[Fix] Update correct In Collection in metafile of each configs. (op…

…en-mmlab#1239) * change md2yml file * update metafile * update twins In Collection automatically * fix twins metafile * fix twins metafile * all metafile use value of Method * update collect name * update collect name * fix some typo * fix FCN D6 * change JPU to FastFCN * fix some typos in DNLNet, NonLocalNet, SETR, Segmenter, STDC, FastSCNN * fix typo in stdc * fix typo in DNLNet and UNet * fix NonLocalNet typo
AetrexTechnology · Feb 23, 2022 · 59fa6f6 · 59fa6f6
1 parent bcd0c89
commit 59fa6f6
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Showing 52 changed files with 718 additions and 810 deletions.
diff --git a/.dev/md2yml.py b/.dev/md2yml.py
@@ -81,6 +81,13 @@ def parse_md(md_file):
     code_version = None
     repo_url = None
 
+    # To avoid re-counting number of backbone model in OpenMMLab,
+    # if certain model in configs folder is backbone whose name is already
+    # recorded in MMClassification, then the `COLLECTION` dict of this model
+    # in MMSegmentation should be deleted, and `In Collection` in `Models`
+    # should be set with head or neck of this config file.
+    is_backbone = None
+
     with open(md_file, 'r') as md:
         lines = md.readlines()
         i = 0
@@ -117,9 +124,13 @@ def parse_md(md_file):
                 datasets.append(line[4:])
                 current_dataset = line[4:]
                 i += 2
+            elif line[:15] == '<!-- [BACKBONE]':
+                is_backbone = True
+                i += 1
             elif line[0] == '|' and (
                     i + 1) < len(lines) and lines[i + 1][:3] == '| -':
                 cols = [col.strip() for col in line.split('|')]
+                method_id = cols.index('Method')
                 backbone_id = cols.index('Backbone')
                 crop_size_id = cols.index('Crop Size')
                 lr_schd_id = cols.index('Lr schd')
@@ -155,11 +166,12 @@ def parse_md(md_file):
                         mem_id] != '' else -1
                     crop_size = els[crop_size_id].split('x')
                     assert len(crop_size) == 2
+                    method = els[method_id].split()[0].split('-')[-1]
                     model = {
                         'Name':
                         model_name,
                         'In Collection':
-                        collection_name,
+                        method,
                         'Metadata': {
                             'backbone': els[backbone_id],
                             'crop size': f'({crop_size[0]},{crop_size[1]})',
@@ -213,6 +225,7 @@ def parse_md(md_file):
     flag = (code_url is not None) and (paper_url is not None) and (repo_url
                                                                    is not None)
     assert flag, f'{collection_name} readme error'
+    collection['Name'] = method
     collection['Metadata']['Training Data'] = datasets
     collection['Code']['URL'] = code_url
     collection['Code']['Version'] = code_version
@@ -232,8 +245,10 @@ def parse_md(md_file):
                     collection.pop(check_key)
                 else:
                     collection[check_key].pop(key)
-
-    result = {'Collections': [collection], 'Models': models}
+    if is_backbone:
+        result = {'Models': models}
+    else:
+        result = {'Collections': [collection], 'Models': models}
     yml_file = f'{md_file[:-9]}{collection_name}.yml'
     return dump_yaml_and_check_difference(result, yml_file)
 

