mask-rcnn mmdetection config

mask_rcnn_r101_rpn_handdatafull.py

# Place file config in a folder in {MMDETECTION_ROOT}/configs
_base_ = [
    '../_base_/models/mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# Use r101 model
model = dict(
    pretrained='torchvision://resnet101',
    backbone=dict(depth=101),
    roi_head=dict(
        bbox_head=dict(num_classes=2),
        mask_head=dict(num_classes=2)))

classes = ('lefthand', 'righthand')
data_root = '/content/drive/My Drive/Data/HandData/egohands_mask_rcnn/'
data = dict(
    train=dict(
        ann_file=data_root + 'annotations/train_handdatafull.json',
        img_prefix=data_root + 'train/',
        classes=classes
        ),
    val=dict(
        ann_file=data_root + 'annotations/val_handdatafull.json',
        img_prefix=data_root + 'val/',
        classes=classes
        ),
    test=dict(
        ann_file=data_root + 'annotations/test_handdatafull.json',
        img_prefix=data_root + 'test/',
        classes=classes
        ))


# in case you want to use mask r101 pretrain
load_from = 'http://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth'  # noqa