Mohit Bhosale,,
S.K. Shinde,,
Nikita Bhosale,,
Tejesh Allapure,,
- Student, Department of Electronics &Telecommunication, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune, Maharashtra, India
- Professor, Department of Electronics &Telecommunication, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune, Maharashtra, India
- Student, Department of Electronics &Telecommunication, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune, Maharashtra, India
- Student, Department of Electronics &Telecommunication, Smt. Kashibai Navale College of Engineering, Vadgaon, Pune, Maharashtra, India
Abstract
Various research and development have taken place over the years on computer vision which is a branch of AI. AI disciplines like a vision system is applied in various fields like self-driving cars, face detection by social media apps and law enforcement software’s google lens and so on. The proposed system deals with design and implementation of an efficient way of training a GPU using python libraries to process and classify an image. The design is profoundly described along with its performance efficiency. The image processing techniques presented in this classifier includes the concept of neural networks and machine learning. The base for both model training and model evaluation is provided by Cifar-10 dataset. The core of the image classification system consists of deep neural networks, specifically Convolutional Neural Networks (CNNs) Linear SVC. The results highlight the model’s potential uses in a variety of fields, including healthcare, autonomous vehicles, and content recommendation systems, and show how well the model performs when accurately categorizing a range of photos. The results highlight the model’s potential applications in various fields, such as healthcare, autonomous vehicles, and content recommendation systems. They demonstrate the model’s proficiency in accurately categorizing a wide range of images. This system not only showcases the advancements in computer vision but also underscores the practical implications of these technologies. By leveraging CNNs and other machine learning techniques, the proposed system achieves high accuracy and efficiency, making it a valuable tool for real-world applications. In conclusion, the design and implementation of this image classification system represent a significant step forward in the application of AI in image processing, offering robust solutions for diverse industries.
Keywords: Data Set, Model Selection, Training, Monitoring and Maintenance, SVC, CNN, Python
[This article belongs to Trends in Opto-electro & Optical Communication(toeoc)]
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Trends in Opto-electro & Optical Communication
| Volume | 14 |
| Issue | 02 |
| Received | July 16, 2024 |
| Accepted | July 29, 2024 |
| Published | August 16, 2024 |