add in readme

README.md

@@ -10,15 +10,65 @@
 
 Humans can recognize novel objects in this image despite having never seen them  before. “Is it possible to learn open-world (novel) object proposals?” In this paper we propose **Object Localization Network (OLN)** that learns localization cues instead of foreground vs background classification. Only trained on COCO, OLN is able to propose many novel objects (top) missed by Mask R-CNN (bottom) on an out-of-sample frame in an ego-centric video.
 
-<img src="./images/epic.png" width="300"> <img src="./images/oln_overview.png" width="600"> <br/>
+<img src="./images/epic.png" width="500"> <img src="./images/oln_overview.png" width="500"> <br/>
 
 ## License
 
 This project is released under the [Apache 2.0 license](LICENSE).
 
-## Installation
+## Disclaimer
+
+This repo is tested under Python 3.7, PyTorch 1.7.0, Cuda 11.0, and mmcv==1.2.5.
+
+## Installation
+This repo is built based on [mmdetection](https://github.com/open-mmlab/mmdetection). 
+
+You can use following commands to create conda env with related dependencies.
+```
+conda create -n oln python=3.7 -y
+conda activate oln
+conda install pytorch=1.7.0 torchvision cudatoolkit=11.0 -c pytorch -y
+pip install mmcv-full
+pip install -r requirements.txt
+pip install -v -e . 
+```
+Please also refer to [get_started.md](docs/get_started.md) for more details of installation.
+
+
+## Prepare datasets
+
+COCO dataset is available from official websites. It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to $OLN/data as below.
+```
+object_localization_network
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+
+```
+
+
+## Testing
+Our trained models are available for download [here](https://drive.google.com/uc?id=1KcHYnghbs2KC6hQc7QVkPkEiJMrLr73s). Rename it to `latest.pth` and run the following commands to test OLN on COCO dataset.
+
+```
+# Multi-GPU distributed testing
+bash ./tools/dist_test_bbox.sh configs/oln_box/oln_box.py \
+  trained_weights/latest.pth ${NUM_GPUS}
+```
+
+
+## Training
+```
+# Multi-GPU distributed training
+bash ./tools/dist_train.sh configs/oln_box/oln_box.py ${NUM_GPUS}
+```
 
-Please refer to [get_started.md](docs/get_started.md) for installation.
 
 ## Acknowledgement