What is ResNet-50 and How Can It Transform Your Business Automation?

ResNet 50 is an AI model for image recognition, classification, and object detection. Introduced in 2015 by Kaiming He and colleagues from Microsoft Research, it has changed deep learning with its innovative residual learning framework. This model tackled the vanishing gradient problem, allowing for the training of much deeper neural networks.

The following guide covers the ResNet 50 model functionality and architecture. You will get a full overview of how it works, what it's needed for, and where it may be used. In addition, the article shows a simple Latenode scenario featuring its benefits, so you'll have complete know-how on using the integration with it in practice. 

Key Takeaways: ResNet-50 has revolutionized deep learning by effectively addressing the vanishing gradient problem, enabling the training of much deeper neural networks. This guide provides a comprehensive overview of ResNet50, explaining its architecture and practical applications. The article also details how this model is integrated into various AI services, including computer vision APIs, medical imaging, autonomous vehicles, and facial recognition systems. Additionally, it explores how businesses can leverage Latenode to automate workflows using ResNet 50.

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What is ResNet50?

Resnet is a deep learning model neural networks use to recognize images and objects in them. Many developers find that their systems struggle to accurately interpret image information because their layers—the sets of neurons that process data—are poorly trained or not trained at all due to the lack of processing power, inaccurate architecture, etc.

For instance, if you provide a neural network with an image of the wallet, it might incorrectly identify it as a purse or backpack. This issue, known as vanishing gradients, occurs when gradients used to train the network become too small, hindering effective learning and accurate recognition. Resnet-50 is designed to solve this issue.

Gradients are values that indicate how much the neural network parameters (weights) should be adjusted to minimize prediction error. When they vanish or become too small, it hampers the updating of weights, which impedes learning. Gradients are computed during the backpropagation algorithm, which identifies an error, passes it through the network, and adjusts them.

Resnet 50 architecture integrates its two components, residual blocks and skip connections. They work together to incorporate 50 convolutional layers that apply filters to the image and create feature maps. They highlight specific aspects of the image, such as edges, hues, and patterns. After multilayer analysis, it builds a hierarchical representation of the data, capturing increasingly complex features at each successive layer. 

This process helps handle image recognition tasks in the most complex cases. Instead of learning from the entire image all at once, the Resnet50 model analyzes the data piece by piece, passing it through the layers for analysis. Residual blocks allow gradients to flow more smoothly through the network, making it possible to train a deep neural network and surpass traditional limitations.

ResNet has impacted various industries involving pictures, images, and objects. This AI model is often pre-trained on large datasets like ImageNet and then fine-tuned by developers. Its accuracy and efficiency make it popular for many computer vision applications.

AI Services Using ResNet 50 Architecture

This model has become a conduit to better performance for AI systems in many industries where these technologies are needed to accurately recognize disparate objects, patterns, or text in an image. Resnet 50 model can handle recognition tasks for enterprises, computer vision tools, face ID systems, etc. So, check here to learn how this model may be used:

Customer service

ResNet-50 enhances product recommendations and visual search capabilities. Analyzing the visual attributes of products, it provides personalized recommendations, improves customer satisfaction, and eventually increases sales. Additionally, visual search enables customers to find products using images, streamlining the shopping experience and boosting engagement.

Inventory management

ResNet50 model aids in inventory management and loss prevention. For example, its image recognition capabilities allow for real-time monitoring of stock levels and automated restocking alerts. This reduces operational inefficiencies and ensures optimal inventory levels. Notably, the Latenode scenario below simplifies inventory management by classifying and describing the product categories from image you give.

Healthcare

Healthcare businesses can also benefit from ResNet50 architecture. Its ability to detect and classify abnormalities in medical scans, such as MRIs and CTs, aids in early diagnosis and treatment planning. This improves patient outcomes and enhances the efficiency of medical practitioners, reducing diagnosis time and associated costs.

