Deep Collector
The fastest way to collect your training datasets is completely automated and with all the tools you need. Build better data and drastically reduce data acquisition costs.
Trusted by professionals
The product
Completely skip the manual effort of building training data. Deep Collector provides best-in-class AI algorithms to automatically collect, prepare and label datasets for your face recognition and speaker identification through a perfectly integrable, scalable, and secure solution.
Benefits
Deep Collector reduces the manual effort of your data acquisition and preparation time by up to 98%.
Deep Collector is packed with automation tools to validate, prepare, and store your training data. It’s the easiest way to start building your own AI.
Deep Collector stores the collected training data in a way that you can easily export it and use it in any other workflow without vendor lock-in.
Use cases
How can I build up a dataset for my speaker recognition?
Read MoreHow can I automatically build training datasets from my media footage?
Read MoreDeep Collector
Using images of the faces and text information (e.g. from belly bands), Face Dataset Creation automatically creates entire datasets, without any manual labor.
Integration & deployment
Deep Collector integration lets you take advantage of AI-powered computer vision, machine learning, and low-code automation.
DeepVA-powered solutions
Success stories
With the help of DeepVA, the user can intuitively and easily create and manage training data directly in the media asset management system (MAM).
Read MoreCreating and managing training data for face recognition using AI requires a lot of time and resources.
Read MorePricing
Every company has special requirements. No matter if you are a startup, a corporate, or an enterprise. Whether subscription, license, or pay-as-you-use. We will help you find the most suitable pricing model!
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A product of «The Chainless».
Designed and engineered in Germany.
The Chainless GmbH
Wannerstraße 19
79106 Freiburg
Germany
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
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