Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
This provides a comprehensive tool for researchers and practitioners in computer vision to advance FGIR, though it is incremental as it packages existing methods into a library.
The authors tackled the lack of a unified open-source software library for Fine-Grained Image Recognition (FGIR) by developing Hawkeye, a PyTorch-based library that implements 16 state-of-the-art methods across 6 paradigms, making it publicly available to researchers and practitioners.
Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Internet applications. However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field. To address this gap, we present Hawkeye, a PyTorch-based library for FGIR with deep learning. Hawkeye is designed with a modular architecture, emphasizing high-quality code and human-readable configuration, providing a comprehensive solution for FGIR tasks. In Hawkeye, we have implemented 16 state-of-the-art fine-grained methods, covering 6 different paradigms, enabling users to explore various approaches for FGIR. To the best of our knowledge, Hawkeye represents the first open-source PyTorch-based library dedicated to FGIR. It is publicly available at https://github.com/Hawkeye-FineGrained/Hawkeye/, providing researchers and practitioners with a powerful tool to advance their research and development in the field of FGIR.