click here to download project abstract/base paper of image classification
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTRACT
In the Dogs vs. Cats Kaggle competition, our project aimed to develop an effective algorithm for image classification. We explored two distinct approaches to tackle this challenge. The initial strategy involved employing a traditional pattern recognition model. This method entailed the extraction of human-crafted features such as color and Dense-SIFT (Scale-Invariant Feature Transform). The images were then represented using a bag-of-words model, and Support Vector Machines (SVMs) classifiers were trained on the derived features.
In contrast, our second approach leveraged the power of Deep Convolutional Neural Networks (CNN). By employing CNNs, we aimed to automatically learn hierarchical features from the images. Subsequently, Backpropagation (BP) Neural Networks and SVMs were trained for the classification task. This deep learning approach allowed us to capture intricate patterns and representations within the data.
Throughout the project, we conducted a series of experiments to enhance our model’s performance on the test dataset. These experiments involved fine-tuning hyperparameters, adjusting the architecture of the CNN, and exploring various data augmentation techniques. The iterative nature of our experimentation process enabled us to refine our models and identify optimal configurations.
In conclusion, our project not only demonstrated the effectiveness of deep learning in image classification but also emphasized the importance of staying at the forefront of technological advancements to tackle challenges in the field.
