def METHOD_NAME(self):
    # a basic R50 Mask R-CNN
    MMDetDetector(
        detector=dict(
            type="MaskRCNN",
            backbone=dict(
                type="ResNet",
                depth=50,
                num_stages=4,
                out_indices=(0, 1, 2, 3),
                frozen_stages=1,
                norm_cfg=dict(type="BN", requires_grad=True),
                norm_eval=True,
                style="pytorch",
                # skip pretrained model for tests
                # init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'))
            ),
            neck=dict(
                type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5
            ),
            rpn_head=dict(
                type="RPNHead",
                in_channels=256,
                feat_channels=256,
                anchor_generator=dict(
                    type="AnchorGenerator",
                    scales=[8],
                    ratios=[0.5, 1.0, 2.0],
                    strides=[4, 8, 16, 32, 64],
                ),
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder",
                    target_means=[0.0, 0.0, 0.0, 0.0],
                    target_stds=[1.0, 1.0, 1.0, 1.0],
                ),
                loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
                loss_bbox=dict(type="L1Loss", loss_weight=1.0),
            ),
            roi_head=dict(
                type="StandardRoIHead",
                bbox_roi_extractor=dict(
                    type="SingleRoIExtractor",
                    roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
                    out_channels=256,
                    featmap_strides=[4, 8, 16, 32],
                ),
                bbox_head=dict(
                    type="Shared2FCBBoxHead",
                    in_channels=256,
                    fc_out_channels=1024,
                    roi_feat_size=7,
                    num_classes=80,
                    bbox_coder=dict(
                        type="DeltaXYWHBBoxCoder",
                        target_means=[0.0, 0.0, 0.0, 0.0],
                        target_stds=[0.1, 0.1, 0.2, 0.2],
                    ),
                    reg_class_agnostic=False,
                    loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
                    loss_bbox=dict(type="L1Loss", loss_weight=1.0),
                ),
                mask_roi_extractor=dict(
                    type="SingleRoIExtractor",
                    roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0),
                    out_channels=256,
                    featmap_strides=[4, 8, 16, 32],
                ),
                mask_head=dict(
                    type="FCNMaskHead",
                    num_convs=4,
                    in_channels=256,
                    conv_out_channels=256,
                    num_classes=80,
                    loss_mask=dict(type="CrossEntropyLoss", use_mask=True, loss_weight=1.0),
                ),
            ),
            # model training and testing settings
            train_cfg=dict(
                rpn=dict(
                    assigner=dict(
                        type="MaxIoUAssigner",
                        pos_iou_thr=0.7,
                        neg_iou_thr=0.3,
                        min_pos_iou=0.3,
                        match_low_quality=True,
                        ignore_iof_thr=-1,
                    ),
                    sampler=dict(
                        type="RandomSampler",
                        num=256,
                        pos_fraction=0.5,
                        neg_pos_ub=-1,
                        add_gt_as_proposals=False,
                    ),
                    allowed_border=-1,
                    pos_weight=-1,
                    debug=False,
                ),
                rpn_proposal=dict(
                    nms_pre=2000,
                    max_per_img=1000,
                    nms=dict(type="nms", iou_threshold=0.7),
                    min_bbox_size=0,
                ),
                rcnn=dict(
                    assigner=dict(
                        type="MaxIoUAssigner",
                        pos_iou_thr=0.5,
                        neg_iou_thr=0.5,
                        min_pos_iou=0.5,
                        match_low_quality=True,
                        ignore_iof_thr=-1,
                    ),
                    sampler=dict(
                        type="RandomSampler",
                        num=512,
                        pos_fraction=0.25,
                        neg_pos_ub=-1,
                        add_gt_as_proposals=True,
                    ),
                    mask_size=28,
                    pos_weight=-1,
                    debug=False,
                ),
            ),
            test_cfg=dict(
                rpn=dict(
                    nms_pre=1000,
                    max_per_img=1000,
                    nms=dict(type="nms", iou_threshold=0.7),
                    min_bbox_size=0,
                ),
                rcnn=dict(
                    score_thr=0.05,
                    nms=dict(type="nms", iou_threshold=0.5),
                    max_per_img=100,
                    mask_thr_binary=0.5,
                ),
            ),
        ),
        pixel_mean=[1, 2, 3],
        pixel_std=[1, 2, 3],
    )