Nature Scene Classification Using Transfer Learning with InceptionV3 on the Intel Scene Dataset
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 3
Abstract
Nature scene classification is vital for various applications, including environmental monitoring and autonomous systems need to develop efficient models that can sort out different scenes. This work proposes a new approach using state-of-the-art CNNs like InceptionV3, Xception and VGG19, to enhance the classification accuracy and generalization of nature scenes. We worked with six classes with 20,926 training images and 5,228 validation images and augmented the data to improve the model. Models were fine-tuned from the pretrained models of ImageNet and early stopping and model checkpoints were used to avoid overfitting. The results indicated that the proposed InceptionV3 model achieved a training accuracy of 94.49% and validation accuracy of 92.81% which is higher than previous work and Xception model had a high accuracy of 95.52% but the model might be overfitting. During the comparison of the results, it was revealed that InceptionV3 provided the highest accuracy with the least standard deviation, which proved the effectiveness of the selected architecture for scene classification. These results indicate that the selection of the model and the technique for the classification of nature scenes is important. It is a good advancement in the field of nature scene classification and provides a reliable solution to enhance accuracy in real-world scenarios.
Authors and Affiliations
Muhammad Talha Jahangir, Syed Yameen Ali, Muhammad Hamza Khan, Ahmad Ali Zain, Umair Arshad
Keywords
Related Articles
- EP ID EP760525
- DOI -
- Views 8
- Downloads 0
Comprehensive Assessment of Air Quality Dynamics Around Yosemite National Park Using Remote Sensing, GIS, and Computational Analysis During Wildfire Events
In recent years, Mariposa County has experienced several significant wildfires, including the catastrophic Rim Fire of 2013. On July 22, 2022, Yosemite National Park faced one of its most devastating wildfires, profoun...
Plant Disease Detection Using Computational Approaches: A Systematic Literature Review
Rapid improvements in ML and DL techniques have made it possible to detect and recognize objects from images. Computational approaches using ML and DL have been recently applied to agriculture or farming applications an...
Machine Learning-Based Heart Disease Classification for Symptom-Driven Diagnostics
Heart diseases are increasing over the period while identifying cardiac diseases at an early stage continues to pose a challenge. This study focuses on the application of AI specifically in machine learning to improve...
VDMF: VANETsDetection Mechanism Using Fog Computing forCollusion and Sybil Attacks
Vehicular Ad Hoc Networks (VANETs) have evolved as a key component of the intelligent transportation system, enhancing road safety and traffic efficiency. It is crucial to secure sensitive information, and detection of...
Comparison of IoT Messaging Protocols: A novel Crop-Specific Protocolfor Wheat, Banana,and Chili
IoT systems mostly depend on messaging protocols to facilitate the exchange of IoT data, with various protocols or frameworks available to support different types of messaging patterns. Choosing a suitable IoT messagin...