APIs to access a trained model customised for your needs.
- Object Detection
- Image Classification
- Image Tagging
- Image Segmentation
How it works
Nanonets makes machine learning simple
With Nanonets the process of building Deep Learning models is as simple as uploading your data. No parameter tuning. No need to bother about finding the right infrastructure to host your models. Just show us a few samples that the model can learn from and wait for the magic. We build train and host the model so you just need to add 2 lines of code to your codebase and you're good to go.
Ready to use Models
Explore all our ready-to-use image recognition models to suit your specific needs.
3 Ways to Use Nanonets
Nanonets makes it super easy to use our models across devices and platforms
Download our model as a docker binary and run it on your own infrastructure. Our docker models come in two flavors, GPU and CPU and can run on almost any platform including embedded devices.
Download our Mobile SDK for iOS or Android and integrate with your app. Run the model on every users device and make inference instantly. Our models for mobile are ultra optimised for performance.
Human-powered high quality results for AI applications.
Solar Plant Inspection
Nanonet's API allows for automated inspection of Solar Panels. We identify any defect in the panel reducing performance with severity, location and size. This can help companies easily maintain the performance of their solar panels.
Fault Detection In Wind Turbines
Nanonet's can easily detect faults in Wind Turbines. Using our automated defect analysis, customers can cut down on cost of maintenance and inspection as well as greatly reducing turn around time.
Nanonets APIs to monitor and filter inappropriate images from your social website, app or platform. Review millions of images each day using Nanonet's accurate models on custom categories. Cut down manual review costs.
Parking Lot Study
Nanonets APIs to count objects of interest in an image. Get accurate count of cars, animals, or other custom object within an image. Save time and cut down costs of manual image analysis.