A pretrained hardwood vs softwood classifier that sorts an image into one of 2 categories. Use the hardwood vs softwood API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Hardwood",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 hardwood vs softwood categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
Companies that source timber can utilize the hardwood vs softwood identifier to ensure they are making informed purchasing decisions that support sustainable practices. By classifying wood types accurately, businesses can reduce their environmental impact and enhance their brand reputation by promoting responsible sourcing.
Wood manufacturers can implement this classification function as a part of their quality control processes. By ensuring that the correct wood type is used in products, they can maintain consistent quality and reduce defects caused by using inappropriate materials.
Furniture designers can leverage the identifier to select suitable wood types for their designs. Understanding the differences between hardwood and softwood allows for better material selection that aligns with aesthetic goals, durability requirements, and cost constraints.
Construction firms can utilize this function during the procurement phase to differentiate between various wood materials. Knowing whether timber is hardwood or softwood can help in determining load-bearing capacities, longevity, and suitability for various structural applications.
Educational institutions and training programs can employ this technology to teach students about the characteristics of different wood types. By incorporating practical identification tools into their curriculum, students gain hands-on experience that reinforces their understanding of wood science and forestry.
Companies involved in recycling wood can adopt this identifier to streamline their sorting processes. By accurately classifying incoming wood materials as hardwood or softwood, they can optimize recycling pathways or repurposing strategies for maximum material recovery.
Retailers in the wood and timber industry can analyze market trends through data derived from the classification function. Understanding the demand for hardwood versus softwood can inform inventory decisions and targeted marketing campaigns to better meet customer preferences and increase sales.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This hardwood vs softwood classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.