Closed srishilesh closed 2 years ago
Great topic 🚀 , make sure it matches the following:
Please reference any relevant EngEd articles in yours and build a unique project - Approved. @srishilesh
I'm going to close this topic due to inactivity. It can be reopened.
Hey @lalith1403 ,
On thinking through the topics that I could cover under this, I felt that this article would be informative/understandable, only the reader has some basic idea on what PASCAL VOC and COCO is - which I think is already an extensive topic.
Putting all (PASCAL VOC, COCO and Conversion implementation) together in a single article would make it too lengthy.
So, can I have this issue as "Part 2"? while the "Part 1" discusses about the concepts of why and how annotation is done, what bounding boxes are, history behind PASCAL VOC and COCO, how are they different from each other, what types of problems does each type support, and so on.
What do you think? Shall I create a new topic suggestion for the "Part 1"?
Thanks.
I think covering it in a single article would be better for the readers. That way, this can be a single article for everything related to object detection data formats.
Topic Suggestion
Machine Learning
Writing sample(s):
https://www.section.io/engineering-education/authors/srishilesh-p-s/
Proposal Submission
Proposed title of article
Converting Datasets in PASCAL VOC to COCO format for Object Detection
Proposed article introduction
Object detection refers to the capability of computer and software systems to locate objects in an image/scene and identify each object. Detection of objects are either represented using XML files or JSON files. Each representation has it's own pros and cons. In this article, we will be understanding these representations in-detail, and how to convert an XML file (PASCAL VOC) into a JSON file (COCO).
Key takeaways
This article will be an introduction to:
Article quality
References
Conclusion
The reader will understand how to: