can use that data to train models. These models are then used for labeling rest of the data automatically. Models are continuously retrained if and when required by Labellerr depending upon the performance they give. Prediction on data — While labeling, if the autolabel feature is enabled, you can see suggestions for the questions. You can either just see the suggestion or automatically select the label that is suggested.

Trained model stats (Accuracy) — You can always view the models that have been trained on your data with stats like accuracy and loss specific to each model and select a model you want to use for your autolabel. By default, the best model trained on most files and the best accuracy is used for predictions.

Active models — Used for prediction

Model selection for particular question

Model training history of specific question

Conclusion

Labellerr as an annotation tool, handles the complete pipeline of building an image classification model from preparing data, labeling it with minimum effort ,then training the model with best possible algorithm for your use case and exporting the data in your required format.

Got your own image classification use case ? Signup on Labellerr and start using it to build AI and ML solutions

For any queries, write to us at support@tensormatics.com