Summary of Zero-shot Automatic Annotation and Instance Segmentation Using Llm-generated Datasets: Eliminating Field Imaging and Manual Annotation For Deep Learning Model Development, by Ranjan Sapkota et al.
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development
by Ranjan Sapkota, Achyut Paudel, Manoj Karkee
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study presents a novel method for deep learning-based instance segmentation of apples in commercial orchards that eliminates the need for labor-intensive field data collection and manual annotation. The approach utilizes a Large Language Model (LLM) to synthetically generate orchard images, which are then automatically annotated using the Segment Anything Model (SAM) integrated with a YOLO11 base model. This method significantly reduces reliance on physical sensors and manual data processing, presenting a major advancement in “Agricultural AI”. The synthetic dataset was used to train the YOLO11 model for Apple instance segmentation, which was then validated on real orchard images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use computers to identify apples in fields without needing to collect lots of data and label every picture. They created a way to generate fake pictures of apple trees using a computer program, and then used another program to mark the apples in those pictures. This lets them train their computer model without needing all that extra work. The results show that this method is pretty good at identifying apples, with a high degree of accuracy. |
Keywords
» Artificial intelligence » Deep learning » Instance segmentation » Large language model » Sam
