Update README.md

README.md

@@ -12,4 +12,51 @@ This repo contains the pre-trained weights of OSNet, specialized for person reco
 
 Related work:
 - [Omni-Scale Feature Learning for Person Re-Identification](https://arxiv.org/abs/1905.00953)
-- [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827)
+- [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://arxiv.org/abs/1910.06827)
+
+# Get started
+
+Install the [Torchreid](https://github.com/KaiyangZhou/deep-person-reid) package.
+
+```bash
+# cd to your preferred directory and clone this repo
+git clone https://github.com/KaiyangZhou/deep-person-reid.git
+
+# create environment
+cd deep-person-reid/
+conda create --name torchreid python=3.7
+conda activate torchreid
+
+# install dependencies
+# make sure `which python` and `which pip` point to the correct path
+pip install -r requirements.txt
+
+# install torch and torchvision (select the proper cuda version to suit your machine)
+conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
+
+# install torchreid (don't need to re-build it if you modify the source code)
+python setup.py develop
+```
+
+Use Torchreid as a feature extractor in your project.
+
+```python
+from torchreid.utils import FeatureExtractor
+
+extractor = FeatureExtractor(
+    model_name='osnet_x1_0',
+    model_path='a/b/c/model.pth.tar',
+    device='cuda'
+)
+
+image_list = [
+    'a/b/c/image001.jpg',
+    'a/b/c/image002.jpg',
+    'a/b/c/image003.jpg',
+    'a/b/c/image004.jpg',
+    'a/b/c/image005.jpg'
+]
+
+features = extractor(image_list)
+print(features.shape) # output (5, 512)
+```