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

• URL: http://arxiv.org/abs/2503.02420v1
• Date: Tue, 04 Mar 2025 09:05:01 GMT
• Title: Exploring Model Quantization in GenAI-based Image Inpainting and Detection of Arable Plants
• Authors: Sourav Modak, Ahmet Oğuz Saltık, Anthony Stein,
• Abstract summary: We propose a framework that leverages Stable Diffusion-based inpainting to augment training data progressively in 10% increments -- up to an additional 200%.<n>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.<n> Deployment of the downstream models on the Jetson Orin Nano demonstrates the practical viability of our framework in resource-constrained environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Enhancing weed detection performance by means of GenAI-based image augmentation [0.0]
  This paper investigates a generative AI-based augmentation technique that uses the Stable Diffusion model to produce diverse synthetic images for weed detection models.<n>Results show substantial improvements in mean Average Precision for YOLO models trained with generative AI-augmented datasets.
  arXiv  Detail & Related papers  (2024-11-27T17:00:34Z)
• Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems [0.0]
  This study presents a new approach for generating synthetic images to improve deep learning-based object detection models for intelligent weed control. Our GenAI-based image generation pipeline integrates the Segment Anything Model (SAM) for zero-shot domain adaptation with a text-to-image Stable Diffusion Model. We evaluate these synthetic datasets using lightweight YOLO models, measuring data efficiency with mAP50 and mAP50-95 scores.
  arXiv  Detail & Related papers  (2024-11-01T12:58:27Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Developing Gridded Emission Inventory from High-Resolution Satellite Object Detection for Improved Air Quality Forecasts [1.4238093681454425]
  This study presents an innovative approach to creating a dynamic, AI based emission inventory system for use with the Weather Research and Forecasting model coupled with Chemistry (WRF Chem) The system offers unprecedented temporal and spatial resolution in emission estimates, facilitating more accurate short term air quality forecasts and deeper insights into urban emission dynamics. Future work will focus on expanding the system's capabilities to non vehicular sources and further improving detection accuracy in challenging environmental conditions.
  arXiv  Detail & Related papers  (2024-10-14T01:32:45Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation [42.48819460873482]
  Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity. We show that they also excel in estimating optical flow and monocular depth, surprisingly, without task-specific architectures and loss functions.
  arXiv  Detail & Related papers  (2023-06-02T21:26:20Z)
• On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications [0.0]
  Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task.
  arXiv  Detail & Related papers  (2021-11-11T22:24:15Z)
• GOAT: GPU Outsourcing of Deep Learning Training With Asynchronous Probabilistic Integrity Verification Inside Trusted Execution Environment [0.0]
  Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a range of applications ranging from self-driving cars to COVID-19 treatment discovery. To support the computational power necessary to learn a DNN, cloud environments with dedicated hardware support have emerged as critical infrastructure. Various approaches have been developed to address these challenges, building on trusted execution environments (TEE)
  arXiv  Detail & Related papers  (2020-10-17T20:09:05Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.