Financial security

ResNet 50 model supports financial services by enhancing fraud detection and customer verification processes. Its advanced image recognition capabilities accurately identify forged documents and fraudulent activities. This improves the security of financial transactions, instilling customer trust and reducing financial losses due to fraud, ultimately strengthening the company's market position.

Latenode Scenarios

Using ResNet-50, businesses and organizations can integrate their services with visual detection features, which improves customer comfort. In addition, this AI model can be used to automate business processes, such as quality control in manufacturing or automated tagging in digital asset management. Latenode provides direct integration with this model. Check the following sections to learn more about this platform and how to create a simple scenario with Resnet50.

Integrating Latenode with ResNet 50 Model and Other Tools

Latenode is an innovative platform that allows you to create automated workflows to simplify various aspects of your business. You can set up complicated scenarios to manage routine tasks like updating your CRM databases, spreading emails to your clients, or even managing communications between your customers and support service. The limit of its capabilities is determined only by your imagination.

Latenode’s advantage is its ability to cooperate with web services through APIs or direct integrations, such as the one with ResNet50. This approach makes work easier for your team, allowing you to shift money and time from routine to more pressing tasks like brainstorming, strategic planning, or product development.

Creating scenarios is like building Lego. You add various nodes, specify their properties, then click Run to see the magic happening. If you need more features or help building an automated workflow, Latenode has a solution. Its JavaScript-based AI assistant can write code to boost the automation of your business even further. 

It can also debug existing code, explain specific terms in different areas or commands of your code, or even suggest customized scenarios while describing each step of your actions. Below is an example of a workflow with ResNet-50 integration made with AI assistance.

Creating Product Category Descriptions with ResNet 50 and LLama 3

This workflow enables product images to be processed by the ResNet-50 node for categorization. It also leverages another AI model, LLama 3, to generate descriptions for categories that these products belong to, which help you quickly build extensive product databases. The guide below explains how everything works.

1. Start your workflow. After registering, you'll be directed to your collection page. Check the upper left corner of the screen, and find the button to create a new scenario. Once you’re inside the script, you'll see an empty field with a single icon in the center that says ‘Add Trigger Node to Begin.’
2. Add the Trigger on Run Once node. Simply click the central icon or a green button at the bottom of the screen to open the library. It features various triggers that can activate scripts by a schedule, interaction with a third-party app, webhook, and more. To find the block you need, search for Triggers ➨ Core Utilities ➨ Trigger on Run Once, as shown in the screenshot.

3. Add the ResNet50 model. In addition to various triggers, you'll find numerous AI model integrations split into folders for text and picture generation, transforming images into text (and vice versa), audio interaction, and more. To get started, look for the Actions ➨ AI: Image Classification Folder ➨ Resnet 50 (Preview) node.

 

4. Customize it. Once you add the node, open the settings. You'll see a single field labeled Image. This setup uses the image URL, but you can also upload a file directly.

5. Implement the Javascript module. There are two versions of this node: Headless Browser and JavaScript. Both use JS, but the former lets you interact with the browser, while the JavaScript version allows you to perform custom actions within the workflow, which you need for this one to work. You'll find the JS node by searching for Core Utilities ➨ Code ➨ JavaScript. Why it’s needed will become clear later.

6. Open the JS settings. A large window will open displaying placeholder code that you need to replace with your own. In this scenario, the JavaScript code converts the ResNet 50 model results into plain text and then sends a prompt to the next node, LLama 3.

 

You can write your own code if you're familiar with programming, or you can use Latenode's unique AI assistant to generate the code for you. It can also fix and modify the code as needed. The screenshot below shows both the request to the AI assistant and the prompt to LLama, as they're in one message.

Once you add the code, you need to make a test run by clicking the button Run Once in the node’s settings. It will create the variable that contains the data for the following node. Here is what the AI-generated code looks like: 

7. Add an integration with LLama 3 node. Tnis is an AI model developed by Facebook to handle user requests for text, images, and more. In Latenode, it’s used specifically for text generation, which fits this workflow perfectly. Notably, there are several LLama nodes. You can find the right once by the route Actions ➨ AI: Text Generation ➨ Llama 3 8B Instruct Prompt (Preview).

