#detectron2-serve-model .el-tabs.el-tabs-cards .el-radio { display: flex; align-items: start; /*margin-bottom: 10px;*/ margin-left: 0; white-space: normal; } #detectron2-serve-model .el-tabs.el-tabs-cards .el-radio__label div { color: #7f858e; font-size: 13px; } .beautiful-table { border-collapse: collapse; } .beautiful-table tr:nth-child(2n) { background-color: #f6f8fa; } .beautiful-table td, .beautiful-table th { border: 1px solid #dfe2e5; padding: 6px 13px; text-align: center; line-height: 20px; } #detectron2-serve-model .el-tabs.el-tabs-cards { border-radius: 4px; box-shadow: none; } #detectron2-serve-model .el-tabs.el-tabs-cards .el-tabs__header { background-color: #f6fafd; } #detectron2-serve-model .el-tabs.el-tabs-cards .el-tabs__nav { float: none; display: flex; justify-content: space-between; } #detectron2-serve-model .el-tabs.el-tabs-cards .el-tabs__item { flex: 1; margin-bottom: -3px; padding: 9px 16px 13px; height: auto; line-height: normal; border-radius: 4px; }

Custom weights

load custom weights from Team Files

Pretrained models

weights pretrained on popular datasets

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Detectron2 provides us Mask R-CNN Instance Segmentation
baselines based on 3 different backbone combinations

FPN: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction, respectively. It obtains the best speed/accuracy tradeoff, but the other two are still useful for research.
C4: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
DC5 (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads for mask and box prediction, respectively. This is used by the Deformable ConvNet paper.

more about models
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framework:mvd

Train Mouse Action Recognition