Update README.md

README.md

@@ -1,3 +1,121 @@
 ---
 license: gpl-3.0
+tags:
+- object-detection
+- computer-vision
+- sort
+- tracker
+- osnet
 ---
+
+<div align="center">
+<h1>
+  Torchreid-Pip: Packaged version of Torchreid 
+</h1>
+<h4>
+    <img width="700" alt="teaser" src="https://raw.githubusercontent.com/goksenin-uav/torchreid-pip/main/doc/logo.png">
+</h4>
+</div>
+
+This repo is a packaged version of the [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) algorithm.
+### Installation
+```
+pip install torchreid
+```
+
+### Model Description
+[Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1905.00953): 
+[Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1910.06827)
+[Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://arxiv.org/abs/1910.10093)
+
+
+### Overview
+##### 1. Import ``torchreid``
+```python
+import torchreid
+```
+##### 2. Load data manager
+
+```python 
+datamanager = torchreid.data.ImageDataManager(
+    root="reid-data",
+    sources="market1501",
+    targets="market1501",
+    height=256,
+    width=128,
+    batch_size_train=32,
+    batch_size_test=100,
+    transforms=["random_flip", "random_crop"]
+)
+```
+##### 3 Build model, optimizer and lr_scheduler
+
+```python 
+model = torchreid.models.build_model(
+    name="resnet50",
+    num_classes=datamanager.num_train_pids,
+    loss="softmax",
+    pretrained=True
+)
+
+model = model.cuda()
+
+optimizer = torchreid.optim.build_optimizer(
+    model,
+    optim="adam",
+    lr=0.0003
+)
+
+scheduler = torchreid.optim.build_lr_scheduler(
+    optimizer,
+    lr_scheduler="single_step",
+    stepsize=20
+)
+```
+##### 4. Build engine
+
+```python
+engine = torchreid.engine.ImageSoftmaxEngine(
+    datamanager,
+    model,
+    optimizer=optimizer,
+    scheduler=scheduler,
+    label_smooth=True
+)
+```
+##### 5. Run training and test
+
+```python
+engine.run(
+    save_dir="log/resnet50",
+    max_epoch=60,
+    eval_freq=10,
+    print_freq=10,
+    test_only=False
+)
+```
+Citation
+---------
+If you use this code or the models in your research, please give credit to the following papers:
+```bibtex
+@article{torchreid,
+    title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
+    author={Zhou, Kaiyang and Xiang, Tao},
+    journal={arXiv preprint arXiv:1910.10093},
+    year={2019}
+} 
+
+@inproceedings{zhou2019osnet,
+    title={Omni-Scale Feature Learning for Person Re-Identification},
+    author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+    booktitle={ICCV},
+    year={2019}
+}
+
+@article{zhou2021osnet,
+    title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
+    author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+    journal={TPAMI},
+    year={2021}
+}
+```