PerceptiLabs

Use Case: Classifying Wood Veneers Into Dry and Wet

Industrial IoT is playing a significant role in Industry 4.0, especially for automating manufacturing processes, including manufacturing wood veneers. We built an image recognition model that could identify veneers as either dry or wet.

healthcare

Use Case: Detecting Defective Pills

To help automate the detection of defects in medical pills we set out to build an image recognition model in PerceptiLabs that could identify defective pills by analyzing images.

Health and Safety

Use Case: Classifying Ways to Wear a Face Mask

The spread of COVID-19 required that masks be worn correctly. We set out to build an image recognition model in PerceptiLabs which could classify different ways that people wear masks.

Optometry

Use Case: Ocular Disease Recognition

We decided to build an ML model in PerceptiLabs that applies image recognition techniques on fundus images to detect possible cataracts in patients. Using a model like this could help doctors, optometrists, and researchers to more easily classify and detect such conditions.

medical

Use Case: Breast Cancer Detection

Inspired by the growing use of ML in healthcare, we decided to build an image recognition model in PerceptiLabs trained on malignant and benign microscopic biopsy images.

Top Five TensorFlow Issues Solved by PerceptiLabs

TensorFlow is a powerful, open-ended API for building ML models, but it does have shortcomings. In our blog An Overview of TensorFlow and how PerceptiLabs Makes it Easier, we discussed how TensorFlow works and how PerceptiLabs' GUI and visual API make it easier to build TensorFlow models.

Manufacturing

Use Case: Air Conditioner Piston Check

With the growing use of computer vision in Industrial IoT (IIoT) and Industry 4.0 to analyze and detect defects, we were inspired to build an image recognition model that can classify images of AC pistons.

Machine Learning

Case Study: Genetic Screening Using CNNs in PerceptiLabs

One group of scientists leveraging ML is the Buchser lab at Washington University in Saint Louis School of Medicine in the Department of Genetics. There, Dr. Buchser and his team are working in functional genomics, High-Throughput Screening, and Next-Generation Sequencing.

Manufacturing

Use Case: Defect Detection in Metal Surfaces

With the rise of Industrial IoT (IIoT) and Industry 4.0, ML is playing an ever-increasing role in enabling automation. With this in mind, we built an ML model that can classify defects on metal materials using image recognition.

Machine Learning

Use Case: Skin Cancer Classification

To accurately diagnose cancer cases, doctors need every possible tool at their disposal. In the case of skin cancer, the affected areas can often be visually examined without the need for invasive diagnostics typically involved with cancers inside the body.

Use Case

Use Case: Classifying Chest X-Rays to Detect Pneumonia

To accurately diagnose ailments, doctors need every possible tool at their disposal. This Use Case shows how to classify chest x-rays to detect Pneumonia.

image classification

Use Case: Automated Weather Analysis Using Image Recognition

Many industries have the need to identify current and past weather conditions. The data helps them plan, organize, and/or optimize their operations. This Use Case shows automated weather analysis using Image Recognition.

Machine Learning

Essential Lingo for the Aspiring ML Practitioner

Every industry has its lingo and machine learning (ML) is no different. Here's a brief checklist of key terminology that all aspiring ML developers should be familiar with when building models.

Machine Learning

Tips to Steer Your Developer Career Towards Machine Learning

Excited by the hype around machine learning (ML)? Perhaps you’d like to transition into the field but you’re not sure where to start. Learn more here.

Machine Learning

Inference for the Real World: What to do with your ML Model Once You've Trained it

The end result of the ML modeling process is a file containing the model's trained weights ready for inference. But once you have this file, how do you actually use it for inference?

Machine Learning

The Power of Abstraction: How ML Frameworks and PerceptiLabs Provide a more Human-Readable Language for Describing ML Models

Everyone wants to enhance their understanding of machine learning or ML these days. But what does it really mean to do ML?

Loss Function

Choosing and Customizing Loss Functions for Image Processing

A loss function is the algorithm used during training to determine how well your model can make predictions using a given set of weights and biases. In this blog we review how a loss function works, its role and importance in optimization, how to choose one, and how to work with it in Perceptilabs.

visual modeling tool

Comparing PerceptiLabs to Pure Code in Transfer Learning

Pure-code ML frameworks like TensorFlow are powerful tools, but are often limited when it comes to the insight and explainability of models. In this blog we explore these issues, and introduce PerceptiLabs to solve these challenges.

Machine Learning

Transfer Learning Part II: When to use them in Image Processing

Transfer learning allows you to leverage an existing model by modifying and retraining it to fulfill a new use case. In this blog, we explore some key scenarios for why and when you should choose transfer learning over building a new machine learning model from scratch for image processing.

Machine Learning

Five GANs for Better Image Processing

Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.

Machine Learning

Don’t Start your Model from Scratch - Use Transfer Learning

Did you ever wish you could use one of your existing models as a starting point to solve a similar problem? Then you might want to try Transfer Learning. Transfer learning means to use or adapt an existing machine learning (ML) model for a new purpose and helps get you

Top Five Ways That Machine Learning is Being Used for Image Processing and Computer Vision

The field of digital image processing is increasingly shifting from deterministic mathematical calculations and statistical methods, to machine learning (ML)-based approaches which can provide better and more accurate results. This in turn is also helping to drive new methods and use cases for computer vision in the pursuit of

Machine Learning

Recreating Nvidia’s PilotNet in PerceptiLabs

Computer Vision is a key technology for building algorithms to enable self-driving cars. One of the pioneering projects in this field was an experimental system called PilotNet by Nvidia. It uses a deep neural network (DNN) that takes image frames from cameras mounted on the front of a car and

What’s next? Machine Learning 2021

Ah 2020! From global healthcare issues to revolutions in how technology is being adopted and even repurposed, it has been quite a year. At the end of each year, it's always fun to pause and think about machine learning (ML) trends which have seen phenomenal growth, especially around tools, resources,

Machine Learning

Going Beyond the Repo: How Contributing to GitHub can Help Your Career Growth in Machine Learning

Version control tools have long been a staple for information workers, especially developers who need a place to store and collaborate on their code base, while maintaining a complete history of changes. Over the years many such tools have come and gone, or at least fallen out of flavour, but