Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
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Abstract (arXiv)
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.
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References · 14
- 2015Learning Deep Features for Discriminative Localizationcited by 2,430
- 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
- 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
- 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
- 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
- 2018Learning to Navigate for Fine-grained Classificationcited by 103
- 2015Compact Bilinear Poolingcited by 97
- 2020Learning Attentive Pairwise Interaction for Fine-Grained Classificationcited by 84
- 2020Interpretable and Accurate Fine-grained Recognition via Region Groupingcited by 75
- 2017Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalizationcited by 65
- 2019Cross-X Learning for Fine-Grained Visual Categorizationcited by 61
- 2018Multi-Attention Multi-Class Constraint for Fine-grained Image Recognitioncited by 58
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