Collection¶
To keep related model together, you can create Collections. The metadata format for collections is the same as for models and all member models inherit all the metadata and can override/add to it.
The fields of a collection are exactly the same as fields of a Model - see the description there.
A full example¶
Collections:
- Name: Mask R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
Training Resources: 8x V100 GPUs
Architecture:
- RoI Align
- RPN
Paper: https://arxiv.org/abs/1703.06870v3
README: docs/maskrcnn.md
Models:
- Name: Mask R-CNN (R101-C4, 3x)
In Collection: Mask R-CNN
Metadata:
inference time (s/im): 0.145
train time (s/iter): 0.652
Training Memory (GB): 6.3
Results:
- Task: Object Detection
Dataset: COCO minival
Metrics:
box AP: 42.6
- Task: Instance Segmentation
Dataset: COCO minival
Metrics:
mask AP: 36.7
Weights: https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl
- Name: Mask R-CNN (R50-C4, 3x)
In Collection: Mask R-CNN
Metadata:
inference time (s/im): 0.111
train time (s/iter): 0.575
Training Memory (GB): 5.2
Results:
- Task: Object Detection
Dataset: COCO minival
Metrics:
box AP: 39.8
- Task: Instance Segmentation
Dataset: COCO minival
Metrics:
mask AP: 34.4
Weights: https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl
In this example we have two variants of the Mask R-CNN
model, one with a ResNet-50 backbone and one with a ResNet-101 backbone. These models belong together as variants of the Mask R-CNN, so we link them via a Mask R-CNN
model collection.