Models:
  - Name: mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco
    In Collection: Mask R-CNN
    Config: configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py
    Metadata:
      Training Memory (GB): 7.3
      Epochs: 36
      Training Data: COCO
      Training Techniques:
        - AdamW
        - Mixed Precision Training
      Training Resources: 8x A100 GPUs
      Architecture:
        - ConvNeXt
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 46.2
      - Task: Instance Segmentation
        Dataset: COCO
        Metrics:
          mask AP: 41.7
    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth
    Paper:
      URL: https://arxiv.org/abs/2201.03545
      Title: 'A ConvNet for the 2020s'
    README: configs/convnext/README.md
    Code:
      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
      Version: v2.16.0

  - Name: cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco
    In Collection: Cascade Mask R-CNN
    Config: configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
    Metadata:
      Training Memory (GB): 9.0
      Epochs: 36
      Training Data: COCO
      Training Techniques:
        - AdamW
        - Mixed Precision Training
      Training Resources: 8x A100 GPUs
      Architecture:
        - ConvNeXt
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 50.3
      - Task: Instance Segmentation
        Dataset: COCO
        Metrics:
          mask AP: 43.6
    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth
    Paper:
      URL: https://arxiv.org/abs/2201.03545
      Title: 'A ConvNet for the 2020s'
    README: configs/convnext/README.md
    Code:
      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
      Version: v2.25.0

  - Name: cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco
    In Collection: Cascade Mask R-CNN
    Config: configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
    Metadata:
      Training Memory (GB): 12.3
      Epochs: 36
      Training Data: COCO
      Training Techniques:
        - AdamW
        - Mixed Precision Training
      Training Resources: 8x A100 GPUs
      Architecture:
        - ConvNeXt
    Results:
      - Task: Object Detection
        Dataset: COCO
        Metrics:
          box AP: 51.8
      - Task: Instance Segmentation
        Dataset: COCO
        Metrics:
          mask AP: 44.8
    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth
    Paper:
      URL: https://arxiv.org/abs/2201.03545
      Title: 'A ConvNet for the 2020s'
    README: configs/convnext/README.md
    Code:
      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
      Version: v2.25.0