Train your retail computer vision models with datasets annotated by a Human in the Loop!
We have a dedicated team for retail annotation which provides superior quality on any given task: from complex SKU labeling, to shopper tracking and e-commerce automation. By using the right tools for automation and QC, we are powering the smart retail industry through our work.
Analyzing the behaviour of customers in a store is essential for making cashier-free shops a reality. The labeling can consist of skeleton keypoints for movement detection, action tracking on video frames, as well as product localization and intention detection.
Monitoring the stocks on shelves can be very useful for supplier logistics and inventory management. Product positioning can be analyzed using bounding boxes, including for the detection of empty spaces, as well as incorrectly placed or stacked products.
While traditional barcodes have to be scanned once at a time, computer vision models can be trained on labeled data to recognise and process barcodes at scale. We can provide labeling for the entire barcode, as well as separate labels and transcription for each number.
Product recognition is a challenging task due to the enormous number of different products and the fact that many of them look similar. By using special tool add-ons for SKUs, our workforce can make sure each product is matched to its SKU, even if it’s one in a million.
The way people shop has never been easier. Smart carts equipped with cameras can be trained on labeled datasets in order to identify, classify, and record the contents even when there is occlusion – which is where bounding box annotation comes in handy.
Logo applications can detect and identify logos in images or video for blurring purposes or for analyzing product placement. The task is not too simple because of the many potential logo permutations, or the appearance of unknown logos which have to be identified using reverse search.
In order to classify products for search engines at scale, automatic tagging is needed for e-commerce applications. By tagging each product with the appropriate tags according to the search filters of the website, our annotators can make sure clients discover exactly what they want.
Fashion trends can be documented and analysed by training AI models to identify and classify various articles of clothing. The labeling taxonomy may get very complex, from garment type classification, to color, fabric, pattern, and other additional attributes for each item.
Wondering who is annotating your data?
When you are hiring a company to help you with your annotation needs, you frequently never meet the workers who are labeling your data. We want to change this and present to you the inspiring stories of our annotators! This article features 4 stories from members of our Retail annotation team who are working out of the Annotation Hub in Syria managed by our partners at the Roia Foundation
Types of annotation
Interested in having a Human in the Loop label your geospatial dataset?
Get in touch with our team at Humans in the Loop and a project manager will help you find the best solution to your computer vision needs!