Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning (2023-10-14T00:00:00.000000Z)

TL;DR

Hawkeye is designed with a modular architecture, emphasizing high-quality code and human-readable configuration, providing a comprehensive solution for FGIR tasks, and represents the first open-source PyTorch-based library dedicated to FGIR.

Abstract

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.

Authors

Jiabei He

1 papers

Yang Shen

1 papers

Xiu-Shen Wei

1 papers

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

TL;DR

Abstract

Authors

References24 items

Fine-Grained Image Analysis With Deep Learning: A Survey

Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

Neural Prototype Trees for Interpretable Fine-grained Image Recognition

Interpretable and Accurate Fine-grained Recognition via Region Grouping

Channel Interaction Networks for Fine-Grained Image Categorization

Learning Attentive Pairwise Interaction for Fine-Grained Classification

Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization

Selective Sparse Sampling for Fine-Grained Image Recognition

Cross-X Learning for Fine-Grained Visual Categorization

Destruction and Construction Learning for Fine-Grained Image Recognition

Learning to Navigate for Fine-grained Classification

Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

The iNaturalist Species Classification and Detection Dataset

Improving Fine-Grained Visual Classification using Pairwise Confusion

Learning Deep Features for Discriminative Localization

Compact Bilinear Pooling

Bilinear CNN Models for Fine-Grained Visual Recognition

3D Object Representations for Fine-Grained Categorization

Fine-Grained Visual Classification of Aircraft

The iNaturalist Species Classification and Detection Dataset-Supplementary Material

Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs

TheCaltech-UCSDbirds-200-2011dataset

Field of Study

Journal Information

Name

Venue Information

Name

Type

URL

Alternate Names