diff --git a/configs/knet/README.md b/configs/knet/README.md
index a51c5cbcf..0a881406f 100644
--- a/configs/knet/README.md
+++ b/configs/knet/README.md
@@ -43,7 +43,7 @@ Semantic, instance, and panoptic segmentations have been addressed using differe
 | KNet + UPerNet   | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)         |
 | KNet + UPerNet   | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)         |
 | KNet + UPerNet   | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)         |
-| KNet + UPerNet   | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)         |
+| KNet + UPerNet   | Swin-L   | 640x640   | 80000   | 18.31    | 5.55           | 52.46 | 53.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220720_165636-cbcaed32.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220720_165636.log.json)         |
 
 Note:
 
diff --git a/configs/knet/knet.yml b/configs/knet/knet.yml
index 5e2e52955..1dec27be2 100644
--- a/configs/knet/knet.yml
+++ b/configs/knet/knet.yml
@@ -152,18 +152,18 @@ Models:
     crop size: (640,640)
     lr schd: 80000
     inference time (ms/im):
-    - value: 120.63
+    - value: 180.18
       hardware: V100
       backend: PyTorch
       batch size: 1
       mode: FP32
       resolution: (640,640)
-    Training Memory (GB): 13.54
+    Training Memory (GB): 18.31
   Results:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 52.21
-      mIoU(ms+flip): 53.34
+      mIoU: 52.46
+      mIoU(ms+flip): 53.78
   Config: configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220720_165636-cbcaed32.pth
diff --git a/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py b/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
index fc6e9fe39..eceeb4ab3 100644
--- a/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
+++ b/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
@@ -46,9 +46,10 @@ test_pipeline = [
             dict(type='Collect', keys=['img']),
         ])
 ]
+# In K-Net implementation we use batch size 2 per GPU as default
 data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
     train=dict(pipeline=train_pipeline),
     val=dict(pipeline=test_pipeline),
     test=dict(pipeline=test_pipeline))
-# In K-Net implementation we use batch size 2 per GPU as default
-data = dict(samples_per_gpu=2, workers_per_gpu=2)