Bounding boxes for Fashion retail
We help you build annotated and labeled images with bounding boxes to build computer vision models for fashion retail applications at scale.
Bounding boxes for Fashion retail
Annotations
Bounding boxes for Fashion retail
Annotations
Classes
- Shades
- Bag
- Ear accessory
- Neck accessory
- Top
- Bottom
- Arm accessory
Similar use cases
Here’s what our customers say about us
“We were very impressed with Playment’s ability to grasp complex requirements and quickly build custom tools to support it. Our dedicated engagement manager was very helpful in sharing his domain expertise to formulate the right solution for our team.”
“Playment's fully managed approach has been critical in factoring the variability and scaling up our annotation requirements. Thanks to their tools and skilled workforce, we are extremely satisfied by the quality and turnaround time they have provided. I would highly recommend Playment.”
“Playment's quality annotations helped us focus our efforts towards building perception system for our robots. Their flexibility to seamlessly incorporate our feedback and evolving requirements have made them a trusted partner.”
“Quality annotations by Playment have helped us achieve higher accuracy of our models in a very short time. Flexible solutions, QA process, and dedicated project manager helped us have peace of mind. The team was able to experience a real off-loading of annotation needs.”
We make the labeling process simple
Project Setup
Our data labeling platform enables a project manager to train real people to annotate your data and to make sure your models are being trained on high-quality data.
Raw Dataset
Send us dataset and task guidelines. We support API, .CSV, FTP, cloud etc. to source data and setup tasks.
Project Setup
Our data labeling platform enables a project manager to train real people to annotate your data and to make sure your models are being trained on high-quality data.
Human in the Loop
The semi-automatic labeling process involves a combination of deep learning models, heuristics and manual human edits to create high quality annotation at scale.
Rigorous QC
We execute multi-level automated and manual quality checks over each and every annotation. Machine checks to eliminate random or systematic errors combined with human consensus models ensures highly accurate output.
Export Results
Collect all the ground truth data you need to train your model.