121 lines
4.2 KiB
YAML
121 lines
4.2 KiB
YAML
Models:
|
|
- Name: mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco
|
|
In Collection: Mask R-CNN
|
|
Config: configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
|
Metadata:
|
|
Training Memory (GB): 11.9
|
|
Epochs: 36
|
|
Training Data: COCO
|
|
Training Techniques:
|
|
- AdamW
|
|
Training Resources: 8x V100 GPUs
|
|
Architecture:
|
|
- Swin Transformer
|
|
Results:
|
|
- Task: Object Detection
|
|
Dataset: COCO
|
|
Metrics:
|
|
box AP: 48.2
|
|
- Task: Instance Segmentation
|
|
Dataset: COCO
|
|
Metrics:
|
|
mask AP: 43.2
|
|
Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth
|
|
Paper:
|
|
URL: https://arxiv.org/abs/2107.08430
|
|
Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
|
|
README: configs/swin/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: mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco
|
|
In Collection: Mask R-CNN
|
|
Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
|
|
Metadata:
|
|
Training Memory (GB): 10.2
|
|
Epochs: 36
|
|
Training Data: COCO
|
|
Training Techniques:
|
|
- AdamW
|
|
Training Resources: 8x V100 GPUs
|
|
Architecture:
|
|
- Swin Transformer
|
|
Results:
|
|
- Task: Object Detection
|
|
Dataset: COCO
|
|
Metrics:
|
|
box AP: 46.0
|
|
- Task: Instance Segmentation
|
|
Dataset: COCO
|
|
Metrics:
|
|
mask AP: 41.6
|
|
Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_20210906_131725-bacf6f7b.pth
|
|
Paper:
|
|
URL: https://arxiv.org/abs/2107.08430
|
|
Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
|
|
README: configs/swin/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: mask_rcnn_swin-t-p4-w7_fpn_1x_coco
|
|
In Collection: Mask R-CNN
|
|
Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
|
|
Metadata:
|
|
Training Memory (GB): 7.6
|
|
Epochs: 12
|
|
Training Data: COCO
|
|
Training Techniques:
|
|
- AdamW
|
|
Training Resources: 8x V100 GPUs
|
|
Architecture:
|
|
- Swin Transformer
|
|
Results:
|
|
- Task: Object Detection
|
|
Dataset: COCO
|
|
Metrics:
|
|
box AP: 42.7
|
|
- Task: Instance Segmentation
|
|
Dataset: COCO
|
|
Metrics:
|
|
mask AP: 39.3
|
|
Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth
|
|
Paper:
|
|
URL: https://arxiv.org/abs/2107.08430
|
|
Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
|
|
README: configs/swin/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: mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco
|
|
In Collection: Mask R-CNN
|
|
Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
|
|
Metadata:
|
|
Training Memory (GB): 7.8
|
|
Epochs: 36
|
|
Training Data: COCO
|
|
Training Techniques:
|
|
- AdamW
|
|
Training Resources: 8x V100 GPUs
|
|
Architecture:
|
|
- Swin Transformer
|
|
Results:
|
|
- Task: Object Detection
|
|
Dataset: COCO
|
|
Metrics:
|
|
box AP: 46.0
|
|
- Task: Instance Segmentation
|
|
Dataset: COCO
|
|
Metrics:
|
|
mask AP: 41.7
|
|
Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco_20210908_165006-90a4008c.pth
|
|
Paper:
|
|
URL: https://arxiv.org/abs/2107.08430
|
|
Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
|
|
README: configs/swin/README.md
|
|
Code:
|
|
URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
|
|
Version: v2.16.0
|