Metadata-Version: 2.1
Name: dolg
Version: 0.1.5.1
Summary: Re-implementation of DOLG paper in torch and tensorflow with converted checkpoints
Home-page: https://github.com/Shiro-LK/python-DOLG
Author: Shiro-LK
Author-email: shirosaki94@gmail.com
License: MIT License
Download-URL: https://github.com/Shiro-LK/python-DOLG.git
Description: # DOLG in torch and tensorflow (TF2)
        
        Re-implementation (Non Official) of the paper DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features accepted at ICCV 2021.
        [paper](https://arxiv.org/pdf/2108.02927.pdf)
        
        The pytorch checkpoint has been converted into tensorflow format (.h5) from this repository : https://github.com/feymanpriv/DOLG (Official) 
        
        ## Pipeline
        
        ![Image](images/dolg.png)
        
        ## Installation 
        
        > pip install opencv-python==4.5.5.64
        
        > pip install huggingface-hub
        
        to install dolg : 
        
        > pip install dolg
        OR 
        > pip install -e .
        
        ## Inference
        
        To do some inference on single sample, you can use python script in examples/ folder or use as follows:
        
        ```
        import dolg
        import numpy as np
        from dolg.utils.extraction import process_data
        
        depth = 50
        
        # for pytorch
        
        import torch
        from dolg.dolg_model_pt import DOLG
        from dolg.resnet_pt import ResNet
        
        backbone = ResNet(depth=depth, num_groups=1, width_per_group=64, bn_eps=1e-5, 
                     bn_mom=0.1, trans_fun="bottleneck_transform")
        model = DOLG(backbone, s4_dim=2048, s3_dim=1024, s2_dim=512, head_reduction_dim=512,
                     with_ma=False, num_classes=None, pretrained=f"r{depth}")
        img = process_data("image.jpg", "", mode="pt").unsqueeze(0)
        
        with torch.no_grad():
            output = model(img)
        print(output)
        
        # for tensorflow
        
        import tensorflow as tf
        from dolg.dolg_model_tf2 import DOLG
        from dolg.resnet_tf2 import ResNet
        
        
        backbone = ResNet(depth=depth, num_groups=1, width_per_group=64, bn_eps=1e-5, 
                     bn_mom=0.1, trans_fun="bottleneck_transform", name="globalmodel")
        model = DOLG(backbone, s4_dim=2048, s3_dim=1024, s2_dim=512, head_reduction_dim=512,
                     with_ma=False, num_classes=None, pretrained=f"r{depth}")
        img = process_data("image.jpg", "", mode="tf")
        img = np.expand_dims(img, axis=0)
        output = model.predict(img)
        print(output)
        ```
        
        ## Data 
        
        The model has been trained on google landmark v2. You can find the dataset on the official repository : https://github.com/cvdfoundation/google-landmark .
        
        
        # Citation : 
        
        ```bibtex
        
        @misc{yang2021dolg,
              title={DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features}, 
              author={Min Yang and Dongliang He and Miao Fan and Baorong Shi and Xuetong Xue and Fu Li and Errui Ding and Jizhou Huang},
              year={2021},
              eprint={2108.02927},
              archivePrefix={arXiv},
              primaryClass={cs.CV}
        }
        
        
        @misc{https://doi.org/10.48550/arxiv.2004.01804,
          doi = {10.48550/ARXIV.2004.01804},
          
          url = {https://arxiv.org/abs/2004.01804},
          
          author = {Weyand, Tobias and Araujo, Andre and Cao, Bingyi and Sim, Jack},
          
          keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
          
          title = {Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval},
        ```
Keywords: DOLG for torch and tensorflow,pretrained weights,tensorflow,tf,pytorch,torch
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
