NanoNets is a Deep Learning platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business.
You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks.
It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs.
NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy.
NanoNets benefits you in four important ways:
- It reduces the amount of data needed to build a Deep Learning Model
- NanoNets handles the infrastructure for hosting and training the model, and for the run time
- It reduces the cost of running deep learning models by sharing infrastructure across models
- It is possible for anyone to build a deep learning model
NanoNets works exclusively with Deep Neural Networks and its backend stack consists of Python, Caffe, Keras, Theano, Docker, TensorFlow, Torch, Elasticsearch, Golang, Docker and Cassandra, which we host on AWS.
Some of NanoNets’ Use Cases
NanoNets’ platform has applications in different sectors such as agriculture, social media, e-commerce, health, and more. Its application ranges from detecting harvest yield from aerial views to identifying explicit content on social media. There are several use cases for NanoNets’ platform across sectors and industries.
Identifying NSFW Content
You can create a new model or use NanoNets’s existing pre-trained model for detecting NSFW or explicit content. NanoNets can filter explicit content at high speed. It overcomes the problem of human subjectivity and inconsistency and avoids the issue of mental health problems that arise from manually moderating NSFW content.
NanoNets’s NSFW filtering model can be customized according to the requirements of different regions and industries making NanoNets is extremely useful due to its customizability. It can moderate 200,000 images per hour and is faster, more effective and more affordable than human moderation.
Offering users recommendations for related products is very important in e-commerce websites selling furniture, electronics, apparels, and other products. Recommended products are typically generated from attributes that are manually created which are prone to errors and incomplete information.
NanoNets’ APIs help by extracting product details and attributes from an image rather than generating products from attributes. This means that the metadata is more accurate and complete and makes it possible to generate better recommendations for users and to increase sales.
Fault Detection in Wind Turbines
NanoNets API can assist in detecting faults and problems in wind turbines by analyzing images captured by drones. Manually detecting problems and issues from hundreds of images is a tedious process that is prone to errors.
You can use NanoNets’ models to inspect machinery and products and categorize different faults with speed and accuracy. In a real example, an entire windmill farm can be covered in a few hours. NanoNets custom inspection models can analyze up to 100 turbines an hour, the cost of which is considerably lesser than expert human labor.
Tagging Stock Photography
Using deep learning to classify stock photography is a useful application of the NanoNets’ API. You can train models to classify images based on different features. This makes it a quick and simple task to categorize images as nature shots, human portraits, landscapes or objects.
This is useful for photographers who work with hundreds and thousands of images as well as stock photography sites that store and sell millions of pictures. NanoNets’ API can derive attributes from images and categorize them with greater accuracy than manual efforts.
Nanonets provides the following solutions
Object detection has applications in autonomous vehicles, analyzing drone images, in augmented reality, etc. NanoNets makes it possible to train models to detect the objects of your choice with a dataset of just 100 images. It also has extensively trained pre-trained models which can be customized. Work with NanoNets experts who will annotate for you, train the best model, the entirety of which runs on the cloud and removes all hardware related expenses.
NanoNets allows you to use its deep learning algorithm to recognize and tag features in images to assign them to a tag, class or category. This creates textual descriptions from images and can be done in a matter of hours compared to months of human labor needed to carry out this task. NanoNets gives you easy integration methods and requires only a small dataset to get accurate results.
Image segmentation has valuable applications in autonomous vehicles, virtual reality, and human-computer interactions. NanoNets enables semantic segmentation and achieves fine inference of objects so that each pixel in an image is labeled with the class of the object that encloses it. NanoNets creates accurate pixel-based masks and can be used to detect a number of objects with minimal data.
NanoNets image classification model extracts rich attributes and tags from images to enrich your database. You can use its 15,000 tags or define your own custom tag. Image classification has applications in e-commerce and is used to recommend products. It can be combined with object detection and provide multi-label classification such as in the case of furniture where an image can be classified based on attributes like material, fabric, color, etc.
NanoNets’ Image Similarity API compares two images and returns a score between 0 to 1. Identical images return a score of 0 while dissimilar images score nearer 1. The Image Similarity API quantifies the degree of similarity between images and this can be used for several applications such as removing duplicates in datasets or grouping similar items.
NanoNets simplifies the creation of deep learning models for businesses and individuals. Create, train, test and integrate Nanonets models with your projects or work with NanoNets to create customized deep learning models for your business applications.