board_qrcode_segment Instance Segmentation Dataset and Pre-Trained Model by Segment

Images

470 images

Explore Dataset

Here are a few use cases for this project:

Manufacturing Quality Control: The model can be used in factories to automate the process of checking components on a board. By identifying each part (QR code, rectangular or circular objects), it can rapidly determine if all components are present and correctly situated.
Inventory Management: In warehousing or storage facilities, the model could quickly scan boards with QR codes, identify the codes and item shape, hence ensuring efficient tracking and organization of items.
Retail: The model could be used in shops to identify product information. By scanning QR codes on the product boards, it can provide details such as price, product origin, etc., hence improving the shopping experience.
Interactive Gaming: In interactive board games, it can be used to identify different pieces based on their shapes (rectangle, circle) and QR codes to track game progress or rules.
Education: The model could benefit teaching and learning, especially in STEM fields. For example, students can use the computer vision model to identify components on a circuit board or other technical devices.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            board_qrcode_segment_dataset,
                            title = { board_qrcode_segment Dataset },
                            type = { Open Source Dataset },
                            author = { Segment },
                            howpublished = { \url{ https://universe.roboflow.com/segment-blktx/board_qrcode_segment } },
                            url = { https://universe.roboflow.com/segment-blktx/board_qrcode_segment },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { oct },
                            note = { visited on 2024-05-03 },
                            }