Why Training Data is Important for Deep Learning and Computer Vision

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In 2022, deep learning algorithms will be (and already are) all the rage. These AI algorithms are the technology behind automatic inspection, object recognition, and even predictive maintenance & optimization. But if you want to know what fuels deep learning algorithms and the next generation of IoT sensors, you are at the right place. It’s training data.

What is Training Data for Deep Learning?

Training data is a collection of images, videos, or text that has been annotated with labels. The training data is used to teach the computer how to recognize certain objects or patterns in images and videos.

The process for preparing training data usually starts with finding a good dataset. A good dataset contains a wide variety of images (or videos) that cover a wide range of objects or patterns. In many cases, industrial companies will attempt to collect this data themselves.

However, some of the highest value applications of deep learning (like defect detection), are rare, and so data is not always readily available. This is where Simerse can help since we provide labeled training data for AI-powered visual inspection. But for sake of argument, let’s say you want to do this in-house. What is the next step?

Well after finding a good dataset, the next step is to store the training data. This can be done by downloading the dataset and extracting the images (or videos) from your camera into your own computer system.

Next, you will need to annotate the training data. Annotating training data is the process of adding labels to images (or videos). This can be done by hand, but it’s a very time-consuming and tedious task. In fact, getting labeled training data is one of the major bottlenecks in deep learning for industrial companies.

Challenges with Annotating Training Data

So, annotation tools make labeling training images pretty easy, right? Wrong. There are a few major challenges with annotating training data:

  • First, it’s very time-consuming and tedious to do by hand. Most industrial companies don’t have the manpower to do this in-house.
  • Second, annotation tools can be inaccurate and produce inconsistent results. This can lead to incorrect labels being applied to training data, which can distort the training process.
  • Third, labeling training data can be expensive. Industrial companies often have to pay third-party annotation services to get their training data annotated accurately and quickly. These annotations can cost up to $30 per image!

In spite of these challenges, however, training data is still the most important ingredient in deep learning algorithms. Without it, your computer will not be able to learn how to recognize objects or patterns in images and videos. So if you’re looking to implement deep learning in your industrial process, make sure you have a good supply of training data!

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The Solution: Simerse!

Simerse is a trusted provider of training data for deep learning-based visual inspection. We have a large, diverse collection of training data that has been accurately and efficiently prepared by our team of experts specifically for industrial use cases.

By making all of these training datasets available in one place, we make it easy for industrial companies to rapidly develop an AI proof-of-concept. And because our training datasets are pre-annotated, industrial companies can get up and running with deep learning in a fraction of the time it would take to annotate training data on their own.

What kinds of AI Proof-of-Concepts could I do?

Well, the world is your oyster! But for most companies new to AI and deep learning, we recommend starting with something simple.

For example, proof-of-concepts based on anomaly detection. This is the process of training deep learning algorithms to recognize “outliers”. So if a computer detects an outlier in your image data, it can trigger additional actions that are specific to that object or pattern.

So let’s say you’re running a factory and you want to detect defects in your products. You could use anomaly detection to develop a computer vision system that can automatically find and mark defective products for further inspection.

Pretty amazing stuff, right? If you are looking for training data to get started with deep learning and computer vision, Simerse is the perfect place to start!

The Technical Stuff: What is Deep Learning?

Deep learning is a branch of machine learning based on training artificial neural networks. These are computer models that mimic the biological structure of neurons in the human brain, which can be trained to learn patterns and make predictions about data.

The training process for deep learning algorithms involves feeding large amounts of training data into these “neural nets” so they become proficient in recognizing patterns. The more training data you can provide, the better your neural net will be at recognizing objects and patterns in images and videos.

That’s why training data is so important! More training data is better, and Simerse can provide more training data than anyone, especially for Industrial IoT or Industry 4.0 applications.

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Where can Deep Learning AI be deployed?

There are two primary options for deep learning deployment: at the edge, or in the cloud. Fortunately at Simerse, we are well-suited for both.

Deep learning algorithms can be deployed on edge devices such as cameras and sensors, which allows for real-time analysis of data without having to send it to a central processing hub. This is ideal for applications where latency is critical, such as in a high-speed production line or fast-moving factory environment.

Alternatively, deep learning algorithms can be run in the cloud. This means training data is sent to a central server where deep learning training algorithms are applied, and then the results of those training experiments can be used for real-time processing via edge devices.

Cloud-based deep learning requires less infrastructure at the edge device level, but more bandwidth costs since all image data must be transferred from your factory gateways into the cloud every time training is performed.

With all things, it is a tradeoff. Simerse can also help inform your decision, and provide you with the training data and algorithms for industrial deep learning and computer vision.

Summary: Deep Learning for the Win!

Deep learning can help supercharge industrial processes like never before. It is absolutely worthy of investment and can lead to incredible ROI. But training data is essential for deep learning, and that’s where Simerse comes in.

Simerse has the training data you need to get started with deep learning and computer vision for industrial applications, so don’t hesitate to reach out. We’re always happy to help. Thanks for reading!