COCO Evaluate Results

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    | AP<sub>50</sub> | AP<sub>75</sub> | AP<sub>s</sub> | AP<sub>m</sub> | AP<sub>l</sub> |
+| ---- | ----- | --------------- | --------------- | -------------- | -------------- | -------------- |
+| 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    | AR<sub>50</sub> | AR<sub>75</sub> | AR<sub>s</sub> | AR<sub>m</sub> | AR<sub>l</sub> |
+| ---- | ----- | --------------- | --------------- | -------------- | -------------- | -------------- |
+| 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
-

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'])