Identify if there's a bike
using AI
Below is a free classifier to identify if there's a bike. Just upload your image, and our AI will predict if there's a bike - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("if-there's-a-bike", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/if-there's-a-bike/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/if-there's-a-bike/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict if there's a bike.
This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Bike Present and No Bike.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if there's a bike).
Whether you're just curious or building if there's a bike detection into your application, we hope our classifier proves helpful.
Need to identify if there's a bike at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Urban Bicycle Rental Monitoring: This function can be utilized by bike-sharing companies to monitor bike availability at different stations. By identifying whether bikes are present or missing, the system can optimize maintenance schedules and restocking strategies to enhance user satisfaction.
- Smart City Traffic Management: City authorities can deploy this function to analyze bike traffic patterns in real-time. By incorporating bike identification into traffic management systems, cities can improve infrastructure planning and promote cycling as a sustainable transportation option.
- Insurance Risk Assessment: Insurance companies can use this identifier to determine the prevalence of bicycles in various neighborhoods when underwriting policies. This data allows for better risk assessments and tailored premiums based on biking activity levels.
- E-commerce Delivery Optimization: Retailers can integrate this classification into their logistics systems to identify areas with high bicycle activity. By doing so, they can develop eco-friendly delivery options, such as bike couriers, for faster and more efficient deliveries.
- Public Health Studies: Researchers can apply this function in public health studies to examine the relationship between bike presence and community health outcomes. By collecting data on bike usage, they can promote cycling as a means to improve public health initiatives and reduce obesity rates.
- Safety Analytics for Cyclists: Organizations focused on cyclist safety can use this technology to gather data on bike presence in accident-prone areas. This information can be crucial in proposing new bike lanes or safety measures, ultimately leading to safer riding conditions.
- Event Planning and Management: Event organizers can use this function to monitor bike congestion in areas where cycling events take place. By understanding bike flow and inventory, they can enhance the event experience and improve logistical planning for both participants and spectators.