Hawkeye: An Easy-to-Use Open Source Tool for Fine-Grained Image Recognition Based on PyTorch

Hawkeye: An Easy-to-Use Open Source Tool for Fine-Grained Image Recognition Based on PyTorch

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Machine Heart Column

Machine Heart Editorial Department
Fine-grained image recognition [1] is an important research topic in visual perception learning, with enormous application value in the new intelligent economy and industrial internet, and has been widely applied in many real-world scenarios… Given the current lack of deep learning open-source toolkits in this field, Professor Wei Xiucan’s team from Nanjing University of Science and Technology has spent nearly a year developing and refining Hawkeye—a deep learning open-source toolkit for fine-grained image recognition, for researchers and engineers in related fields to reference and use. This article provides a detailed introduction to Hawkeye.

Table of Contents

1. What is the Hawkeye Library

2. Models and Methods Supported by Hawkeye

3. Installing Hawkeye

4. Training Models with Hawkeye

1. What is the Hawkeye Library

Hawkeye: An Easy-to-Use Open Source Tool for Fine-Grained Image Recognition Based on PyTorch

Hawkeye is a deep learning tool library for fine-grained image recognition based on PyTorch, designed specifically for researchers and engineers in related fields. Currently, Hawkeye includes various representative paradigms for fine-grained recognition methods, including “based on deep filters”, “based on attention mechanisms”, “based on high-order feature interactions”, “based on special loss functions”, “based on network data” and other methods.

The Hawkeye project features good coding style, clear and readable structure, and strong extensibility. For those new to the field of fine-grained image recognition, Hawkeye is relatively easy to get started with, facilitating their understanding of the main processes and representative methods of fine-grained image recognition, while also allowing for quick implementation of their algorithms on this toolkit. Additionally, we provide training example code for each model in the library, and self-developed methods can also be quickly adapted and added to Hawkeye according to the examples.

Hawkeye open-source library link: https://github.com/Hawkeye-FineGrained/Hawkeye

2. Models and Methods Supported by Hawkeye

Hawkeye currently supports a total of 16 models and methods from the main learning paradigms in fine-grained image recognition, as detailed below:

  • Based on Deep Filters

  • S3N (ICCV 2019)

  • Interp-Parts (CVPR 2020)

  • ProtoTree (CVPR 2021)

  • Based on Attention Mechanisms

  • OSME+MAMC (ECCV 2018)

  • MGE-CNN (ICCV 2019)

  • APCNN (IEEE TIP 2021)

  • Based on High-Order Feature Interactions

  • BCNN (ICCV 2015)

  • CBCNN (CVPR 2016)

  • Fast MPN-COV (CVPR 2018)

  • Based on Special Loss Functions

  • Pairwise Confusion (ECCV 2018)

  • API-Net (AAAI 2020)

  • CIN (AAAI 2020)

  • Based on Network Data

  • Peer-Learning (ICCV 2021)

  • Other Methods

  • NTS-Net (ECCV 2018)

  • CrossX (ICCV 2019)

  • DCL (CVPR 2019)

3. Installing Hawkeye

Installing Dependencies

Use conda or pip to install the required dependencies:

  • Python 3.8

  • PyTorch 1.11.0 or higher

  • torchvison 0.12.0 or higher

  • numpy

  • yacs

  • tqdm

Clone the repository:

git clone https://github.com/Hawkeye-FineGrained/Hawkeye.gitcd Hawkeye
Bash

Preparing the Dataset

We provide 8 commonly used fine-grained recognition datasets along with the latest download links:

First, download a dataset (using CUB200 as an example):

cd Hawkeye/datawget https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgzmkdir bird && tar -xvf CUB_200_2011.tgz -C bird/
Bash

We provide metadata files for the above 8 datasets, which can match with the FGDataset in the library to conveniently load training and testing sets, with the training and testing sets being the official splits provided by each dataset. When using different datasets, simply modify the dataset configuration in the experiment’s config file for easy switching.

Modify the dataset configuration in the experiment’s config file, as shown below:

dataset:  name: cub  root_dir: data/bird/CUB_200_2011/images  meta_dir: metadata/cub
YAML

4. Training Models with Hawkeye

For each method supported by Hawkeye, we provide separate training templates and configuration files. For example, training APINet requires just one command:

python Examples/APINet.py --config configs/APINet.yaml
Bash

Experiment parameters are all in the corresponding yaml files, which are highly readable and easy to modify, such as:

experiment:name: API_res101 2        # Experiment name  log_dir: results/APINet   # Directory for experiment logs, results, etc.  seed: 42                  # Optionally set a fixed random seed#  resume: results/APINet/API_res101 2/checkpoint_epoch_19.pth    # Optionally resume training from a checkpointdataset:  name: cub          # Use CUB200 dataset  root_dir: data/bird/CUB_200_2011/images   # Path where images are placed in the dataset  meta_dir: metadata/cub                  # Path for CUB200 metadata  n_classes: 10         # Number of classes, required by APINet  n_samples: 4          # Number of samples per class  batch_size: 24        # Batch size during testing  num_workers: 4      # Number of threads for DataLoader  transformer:        # Data augmentation parameter configuration    image_size: 224      # Input image size for the model 224x224    resize_size: 256    # Size before image augmentation 256x256model:  name: APINet        # Use APINet model, see `model/methods/APINet.py`  num_classes: 200      # Number of classes#  load: results/APINet/API_res101 1/best_model.pth     # Optionally load pre-trained model parameterstrain:  cuda: [4]          # List of GPU device IDs to use, [] uses CPU  epoch: 100        # Number of training epochs  save_frequence: 10    # Frequency of automatic model saving#  val_first: False      # Optionally test model accuracy before training  optimizer:    name: Adam        # Use Adam optimizer    lr: 0.0001        # Learning rate of 0.0001    weight_decay: 0.00000002  scheduler:    # This example uses a custom combined scheduler, consisting of warmup and cosine annealing learning rates, see `Examples/APINet.py`    name: ''    T_max: 100        # Total iterations for the scheduler    warmup_epochs: 8    # Warmup epochs    lr_warmup_decay: 0.01  # Warmup decay ratio  criterion:    name: APINetLoss    # Loss function used by APINet, see `model/loss/APINet_loss.py`
YAML

The main program for the experiment, Examples/APINet.py, has a trainer APINetTrainer that inherits from Trainer, eliminating the need to write complex training processes, loggers, model saving, configuration loading, and other code; only necessary modules need to be modified. We also provide multiple hooks for the training phase to meet the specific implementation methods of some approaches.

Log files, model weight files, training code used, and the configuration files at that time will all be saved in the experiment output directory log_dir, making it easy to back up configurations and training code for future comparison of different experiments.

For more detailed examples, please refer to the specific information in the project link: https://github.com/Hawkeye-FineGrained/Hawkeye

References

[1] X.-S. Wei, Y.-Z. Song, O. Mac Aodha, J. Wu, Y. Peng, J. Tang, J. Yang, and S. Belongie. Fine-Grained Image Analysis with Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), DOI: 10.1109/TPAMI.2021.3126648. https://ieeexplore.ieee.org/document/9609630

Hawkeye: An Easy-to-Use Open Source Tool for Fine-Grained Image Recognition Based on PyTorch

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