Cord

Cord

Automating data annotation for computer vision

3 followers

Cord automates the creation of your training data through our novel micro-model approach. With Cord you can turn your labeling process into an automated assembly line and create training data for your computer vision applications at unparalleled speeds.
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Cord gallery image
Cord gallery image
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Launch Team
Migma AI
Migma AI
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What do you think? …

Eric Landau
Hey Product Hunt! We're the creators of Cord 👋 You probably have a bunch of stuff to do today, so we will be super brief. 🙋Problem The biggest bottleneck in computer vision AI right now is not the compute power, the number of parameters in the models, or the number of intelligent data scientists, it’s the ability to create usable labeled training datasets to feed the models. This is the blocking element holding us back from building applications to solve some of the hardest problems we are facing today. 💡 Solution That’s why we built Cord, a platform that can automate the data annotation process for computer vision use cases. We use a toolbox we call “micro models” to break up the annotation process into smaller parts and let AI do most of the work. Our platform has helped build datasets for computer vision applications that include the diagnosis of cancer, detection of people in danger in construction sites, and management of small business inventories. 🔥How does it work? 1. Define your label structure or “ontology.” This is the set of “questions you are asking the data”. Cord allows you to create arbitrarily complex label structures and dynamically adapt them throughout the course of your projects. 2. Do a handful of manual annotations. Sometimes even just 4 hand labels are enough to start “teaching” the system what you want to annotate. 3. Build a micro model. It takes less than 5 minutes to get your first model trained and running. 4. Run the model, review the predictions, and retrain. You are now in an active learning loop! 5. Repeat as necessary. Build as many micro models as you need to cover your labeling. 6. Once the labels are set, use Cord’s SDK to create a data pipeline. You now have a working dataset ready for your model! The platform’s visualization features will also help you detect biases and imbalances in your dataset through the process. To get started come talk to us! We are offering a special deal where we are giving out 5 hours of free human annotation to the first 5 people that sign up with us through Product Hunt. Book a demo on our website quoting ‘PRODUCT HUNT’ to qualify. We look forward to hearing your feedback and comments! 🚀
Oskar Barner Bernhardtsen
Looks really cool Eric and Ulrik. Huge devellopment since I saw the first demo 🚀🚀
Dmitry Shklovsky
@eric_landau awesome, congrats on the launch!
Iliya Valchanov
@eric_landau This has been an issue for ages, even for educational purposes. You can't really learn how to do ML, because there are literally 5 datasets.
Eric Landau
Thank you @iliya_valchanov, glad you agree! 3veta also seems super cool, let me know if you are doing any computer vision!
thomasmarge
@eric_landau Congrats!
Joseph Mela
I like Cord.
Eric Landau
Me too
Kevin Simons
Seriously great stuff! Would've loved this in my previous gig where I was managing a team of computer visions experts who were always starving for properly annotated data sets.
Ulrik Hansen
@ksimons thanks, appreciate the support!
Sigurd Seteklev
Cord is great! I can't recommend Ulrik and Eric and their fantastic product enough. Congrats on the launch!
Ulrik Hansen
@kitemaker thanks Sigurd! Really appreciate it :)
Olga Ponomarenko
Love it! Do you know what % of automatically created labels requires manual revision?
Eric Landau
Thank you @olga_ponomarenko3! Depends on the use case. For autonomous vehicles for instance less than 1% require revision, but for more complicated medical use cases, can be much higher.
Juliet Bailin
This is awesome, I know so many healthcare orgs that have been trying to clear this hurdle in their own AI application building efforts. Have you worked with any healthcare use cases yet?
Ulrik Hansen
@julietroseb thanks Juliet! We have worked with orgs like Stanford Medicine and King's College London. We actually published a paper in collaboration with KCL you can see here: https://www.thieme-connect.com/p...
Juliet Bailin
@ulsha Ahh super interesting, I'll take a read and pass this along, thanks!
Rachel Dowling
@julietroseb @ulsha wow nice stuff!
Sydney Cohen
Been following the team, and I trust they are doing something people want :)
Eric Landau
I hope so, it's on my YC mug!
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