Nature Scene Classification Using Transfer Learning with Inception V3 on the Intel Scene Dataset

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 pre-trained models of ImageNet and early stopping and model checkpointswereused 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.