# model settings
model = dict(
    type="ATSS",
    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",
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",
        num_outs=5,
    ),
    bbox_head=dict(
        type="ATSSHead",
        num_classes=10,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type="AnchorGenerator",
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128],
        ),
        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],
        ),
        loss_cls=dict(
            type="FocalLoss",
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0,
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=2.0),
        loss_centerness=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
        ),
    ),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(type="ATSSAssigner", topk=9),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type="nms", iou_threshold=0.6),
        max_per_img=100,
    ),
)