8. Customize the block. Once you open the integration, you’ll see two fields: User Prompt and Max Tokens. In the first field, insert a variable generated by JavaScript. The value in the second field controls the length of your response. By default, it’s set to 256 tokens, but in the script, it’s 600 tokens.

9. Start the script. Click on button in the lower part of the screen and watch the entire process. If everything is functioning correctly, all 4 nodes will show green icons. If there's an issue, they will show red and an error message will appear. You can view the work results of both models by clicking on their respective nodes.

Here's how it works. Before running the script, provide ResNet50 with the link to the image you want to classify. Before you add your image, it's important to note that it should depict products out of context. In Latenode, Resnet50 model integration has been trained so far to classify abstract images of animals alone, products without a backdrop, or similar isolated subjects. Testing has shown that this node may produce inaccurate classifications with more complex images.

In this case, it’s an image of wallets, purses and handbags:

The model analyzes it and identifies five possible categories of items: wallet, binder, purse, mailbag, and buckle. The higher the score, the more likely it is that the named objects are present in the image. All the results are processed through the JavaScript node, converted to plain text, and then passed to the next node, LLama 3, along with a prompt.

This node describes each category, allowing you to copy all or parts of the text to create basic product categories for your marketplace or organize your inventory. The scope of applications for this workflow is huge. Here's an example of the text generated by Llama 3 8B Instruct Prompt (Preview):

If your task is to classify items using stock images from marketplaces like Amazon and eBay, and provide descriptions, then this model and script will serve you well.

Make Your Own Latenode Workflow With ResNet 50

ResNet50 model can be used in a wide array of work cases. In addition to this scenario, you can develop an algorithm to enhance customer support by analyzing screenshots and photos of issues, automate image sorting in archives, or tailor scripts for your beauty or medical projects. So, feel free to use this integration in a custom Latenode workflow!

With the free version of Latenode, you can create scenarios with an unlimited number of nodes inside them. Each script activation takes one credit out of a total of 300. Notably, you can buy access to one of three subscription versions, for $17, $47, and $247 per month.

Each version provides more and more features, including increasing the number of your credits, parallel active scripts, added Latenode accounts, and so on. View all three basic subscription types on this page. You can find business options, price comparisons with competitors, and FAQs there.

If you have any questions about automating your business with this service or wonder how it works, check out the rest of the Latenode blog. In addition, you can visit its Discord community server which houses more than 600 low-code enthusiasts worldwide, including Latenode developers.

You can try ResNet-50 for free on Latenode - Your plarform for Business Automation 🚀

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FAQ

What is ResNet-50?

ResNet-50 is a deep learning model used for image recognition. It uses a residual learning framework to address the vanishing gradient problem, allowing for more effective training of deep neural networks.

How does ResNet-50 improve image recognition?

ResNet-50's architecture includes residual blocks and skip connections that enable smoother gradient flow, enhancing the network's ability to learn from data and recognize complex patterns in images.

What are some applications of ResNet-50?

ResNet-50 is used in various applications, including computer vision APIs (e.g., Google Cloud Vision), medical imaging (e.g., Aidoc), autonomous vehicles (e.g., Tesla), and facial recognition systems (e.g., Microsoft Face API).

How can businesses use ResNet-50 with Latenode?

Businesses can integrate ResNet-50 into Latenode to automate tasks like customer support, image sorting, and quality control. Latenode allows for the creation of automated workflows that simplify and enhance business processes.

What is the cost of using Latenode?

Latenode offers a free version with basic features and three subscription plans ($17, $47, and $247 monthly), each providing additional features and credits for script activation.

Where can I find more information and support for Latenode?

More information and support can be found on the Latenode blog and Discord community server, where over 600 low-code enthusiasts, including Latenode developers, share insights and assistance.