The paper presents a GS1-based, studio-photographed food product image dataset of 1,034 images and 30 object detection labels, with baseline YOLOv5 and ResNet50 results.
Unitail: Detecting, Reading, and Matching in Retail Scene
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various state-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness.
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction
The paper presents a GS1-based, studio-photographed food product image dataset of 1,034 images and 30 object detection labels, with baseline YOLOv5 and ResNet50 results.