Update README.md

Author: hangzhaomit

README.md

@@ -44,7 +44,7 @@ Decoder:
 - c1_bilinear (1 conv + bilinear upsample)
 - c1_bilinear_deepsup (c1_blinear + deep supervision trick)
 - ppm_bilinear (pyramid pooling + bilinear upsample, see PSPNet paper for details)
-- ppm_bilinear_deepsup (psp_bilinear + deep supervision trick)
+- ppm_bilinear_deepsup (ppm_bilinear + deep supervision trick)
 
 ***Coming soon***:
 - UPerNet based on Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM), with down-sampling rate of 4, 8 and 16. It doesn't need dilated convolution, a operator that is time-and-memory consuming. *Without bells and whistles*, it is comparable or even better compared with PSPNet, while requires much shorter training time and less GPU memory.
@@ -66,7 +66,7 @@ IMPORTANT: We use our self-trained base model on ImageNet. The model takes the i
         <td>27.5 hours</td>
     </tr>
     <tr>
-        <td rowspan="2">ResNet-50_dilated8 + psp_bilinear_deepsup</td>
+        <td rowspan="2">ResNet-50_dilated8 + ppm_bilinear_deepsup</td>
         <td>No</td><td>41.26</td><td>79.73</td><td>60.50</td>
         <td rowspan="2">33.4 hours</td>
     </tr>
@@ -110,7 +110,7 @@ The code is developed under the following configurations.
 chmod +x download_ADE20K.sh
 ./download_ADE20K.sh
 ```
-2. Train a network (default: ResNet-50_dilated8 + psp_bilinear_deepsup). During training, checkpoints will be saved in folder ```ckpt```.
+2. Train a network (default: ResNet-50_dilated8 + ppm_bilinear_deepsup). During training, checkpoints will be saved in folder ```ckpt```.
 ```bash
 python3 train.py --num_gpus NUM_GPUS
 ```