# Exploring Model Quantization in GenAI-based Image Inpainting and Detection of Arable Plants

arXiv: https://arxiv.org/abs/2503.02420 · HTML: https://mlsanity.com/paper/2503.02420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://mlsanity.com/paper/2208.07339/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection](https://mlsanity.com/paper/2412.13490/) (2024)
- [Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detection](https://mlsanity.com/paper/2501.17387/) (2025)
- [Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems](https://mlsanity.com/paper/2411.00548/) (2024)
- [Enhancing weed detection performance by means of GenAI-based image augmentation](https://mlsanity.com/paper/2411.18513/) (2024)
- [D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection](https://mlsanity.com/paper/2409.04060/) (2024)

## Built on by (most cited first, 2 of 2)

- [Improving Lightweight Weed Detection via Knowledge Distillation](https://mlsanity.com/paper/2507.12344/) (2025)
- [Diffuse the object, keep its label: curating detector training data from a few unlabeled photographs via VLM-built 3D vegetation scenes](https://mlsanity.com/paper/2608.09691/) (2026)

## Abstract (arXiv)

Deep learning-based weed control systems often suffer from limited training data diversity and constrained on-board computation, impacting their real-world performance. To overcome these challenges, we propose a framework that leverages Stable Diffusion-based inpainting to augment training data progressively in 10% increments -- up to an additional 200%, thus enhancing both the volume and diversity of samples. Our approach is evaluated on two state-of-the-art object detection models, YOLO11(l) and RT-DETR(l), using the mAP50 metric to assess detection performance. We explore quantization strategies (FP16 and INT8) for both the generative inpainting and detection models to strike a balance between inference speed and accuracy. Deployment of the downstream models on the Jetson Orin Nano demonstrates the practical viability of our framework in resource-constrained environments, ultimately improving detection accuracy and computational efficiency in intelligent weed management systems.