mlsanityData to 7 Oct 2026

Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning

arXiv 2310.09600 ↗First posted 14 Oct 2023cs.CVcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 14 Oct 2023.

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References · 14

  1. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  2. 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
  3. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
  4. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
  5. 2021Fine-Grained Image Analysis with Deep Learning: A Surveycited by 135
  6. 2018Learning to Navigate for Fine-grained Classificationcited by 103
  7. 2015Compact Bilinear Poolingcited by 97
  8. 2020Learning Attentive Pairwise Interaction for Fine-Grained Classificationcited by 84
  9. 2020Interpretable and Accurate Fine-grained Recognition via Region Groupingcited by 75
  10. 2017Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalizationcited by 65
  11. 2019Cross-X Learning for Fine-Grained Visual Categorizationcited by 61
  12. 2018Multi-Attention Multi-Class Constraint for Fine-grained Image Recognitioncited by 58
2 more
  1. 2020Channel Interaction Networks for Fine-Grained Image Categorizationcited by 47
  2. 2021Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approachcited by 32

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