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In this project, I will explore the application of machine learning to vehicle detection using a dataset sourced from Kaggle. The objective is to develop a robust vehicle detection model by training and evaluating various machine learning algorithms.The key steps involved in the project include dataset exploration, data preprocessing, model selection, training and optimization, performance evaluation, and real-time vehicle detection.
Github
Apoorvay75
Email
apoorvay75@gmail.com
Label
GSSOC'23
Define You
[1] GSSOC Participant
[2] Contributor
Project Name
Vehicle Recognition and detection by image dataset
Description
Develop a robust vehicle detection model using machine learning algorithms.
Evaluate and compare different machine learning techniques for vehicle detection.
Analyze the Kaggle dataset to understand its structure, features, and labeling methodology.
The expected outcomes are : Real-time vehicle detection demonstration.
Can be used in autonomous vehicles
Timeline
Starting Date - Date it is assigned
Ending Date -31th July
Project Request
Define You
Project Name
Vehicle Recognition and detection by image dataset
Description
Develop a robust vehicle detection model using machine learning algorithms. Evaluate and compare different machine learning techniques for vehicle detection. Analyze the Kaggle dataset to understand its structure, features, and labeling methodology.
The expected outcomes are : Real-time vehicle detection demonstration. Can be used in autonomous vehicles
Timeline
Starting Date - Date it is assigned Ending Date -31th July