Our client is a computer vision startup in the retail space that is backed by some of the prestigious names in the start up world. They have some of the biggest retail chains on the planet in their clientele. With the retail industry set to witness a steady growth and companies investing heavily in autonomous store technology, our client is expected to be a leader in this space.
Our client uses camera footage to understand consumer behaviour and enable a complete unsupervised shopping experience. The performance of their models didn't improve beyond a certain level because of the large number of edge cases and uncertainties in the customer behvaiour.
When our client approaced us, they were expanding into a new geography and the shop layout and the clothing style of the customers were very dirrerent to what they had trained their model on and this resulted in a drop in the performance of their computer vision models.
We Have experience dealing with edge case scenarios before and we identified 5 scenarios which affected the performance of the model and started annotating those cases first to be fed into their pipeline for training. We started with the most difficult cases and proceeded to the not so extreme cases and this approach led to a quicker improvement in the performance of the model.
We totally annotated about 100,000+ cases with varying difficulties in a week's time that ended up improving our client's CV model substantially.
Some metrics of the project
With our high quality annotation services, our client was able to improve their model and the predcitions. They were able to successfuly implement their project in the new location and it certainly improved the overall performance of their models with edge case scenarios.
Here's what the founder of the startup had to say about our services,
"DataClap helped us improve our predcition models in a big way. Right from onboarding to support we were extremely happy with the way things went. This is something that we've not experienced with big data annotation platforms"
Our data annotation efforts helped our client implement a smart city project with challenging requirements.
We helped a sports analytics leader in developing AI models for sports analytics.
Important things to look for when choosing a data annotation partner
Case study on how we used Human in the Loop for data curation
Case study on how we used Human in the Loop for a document processing use case
Case study on how we provided image annotation services for an autonomous robotics startup
Contact us with your requirements and we will set up a team to wok on your free pilot project. No commitments on your side.