Identify wood vs metal screw using AI

Below is a free classifier to identify wood vs metal screw. Just upload your image, and our AI will predict if the screw is made of wood or metal - in just seconds.

API Access


import nyckel

credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("wood-vs-metal-screw", "your_image_url", credentials)


fetch('https://www.nyckel.com/v1/functions/wood-vs-metal-screw/invoke', {
    method: 'POST',
    headers: {
        'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
        'Content-Type': 'application/json',
    },
    body: JSON.stringify(
        {"data": "your_image_url"}
    )
})
.then(response => response.json())
.then(data => console.log(data));


curl -X POST \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
    -d '{"data": "your_image_url"}' \
    https://www.nyckel.com/v1/functions/wood-vs-metal-screw/invoke

How this classifier works

To start, upload your image. Our AI tool will then predict if the screw is made of wood or metal.

This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Metal Screw and Wood Screw.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if the screw is made of wood or metal).

Whether you're just curious or building wood vs metal screw detection into your application, we hope our classifier proves helpful.

Need to identify wood vs metal screw at scale?

Get API or Zapier access to this classifier for free. It's perfect for:

Quality Control in Manufacturing: The True image classification function can be integrated into manufacturing lines to automatically identify and sort screws made of wood versus those made of metal. This helps to ensure that products are assembled with the correct materials, reducing waste and preventing assembly errors.

E-commerce Inventory Management: E-commerce platforms can employ the image classification function to automate the categorization of screws in their inventory systems. This allows for more efficient management of stock, enabling accurate listings and improving customer satisfaction through quicker order fulfillment.

DIY Retail Store Aid: Retail employees can use the classification tool to assist customers in identifying the right screws for their projects. By quickly differentiating between wood and metal screws, staff can provide better service and advice on the best products for specific applications.

Robotics and Automation: Robots equipped with the image classification function can identify and sort screws during assembly processes without the need for complex programming. This will enhance the efficiency of robotic arms in manufacturing settings, ensuring precise handling of materials.

Home Improvement Apps: Mobile applications focused on DIY projects can utilize the function to help users identify the types of screws needed for specific tasks. By simply taking a picture, users receive guidance on whether to use wood or metal screws, improving project success rates.

Construction Site Safety: In construction environments, the classification function can assist workers in ensuring that only the appropriate types of screws are used in various applications. This minimizes the risk of using unsuitable fasteners that could compromise structural integrity and safety.

Research and Development: Engineers and product designers can use the classification tool in R&D to analyze the performance and material properties of different screw types. This data can inform material selection for new products, leading to better design decisions and enhanced product durability.