Summary of Learn From Foundation Model: Fruit Detection Model Without Manual Annotation, by Yanan Wang and Zhenghao Fei and Ruichen Li and Yibin Ying
Learn from Foundation Model: Fruit Detection Model without Manual Annotation
by Yanan Wang, Zhenghao Fei, Ruichen Li, Yibin Ying
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed framework, SDM-D (Segmentation-Description-Matching-Distilling), leverages pre-trained foundation models to train effective, domain-specific, small models for agriculture without manual annotation. The approach begins with SDM, combining SAM2 for segmentation and OpenCLIP for zero-shot open-vocabulary classification. A novel knowledge distillation mechanism is utilized to distill compact, edge-deployable models from SDM, enhancing both inference speed and perception accuracy. This method demonstrates strong performance across various fruit detection tasks without manual annotation, nearly matching the performance of models trained with abundant labels. Additionally, it outperforms open-set detection methods like Grounding SAM and YOLO-World on tested fruit detection datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps farmers by using AI to detect fruits without needing lots of labeled data. They created a special framework called SDM-D that uses two pre-trained models, SAM2 and OpenCLIP, to segment and classify fruits. The framework is very accurate and can work quickly even on edge devices like smartphones. This means farmers can use it to quickly identify which fruits are ripe and ready to harvest. |
Keywords
» Artificial intelligence » Classification » Grounding » Inference » Knowledge distillation » Sam » Yolo » Zero shot
