Update README.md

README.md

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 ---
 license: mit
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+
+# 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) 
+
+
+
+## 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},
+```