{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-pcb-to-coco-convertor","title":"Deep PCB To COCO Convertor","arxiv_id":null,"date":"2022-05-01","proceeding":"International Journal for Research in Engineering Application & Management (IJREAM) 2022 5","authors":["Allena Venkata Sai Abhishek","Genti Sreeja","Dr K S Sudeep"],"abstract":"Millions of datasets and many models use the input datasets in COCO format. In this paper, we are converting the Deep PCB dataset to COCO format. The Deep PCB is a manufacturing defect data set. It has 1500 image pairs. Each has a template image & a test image. The template image has no defects & corresponding test image that has some defects with the annotations in a text file. The MS COCO (Microsoft Common Objects in Context) dataset is large-scale object detection, segmentation, key-point detection, and captioning. It is widely used for various models. We are trying to convert the deep PCB Manufacturing defect into COCO Format.","url_abs":"http://ijream.org/papers/IJREAMV08I0286072.pdf","url_pdf":"http://ijream.org/papers/IJREAMV08I0286072.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-pcb-to-coco-convertor","repo_url":"https://github.com/avs-abhishek123/DeepPCB-to-COCO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"deep-pcb-to-coco-convertor","repo_url":"https://github.com/tangsanli5201/DeepPCB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"detecting-image-manipulation","task_name":"Detecting Image Manipulation"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-cropping","task_name":"Image Cropping"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"image-manipulation-detection","task_name":"Image Manipulation Detection"},{"task_slug":"image-matting","task_name":"Image Matting"},{"task_slug":"image-morphing","task_name":"Image Morphing"},{"task_slug":"image-stitching","task_name":"Image Stitching"},{"task_slug":"image-variation","task_name":"Image-Variation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"roi-based-image-generation","task_name":"ROI-based image generation"},{"task_slug":"image-smoothing","task_name":"image smoothing"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"deep-pcb","name":"Deep PCB","full_name":"Deep Printed Circuit Board"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}