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metafile.yml
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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