Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline

• URL: http://arxiv.org/abs/2406.01071v1
• Date: Mon, 3 Jun 2024 07:44:08 GMT
• Title: Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
• Authors: Jan Lippemeier, Stefanie Hittmeyer, Oliver Niehörster, Markus Lange-Hegermann,
• Abstract summary: We propose an automatic pipeline for generating synthetic image datasets using Stable Diffusion. We leverage YOLOv8 for automatic bounding box detection and quality assessment of synthesized images.
• Score: 3.524869467682149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in machine learning, particularly in deep learning and object detection, have significantly improved performance in various tasks, including image classification and synthesis. However, challenges persist, particularly in acquiring labeled data that accurately represents specific use cases. In this work, we propose an automatic pipeline for generating synthetic image datasets using Stable Diffusion, an image synthesis model capable of producing highly realistic images. We leverage YOLOv8 for automatic bounding box detection and quality assessment of synthesized images. Our contributions include demonstrating the feasibility of training image classifiers solely on synthetic data, automating the image generation pipeline, and describing the computational requirements for our approach. We evaluate the usability of different modes of Stable Diffusion and achieve a classification accuracy of 75%.

Related papers

• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• Scaling Laws of Synthetic Images for Model Training ... for Now [54.43596959598466]
  We study the scaling laws of synthetic images generated by state of the art text-to-image models. We observe that synthetic images demonstrate a scaling trend similar to, but slightly less effective than, real images in CLIP training.
  arXiv  Detail & Related papers  (2023-12-07T18:59:59Z)
• WinSyn: A High Resolution Testbed for Synthetic Data [41.11481327112564]
  We present WinSyn, a unique dataset and testbed for creating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution photographs of windows, selected from locations around the world, with 89,318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images.
  arXiv  Detail & Related papers  (2023-10-09T20:18:10Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images [7.868449549351487]
  This article proposes to enhance our ability to recognise AI-generated images through computer vision. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake.
  arXiv  Detail & Related papers  (2023-03-24T16:33:06Z)
• Synthetic Data for Object Classification in Industrial Applications [53.180678723280145]
  In object classification, capturing a large number of images per object and in different conditions is not always possible. This work explores the creation of artificial images using a game engine to cope with limited data in the training dataset.
  arXiv  Detail & Related papers  (2022-12-09T11:43:04Z)
• Is synthetic data from generative models ready for image recognition? [69.42645602062024]
  We study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks. We showcase the powerfulness and shortcomings of synthetic data from existing generative models, and propose strategies for better applying synthetic data for recognition tasks.
  arXiv  Detail & Related papers  (2022-10-14T06:54:24Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• Synthetic Data for Model Selection [2.4499092754102874]
  We show that synthetic data can be beneficial for model selection. We introduce a novel method to calibrate the synthetic error estimation to fit that of the real domain.
  arXiv  Detail & Related papers  (2021-05-03T09:52:03Z)
• PennSyn2Real: Training Object Recognition Models without Human Labeling [12.923677573437699]
  We propose PennSyn2Real - a synthetic dataset consisting of more than 100,000 4K images of more than 20 types of micro aerial vehicles (MAVs) The dataset can be used to generate arbitrary numbers of training images for high-level computer vision tasks such as MAV detection and classification. We show that synthetic data generated using this framework can be directly used to train CNN models for common object recognition tasks such as detection and segmentation.
  arXiv  Detail & Related papers  (2020-09-22T02:53:40Z)

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.