Update training.md

docs/training.md

@@ -1 +1,53 @@
-Under preparation.
+Firstly you need to prepare the dataset as described [here](https://github.com/Nicholasli1995/EgoNet/blob/master/docs/preparation.md).
+
+Then download a start point model [here](https://drive.google.com/file/d/1VFtMGgBG0cLGnbr3brrnPnJii2xGYj-9/view?usp=sharing) and place it at ${EgoNet_DIR}/resources. 
+
+The training phase consists of two stages which are described as follows. 
+
+For training on other datasets. You need to prepare the training images and camera parameters accordingly.
+
+## Stage 1: train a lifter (L.pth)
+You need to modify the configuration by
+
+```bash
+cd ${EgoNet_DIR}/configs && vim KITTI_train_lifting.yml
+```
+Edit dataset:root to your KITTI directory.
+
+(Optional) Edit dirs:output to where you want to save the output model.
+
+(Optional) You can evaluate during training by setting eval_during to True.
+
+Finally, run
+
+```bash
+ cd tools
+ python train_lifting.py --cfg "../configs/KITTI_train_lifting.yml"
+```
+
+
+## Stage 2: train the remaining part (HC.pth)
+You need to modify the configuration by
+
+```bash
+cd ${EgoNet_DIR}/configs && vim KITTI_train_IGRs.yml
+```
+
+Edit dataset:root to your KITTI directory.
+
+Edit gpu_id according to your local machine and set batch_size based on how much GPU memory you have. 
+
+(Optional) Edit dirs:output to where you want to save the output model.
+
+(Optional) You can evaluate during training by setting eval_during to True.
+
+(Optional) Edit ss to enable self-supervised representation learning. You need to prepare unlabeled images as described here.
+
+(Optional) Edit training_settings:debug to disable saveing intermediate training results.
+
+Finally, run
+
+```bash
+ cd tools
+ python train_IGRs.py --cfg "../configs/KITTI_train_IGRs.yml"
+```