Imagga's custom training enables customers to specify the categories to which their content should be assigned and use the Imagga auto-categorization API with the custom-trained model. This is especially useful in the cases where a company has tried to classify content manually but has found it overwhelmingly challenging.
Provide training data (list of non-overlapping categories and sample image data for each category). In case you do not have sufficient data, our data engineering team will advise you on the available options.
We build a deep learning classification model trained with your specific categories and data. The existing classified content will be used as a training dataset and after completion, the unclassified content will be automatically processed.
The model is then wrapped in an API so it can be easily plugged in and implemented in your company’s existing systems and workflows.
We have successfully worked with a plethora of companies in various industries, always striving to achieve the best possible results for the image-related business processes of our customers.
In case you don't have the image dataset required for quality machine learning we can help and provide a data collection team.
We have the capacity to handle any size of training dataset thanks to our cutting edge internal infrastructure and computation power.
We can operate the running of the technology in our own super-scalable cloud, as well as assists you in deploying it on your premises or integrate it in your applications on the edge.
There are over 320.000 different species worldwide, meaning that Imagga’s categorizer had to be trained for a huge number of classes. For training, it used over 90 million images, making the scale of this project massive even on an enterprise level.Read The Full Story
Use Imagga Image Recognition API on the Cloud to reduce IT costs and to speed up deployment.
We'll help you deploy Imagga API on your private servers for full compliance with the privacy regulations.
You can also export a snapshot of each model to be used directly on the edge.