diff --git a/configs/ann/ann.yml b/configs/ann/ann.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: ann
+- Name: ANN
   Metadata:
     Training Data:
     - Cityscapes
@@ -16,7 +16,7 @@ Collections:
     Code: https://github.com/MendelXu/ANN
 Models:
 - Name: ann_r50-d8_512x1024_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -38,7 +38,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
 - Name: ann_r101-d8_512x1024_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -60,7 +60,7 @@ Models:
   Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
 - Name: ann_r50-d8_769x769_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -82,7 +82,7 @@ Models:
   Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
 - Name: ann_r101-d8_769x769_40k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -104,7 +104,7 @@ Models:
   Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
 - Name: ann_r50-d8_512x1024_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -118,7 +118,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
 - Name: ann_r101-d8_512x1024_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -132,7 +132,7 @@ Models:
   Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
 - Name: ann_r50-d8_769x769_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -146,7 +146,7 @@ Models:
   Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
 - Name: ann_r101-d8_769x769_80k_cityscapes
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -160,7 +160,7 @@ Models:
   Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
 - Name: ann_r50-d8_512x512_80k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -182,7 +182,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
 - Name: ann_r101-d8_512x512_80k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -204,7 +204,7 @@ Models:
   Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
 - Name: ann_r50-d8_512x512_160k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -218,7 +218,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
 - Name: ann_r101-d8_512x512_160k_ade20k
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -232,7 +232,7 @@ Models:
   Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
 - Name: ann_r50-d8_512x512_20k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -254,7 +254,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
 - Name: ann_r101-d8_512x512_20k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -276,7 +276,7 @@ Models:
   Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
 - Name: ann_r50-d8_512x512_40k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -290,7 +290,7 @@ Models:
   Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
 - Name: ann_r101-d8_512x512_40k_voc12aug
-  In Collection: ann
+  In Collection: ANN
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)

diff --git a/configs/apcnet/apcnet.yml b/configs/apcnet/apcnet.yml
@@ -1,5 +1,5 @@
 Collections:
-- Name: apcnet
+- Name: APCNet
   Metadata:
     Training Data:
     - Cityscapes
@@ -15,7 +15,7 @@ Collections:
     Code: https://github.com/Junjun2016/APCNet
 Models:
 - Name: apcnet_r50-d8_512x1024_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -37,7 +37,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
 - Name: apcnet_r101-d8_512x1024_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -59,7 +59,7 @@ Models:
   Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
 - Name: apcnet_r50-d8_769x769_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -81,7 +81,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
 - Name: apcnet_r101-d8_769x769_40k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -103,7 +103,7 @@ Models:
   Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
 - Name: apcnet_r50-d8_512x1024_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,1024)
@@ -117,7 +117,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
 - Name: apcnet_r101-d8_512x1024_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,1024)
@@ -131,7 +131,7 @@ Models:
   Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
 - Name: apcnet_r50-d8_769x769_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (769,769)
@@ -145,7 +145,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
 - Name: apcnet_r101-d8_769x769_80k_cityscapes
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (769,769)
@@ -159,7 +159,7 @@ Models:
   Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
 - Name: apcnet_r50-d8_512x512_80k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -181,7 +181,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
 - Name: apcnet_r101-d8_512x512_80k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)
@@ -203,7 +203,7 @@ Models:
   Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
 - Name: apcnet_r50-d8_512x512_160k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-50-D8
     crop size: (512,512)
@@ -217,7 +217,7 @@ Models:
   Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
 - Name: apcnet_r101-d8_512x512_160k_ade20k
-  In Collection: apcnet
+  In Collection: APCNet
   Metadata:
     backbone: R-101-D8
     crop size: (512,512)

diff --git a/configs/bisenetv1/README.md b/configs/bisenetv1/README.md
@@ -53,7 +53,7 @@ Semantic segmentation requires both rich spatial information and sizeable recept
 | BiSeNetV1| R-18-D32 | 512x512  | 160000 | 6.33 | 74.24 | 28.55 | 29.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json) |
 | BiSeNetV1 (No Pretrain) | R-50-D32 | 512x512  | 160000 | - | - | 29.82 | 30.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json) |
 | BiSeNetV1 | R-50-D32 | 512x512  | 160000 | 9.28 | 32.60 | 34.88 | 35.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json) |
-| BiSeNetV1(No Pretrain) | R-101-D32 | 512x512  | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) |
+| BiSeNetV1 (No Pretrain) | R-101-D32 | 512x512  | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) |
 | BiSeNetV1 | R-101-D32 | 512x512  | 160000 | 10.36 | 25.25 | 37.38 | 37.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) |
 
 Note: