Animal Detector Using Deep Learning and Tkinter GUI is a desktop application that uses a trained deep learning model to detect and classify animals in images or video streams, with a user-friendly interface built using Tkinter.
Price : 16000
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The integration of Artificial Intelligence (AI) and image processing has led to significant advancements in object recognition and classification tasks. One such application is animal detection, which plays a crucial role in wildlife monitoring, smart farming, surveillance, and safety systems. This project, titled "Animal Detector Using Deep Learning and Tkinter GUI," aims to develop an intelligent system that can automatically identify animals in images using deep learning techniques and present the results through a graphical user interface.
In this system, we employ a Convolutional Neural Network (CNN)—a class of deep learning models specifically designed for image classification and pattern recognition. The model is trained on a dataset consisting of various animal categories such as cats, dogs, lions, elephants, and more. Once trained, the model can accurately classify new, unseen images based on learned patterns.
To enhance usability, the application includes a Tkinter-based GUI, which allows users to interact with the system without needing any coding knowledge. Users can upload images, trigger detection, and view the results (e.g., predicted animal name and confidence score) directly within the application. This makes the tool not only educational for machine learning learners but also practical for real-world applications where quick animal recognition is needed.
Overall, the project demonstrates the potential of combining deep learning algorithms with a simple desktop interface, offering a robust solution for animal detection that is accessible, efficient, and highly scalable.
