A Car Model Identification System for Streamlining the Automobile Sales
Process
- URL: http://arxiv.org/abs/2310.13198v2
- Date: Mon, 23 Oct 2023 08:14:18 GMT
- Title: A Car Model Identification System for Streamlining the Automobile Sales
Process
- Authors: Said Togru, Marco Moldovan
- Abstract summary: This project presents an automated solution for the efficient identification of car models and makes from images.
We achieved a notable accuracy of 81.97% employing the EfficientNet (V2 b2) architecture.
The trained model offers the potential for automating information extraction, promising enhanced user experiences across car-selling websites.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This project presents an automated solution for the efficient identification
of car models and makes from images, aimed at streamlining the vehicle listing
process on online car-selling platforms. Through a thorough exploration
encompassing various efficient network architectures including Convolutional
Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models, we
achieved a notable accuracy of 81.97% employing the EfficientNet (V2 b2)
architecture. To refine performance, a combination of strategies, including
data augmentation, fine-tuning pretrained models, and extensive hyperparameter
tuning, were applied. The trained model offers the potential for automating
information extraction, promising enhanced user experiences across car-selling
websites.
Related papers
- Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception [12.416683044819955]
We study the application of emerging chiplet-based Neural Processing Units to accelerate vehicular AI perception workloads in constrained automotive settings.
Our approach realizes 82% and 2.8x increase in throughput and processing engines utilization compared to monolithic accelerator designs.
arXiv Detail & Related papers (2024-11-24T22:59:11Z) - Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments [16.587883982785]
The continual evolution of autonomous driving technology requires car-following models that can adapt to diverse and dynamic traffic environments.
Traditional learning-based models often suffer from performance degradation when encountering unseen traffic patterns due to a lack of continual learning capabilities.
This paper proposes a novel car-following model based on continual learning that addresses this limitation.
arXiv Detail & Related papers (2024-07-17T06:32:52Z) - AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios.
We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
arXiv Detail & Related papers (2024-03-26T04:27:56Z) - Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs.
We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
arXiv Detail & Related papers (2024-03-21T02:33:37Z) - Automated Progressive Learning for Efficient Training of Vision
Transformers [125.22744987949227]
Vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs.
Progressive learning, a training scheme where the model capacity grows progressively during training, has started showing its ability in efficient training.
In this paper, we take a practical step towards efficient training of ViTs by customizing and automating progressive learning.
arXiv Detail & Related papers (2022-03-28T05:37:08Z) - Bayesian Optimization and Deep Learning forsteering wheel angle
prediction [58.720142291102135]
This work aims to obtain an accurate model for the prediction of the steering angle in an automated driving system.
BO was able to identify, within a limited number of trials, a model -- namely BOST-LSTM -- which resulted, the most accurate when compared to classical end-to-end driving models.
arXiv Detail & Related papers (2021-10-22T15:25:14Z) - Utilizing Active Machine Learning for Quality Assurance: A Case Study of
Virtual Car Renderings in the Automotive Industry [0.0]
We propose an active machine learning-based quality assurance system that requires significantly fewer labeled instances to identify defective virtual car renderings.
By employing our system at a German automotive manufacturer, start-up difficulties can be overcome, the inspection process efficiency can be increased, and thus economic advantages can be realized.
arXiv Detail & Related papers (2021-10-18T05:43:06Z) - Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data
Augmentation [77.60050239225086]
We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images.
Our approach is fully automatic without any human interaction.
We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
arXiv Detail & Related papers (2020-12-15T03:03:38Z) - AutoADR: Automatic Model Design for Ad Relevance [26.890941853575253]
Large-scale pre-trained models are memory and computation intensive.
How to design an effective yet efficient model architecture is another challenging problem in online Ad Relevance.
We propose AutoADR -- a novel end-to-end framework to address this challenge.
arXiv Detail & Related papers (2020-10-14T13:24:43Z) - AutoFIS: Automatic Feature Interaction Selection in Factorization Models
for Click-Through Rate Prediction [75.16836697734995]
We propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS)
AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence.
AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service.
arXiv Detail & Related papers (2020-03-25T06:53:54Z)
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.