Identify arcfox models using AI

Below is a free classifier to identify arcfox models. Just upload your image, and our AI will predict if the model is an Arcfox model or not - in just seconds.

API Access


import nyckel

credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("arcfox-models", "your_image_url", credentials)


fetch('https://www.nyckel.com/v1/functions/arcfox-models/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/arcfox-models/invoke

How this classifier works

To start, upload your image. Our AI tool will then predict if the model is an Arcfox model or not.

This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Alpha S and E Hs9.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if the model is an Arcfox model or not).

Whether you're just curious or building arcfox models detection into your application, we hope our classifier proves helpful.

Need to identify arcfox models at scale?

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

Automotive Inventory Management: The True image classification function can be utilized by dealerships to efficiently categorize and manage their inventory of Arcfox models. By automatically identifying vehicle models from images, the system streamlines the inventory process, reduces human error, and enhances data accuracy.

Automated Quality Control: Manufacturers can implement this image classification function in their production lines to perform automated quality checks on Arcfox vehicles. By identifying models in real-time, manufacturers can quickly assess if vehicles meet design specifications and quality standards, ensuring that only compliant products reach the market.

Personalized Marketing Campaigns: Marketing teams can leverage the classification function to analyze consumer preferences by recognizing which Arcfox models are most popular among their audience. This allows for highly targeted advertising efforts and personalized marketing strategies that resonate with specific customer segments.

Insurance Assessment: Insurance companies can use the True image classification function to expedite claims processes by quickly identifying Arcfox models involved in accidents. This speeds up the assessment, helping insurers to determine coverage and process claims more efficiently.

Enhanced Customer Support: Customer service teams can utilize the image classification capability to quickly identify Arcfox models when customers have inquiries or issues. By knowing the exact model, support representatives can provide tailored assistance and efficiently resolve customer concerns.

Fleet Management Optimization: Companies with fleets of Arcfox vehicles can implement this classification function to better track and manage their assets. By automating the identification of different models within the fleet, businesses can optimize maintenance schedules, enhance driver assignment, and reduce operational costs.

Data-Driven Product Development: Automotive designers and engineers can use insights from the image classification function to understand consumer trends and preferences related to Arcfox models. This data-driven approach enables more informed decision-making in product development cycles, ultimately leading to vehicles that align with market demand.