Computer Vision Specialist for Retail Shelf Detection
Skills Required
Description
Retail shelf detection requires building a reliable computer vision pipeline that can accurately identify products, stock levels, and placement. The specialist will use OpenCV and PyTorch to develop detection models that can process images efficiently while maintaining high accuracy. Large volumes of data will need to be handled, so optimization and scalability will be key considerations.
Data labeling will be an important step in training robust models. Each product must be tagged correctly, and variations such as size, packaging, and orientation should be accounted for. Clean and consistent labeling will directly impact the performance of the detection system, so attention to detail is critical during dataset preparation.
Model optimization will ensure that the detection system runs smoothly in real-world retail environments. This includes reducing inference times, compressing models where possible, and ensuring accuracy is not lost when deployed on lower-powered devices such as store scanners or cameras. Continuous monitoring and updates will also be part of the process.
Quick iterations will be required to test different architectures and fine-tune hyperparameters.
Short turnaround times will help validate models against real-world scenarios quickly.
The specialist should also provide documentation of the pipeline for future improvements.
Collaboration with retail teams will help in gathering data that reflects actual ...