From 9929957436f5cb5540f6490ffe61bd24d4524f3e Mon Sep 17 00:00:00 2001
From: Yongbin Sun <yb_sun@mit.edu>
Date: Sun, 4 Feb 2018 20:34:34 -0500
Subject: [PATCH] Update README.md

---
 sem_seg/README.md | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/sem_seg/README.md b/sem_seg/README.md
index 51e220c..f7bd8a3 100644
--- a/sem_seg/README.md
+++ b/sem_seg/README.md
@@ -11,3 +11,23 @@ Download 3D indoor parsing dataset (<a href="http://buildingparser.stanford.edu/
 python collect_indoor3d_data.py
 ```
 to generate "dgcnn/data/stanford_indoor3d"
+
+### Train
+
+We use 6-fold training, such that 6 models are trained leaving 1 of 6 areas as the testing area for each model. We keep using 2 GPUs for distributed training. To train 6 models sequentially, run
+```
+sh +x train_job.sh
+```
+
+### Evaluation
+
+1. To generate predicted results for all 6 areas, run 
+```
+sh +x test_job.sh
+```
+The model parameters are saved every 10 epochs, the saved model used to generate predited results can be changed by setting "--model_path" in "test_job.sh". For example, if you want to use the model saved after 70 epochs, you can set "--model_path" to "log*n*/epoch_70.ckpt" for *n* = 1, 2, ..., 6. To visualize the results, you can add "--visu" flag in the end of each line in "test_job.sh".
+
+2. To obtain overall quantitative evaluation results, run
+```
+python eval_iou_accuracy.py
+```