PerceptiLabs - PerceptiLabs

Benchmarking DL Models: How to do it Right

Check this out to discuss the importance of benchmarking models to derive an optimal version and show how PerceptiLabs makes it easy for you.

Low-code

Using Evaluations and Comparisons to Make Awesome DL Models

Think your DL model is awesome? Think again. In this blog, we'll review some metrics to evaluate your model to uncover whether it's actually solving your problem.

Low-code

Integrations: FastAPI for Fast SaaS Application Deployments

This blog shows why PerceptiLabs added the FastAPI deployment target, and to inspire how you might build on that output.

AI

Integrations: Gradio for Quick and Easy Model Testing

You asked how to deploy, use, and even test a model that has been exported by PerceptiLabs. So, we added a Gradio deployment target. This blog discusses the rationale behind this target and how you can use it to test your model.

Low-code

Low-Code: The Best Approach for TensorFlow

PerceptiLabs adopted a low-code approach right from the start to increase your DL workflow productivity, provide tools for blazingly-fast modeling, and get you to a working model faster. This blog explores why low-code is so effective for working with TensorFlow code.

Machine Learning

Apply Datasets and Pre-Trained Models Faster and Easier with PerceptiLabs

Our mission is evolving to help you apply your DL models to real-world problems, easier, and quicker. How could we possibly do that? Read on to find out!

Machine Learning

Overcoming Deep Double Descent Using PerceptiLabs

If you've ever heard the phrase: "this is gonna get worse before it gets better", then you're already halfway there to understanding Deep Double Descent. In this blog we review the Deep Double Descent phenomenon and how you can use PerceptiLabs to overcome it.

Use Case

Use Case: Wildfire Detection

Every year, millions of hectares of forest are lost due to the spread of wildfires. However, using image recognition, authorities may now be able to identify wildfires from live-streamed images from cameras on highways or buildings.

healthcare

Use Case: Highlighting Blood Cells in Dark-field Microscopy Using Image Segmentation

An application of dark-field microscopy is to view blood sample features such as blood cells. We created a use case for applying image segmentation to dark-field microscopy images using PerceptiLabs.

Machine Learning

A Guide to Using U-Nets for Image Segmentation

Image segmentation is a powerful technique to locate objects or boundaries in images on a per-pixel level. In this blog we take a quick look at how U-Nets work, explore how they build on standard CNNs, and show how easy it is to build and iterate on U-Nets in PerceptiLabs.

Personal Assitant

Use Case: A Voice Recognition model using Image Recognition

Spoken language is the most natural form of human interaction, and thanks to advancements in modern-day ML, it's fast becoming the perfect way to interact with computers.

Retail

Use Case: Classifying Fruit

The handling of perishable foods like fruits and vegetables in a supply chain can involve many processes. Traditionally, these processes were done by hand, but we built an image recognition model in PerceptiLabs, to see if it could identify different types of fruits simply by analyzing images.

Machine Learning

Working With Unbalanced Datasets

ML practitioners wrangle data to create the best training datasets possible for their models. This generally means that the data is unbiased, well-structured, and will help to train a model that gives good predictive performance in the real world. Whether you collect data yourself from the real world, use an

healthcare

Use Case: Retinal OCT

The human eye is a complex sensing organ that reacts to light to provide us with a field of view into the world around us. Taking care of our eyes is an important aspect of our overall healthcare. However, our eyes are susceptible to a number of diseases, some of

Manufacturing

Use Case: Fabric Stain Classification

The global textile industry impacts nearly every human being on the planet and had an estimated size of $1000.3 billion USD in 20201. It includes the production, refinement, and sale of both synthetic and natural fibers used in thousands of industries. The high demand for quality textiles has led

IIoT

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.