Animal Detection Using VIT Transformers with 97% Testing Accuracy
Project Overview
The "Animal Detection Using VIT Transformers with 97% Testing Accuracy" project is a focused and achievable initiative aimed at building an accurate animal detection system. Leveraging Vision Transformer (VIT) models, this project is dedicated to the specific goal of achieving a high testing accuracy of 97% in identifying and classifying 90 different animal species.
Project Objectives
- Testing Accuracy of 97%: The central objective of this project is to train a VIT-based model to reach a remarkable testing accuracy of 97% in identifying 90 different animal species. This level of accuracy ensures the project's success and usability.
Project Phases
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Data Collection and Preprocessing: This phase involves collecting a dataset of images representing the 90 animal species and performing necessary data preprocessing tasks such as image augmentation and data cleaning.
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Model Selection and Training: The project selects an appropriate VIT model and proceeds with training it on the prepared dataset. Fine-tuning is a crucial step to optimize the model's performance.
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Evaluation and Testing: Rigorous evaluation and testing of the trained model are conducted to confirm the achievement of the 97% testing accuracy target. This step may involve validation against various animal species.
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Documentation and Reporting: The project documents the process, methodologies, and results obtained, creating a detailed report that describes the steps taken to achieve the testing accuracy target.
Expected Outcomes
The successful completion of this project will result in a highly accurate animal detection system. While it is a limited-scope project, it serves as a valuable demonstration of the capabilities of VIT transformers in solving specific computer vision tasks. The 97% testing accuracy achieved will provide a reliable foundation for future work and applications in fields like wildlife monitoring, educational tools, and hobbyist projects.