[Fix] Update metafile with missed model checkpoints (open-mmlab#9890)

FANGAreNotGnu · Mar 13, 2023 · c320060 · c320060
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diff --git a/configs/albu_example/metafile.yml b/configs/albu_example/metafile.yml
@@ -0,0 +1,17 @@
+Models:
+  - Name: mask-rcnn_r50_fpn_albu-1x_coco
+    In Collection: Mask R-CNN
+    Config: mask-rcnn_r50_fpn_albu-1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth
diff --git a/configs/boxinst/README.md b/configs/boxinst/README.md
@@ -15,9 +15,10 @@ of learning masks in instance segmentation, with no modification to the segmenta
 
 ## Results and Models
 
-| Backbone |  Style  | MS train | Lr schd | bbox AP | mask AP |                   Config                   |         Download         |
-| :------: | :-----: | :------: | :-----: | :-----: | :-----: | :----------------------------------------: | :----------------------: |
-|   R-50   | pytorch |    Y     |   1x    |  39.4   |  30.8   | [config](./boxinst_r50_fpn_ms-90k_coco.py) | [model](<>) \| [log](<>) |
+| Backbone |  Style  | MS train | Lr schd | bbox AP | mask AP |                   Config                    |                                                                                                                                                  Download                                                                                                                                                   |
+| :------: | :-----: | :------: | :-----: | :-----: | :-----: | :-----------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |    Y     |   1x    |  39.6   |  31.1   | [config](./boxinst_r50_fpn_ms-90k_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r50_fpn_ms-90k_coco/boxinst_r50_fpn_ms-90k_coco_20221228_163052-6add751a.pth) \| [log](https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r50_fpn_ms-90k_coco/boxinst_r50_fpn_ms-90k_coco_20221228_163052.log.json)   |
+|  R-101   | pytorch |    Y     |   1x    |  41.8   |  32.7   | [config](./boxinst_r101_fpn_ms-90k_coco.py) | [model](https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r101_fpn_ms-90k_coco/boxinst_r101_fpn_ms-90k_coco_20221229_145106-facf375b.pth) \|[log](https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r101_fpn_ms-90k_coco/boxinst_r101_fpn_ms-90k_coco_20221229_145106.log.json) |
 
 ## Citation
 

diff --git a/configs/boxinst/metafile.yml b/configs/boxinst/metafile.yml
@@ -0,0 +1,52 @@
+Collections:
+  - Name: BoxInst
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - ResNet
+        - FPN
+        - CondInst
+    Paper:
+      URL: https://arxiv.org/abs/2012.02310
+      Title: 'BoxInst: High-Performance Instance Segmentation with Box Annotations'
+    README: configs/boxinst/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v3.0.0rc6/mmdet/models/detectors/boxinst.py#L8
+      Version: v3.0.0rc6
+
+Models:
+  - Name: boxinst_r50_fpn_ms-90k_coco
+    In Collection: BoxInst
+    Config: configs/boxinst/boxinst_r50_fpn_ms-90k_coco.py
+    Metadata:
+      Iterations: 90000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 30.8
+    Weights: https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r50_fpn_ms-90k_coco/boxinst_r50_fpn_ms-90k_coco_20221228_163052-6add751a.pth
+
+  - Name: boxinst_r101_fpn_ms-90k_coco
+    In Collection: BoxInst
+    Config: configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py
+    Metadata:
+      Iterations: 90000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 32.7
+    Weights: https://download.openmmlab.com/mmdetection/v3.0/boxinst/boxinst_r101_fpn_ms-90k_coco/boxinst_r101_fpn_ms-90k_coco_20221229_145106-facf375b.pth
diff --git a/configs/centernet/metafile.yml b/configs/centernet/metafile.yml
@@ -44,3 +44,16 @@ Models:
         Metrics:
           box AP: 25.9
     Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth
+
+  - Name: centernet-update_r50-caffe_fpn_ms-1x_coco
+    In Collection: CenterNet
+    Config: configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py
+    Metadata:
+      Batch Size: 16
+      Training Memory (GB): 3.3
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.2
diff --git a/configs/lvis/metafile.yml b/configs/lvis/metafile.yml
@@ -0,0 +1,128 @@
+Models:
+  - Name: mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v0.5
+        Metrics:
+          box AP: 26.1
+      - Task: Instance Segmentation
+        Dataset: LVIS v0.5
+        Metrics:
+          mask AP: 25.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth
+
+  - Name: mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v0.5
+        Metrics:
+          box AP: 27.1
+      - Task: Instance Segmentation
+        Dataset: LVIS v0.5
+        Metrics:
+          mask AP: 27.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth
+
+  - Name: mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v0.5
+        Metrics:
+          box AP: 26.7
+      - Task: Instance Segmentation
+        Dataset: LVIS v0.5
+        Metrics:
+          mask AP: 26.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth
+
+  - Name: mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v0.5
+        Metrics:
+          box AP: 26.4
+      - Task: Instance Segmentation
+        Dataset: LVIS v0.5
+        Metrics:
+          mask AP: 26.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth
+
+  - Name: mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 22.5
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 21.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth
+
+  - Name: mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 24.6
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 23.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth
+
+  - Name: mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 26.7
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 25.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth
+
+  - Name: mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1
+    In Collection: Mask R-CNN
+    Config: configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 27.2
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 25.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth
diff --git a/configs/rpn/metafile.yml b/configs/rpn/metafile.yml
@@ -0,0 +1,127 @@
+Collections:
+  - Name: RPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1506.01497
+      Title: "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
+    README: configs/rpn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/rpn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: rpn_r50-caffe_fpn_1x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_r50-caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 58.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth
+
+  - Name: rpn_r50_fpn_1x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 58.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth
+
+  - Name: rpn_r50_fpn_2x_coco
+    In Collection: RPN
+    Config: rpn_r50_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 58.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth
+
+  - Name: rpn_r101-caffe_fpn_1x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_r101-caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.4
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 60.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth
+
+  - Name: rpn_x101-32x4d_fpn_1x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 60.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth
+
+  - Name: rpn_x101-32x4d_fpn_2x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py
+    Metadata:
+      Training Resources: 8x V100 GPUs
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 61.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth
+
+  - Name: rpn_x101-64x4d_fpn_1x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.1
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 61.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth
+
+  - Name: rpn_x101-64x4d_fpn_2x_coco
+    In Collection: RPN
+    Config: configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py
+    Metadata:
+      Training Resources: 8x V100 GPUs
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          AR@1000: 61.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth
diff --git a/configs/soft_teacher/metafile.yml b/configs/soft_teacher/metafile.yml
@@ -0,0 +1,18 @@
+Collections:
+  - Name: SoftTeacher
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2106.09018
+      Title: "End-to-End Semi-Supervised Object Detection with Soft Teacher"
+    README: configs/soft_teacher/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v3.0.0rc1/mmdet/models/detectors/soft_teacher.py#L20
+      Version: v3.0.0rc1