diff --git a/README.md b/README.md
index 5b51ff3..088cef5 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,22 @@ This repo uses Centernet and Conditional Convolutions for Instance Segmentation
> [**Objects as Points**](http://arxiv.org/abs/1904.07850),
> [**CondInst: Conditional Convolutions for Instance Segmentation**](https://arxiv.org/abs/2003.05664)
+## Result
+
+These results are taken for CenterSeg model trained for 101 epochs
+
+| type | AP | AP50 | AP75 | APs | APm | APl |
+| ---- | ----- | --------------- | --------------- | -------------- | -------------- | -------------- |
+| box | 0.278 | 0.430 | 0.297 | 0.129 | 0.305 | 0.382 |
+| mask | 0.226 | 0.387 | 0.227 | 0.078 | 0.253 | 0.340 |
+
+| type | AR | AR50 | AR75 | ARs | ARm | ARl |
+| ---- | ----- | --------------- | --------------- | -------------- | -------------- | -------------- |
+| box | 0.275 | 0.455 | 0.480 | 0.265 | 0.510 | 0.674 |
+| mask | 0.235 | 0.369 | 0.385 | 0.170 | 0.418 | 0.585 |
+
+CenterPoseSeg model not trained yet
+
## Installation
This repo supports both CPU and GPU Training and Inference.
@@ -24,6 +40,7 @@ python3 setup.py build develop
```
Compile NMS
+
```
cd src/lib/external
@@ -47,16 +64,19 @@ Note: Model is not completely trained (101 Epochs only). Will update later.
#### Training
###### For GPU
+
```
python3 main.py ctseg --exp_id coco_dla_1x --batch_size 10 --master_batch 5 --lr 1.25e-4 --gpus 0 --num_workers 4
```
###### FOR CPU
+
```
python3 main.py ctseg --exp_id coco_dla_1x --batch_size 2 --master_batch -1 --lr 1.25e-4 --gpus -1 --num_workers 4
```
#### Testing
+
```
python3 test.py ctseg --exp_id coco_dla_1x --keep_res --resume
```
@@ -73,4 +93,3 @@ https://github.com/xingyizhou
https://github.com/Epiphqny
https://github.com/CaoWGG
-
diff --git a/src/lib/trains/ctseg.py b/src/lib/trains/ctseg.py
index d5721fc..85f1dda 100644
--- a/src/lib/trains/ctseg.py
+++ b/src/lib/trains/ctseg.py
@@ -68,7 +68,6 @@ def forward(self, outputs, batch):
if opt.reg_offset and opt.off_weight > 0:
off_loss += self.crit_reg(output['reg'], batch['reg_mask'],
batch['ind'], batch['reg']) / opt.num_stacks
-
mask_loss += self.crit_mask(output['seg_feat'], output['conv_weight'],
batch['reg_mask'], batch['ind'], batch['instance_mask'])