<gh_stars>10-100
dataset_type = "COCODataset"

data_root = "data/coco/"

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)

train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Resize",
        img_scale=[(1333, 480), (1333, 960)],
        multiscale_mode="range",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(
        type="Normalize",
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True,
    ),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]

test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(
                type="Normalize",
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="DefaultFormatBundle"),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type="CocoDataset",
        ann_file="data/coco/train.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(type="LoadAnnotations", with_bbox=True),
            dict(
                type="Resize",
                img_scale=[(1333, 480), (1333, 960)],
                multiscale_mode="range",
                keep_ratio=True,
            ),
            dict(type="RandomFlip", flip_ratio=0.5),
            dict(
                type="Normalize",
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="DefaultFormatBundle"),
            dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
    val=dict(
        type="CocoDataset",
        ann_file="data/coco/test.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="DefaultFormatBundle"),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
    test=dict(
        type="CocoDataset",
        ann_file="data/coco/test.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="DefaultFormatBundle"),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
)

evaluation = dict(interval=1, metric="bbox")

optimizer = dict(
    type="SGD",
    lr=0.01,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0),
)

optimizer_config = dict(grad_clip=None)

lr_config = dict(
    policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.1, step=[16, 22]
)

runner = dict(type="EpochBasedRunner", max_epochs=24)

checkpoint_config = dict(interval=1)

log_config = dict(
    interval=50, hooks=[dict(type="TensorboardLoggerHook"), dict(type="TextLoggerHook")]
)

custom_hooks = [dict(type="NumClassCheckHook")]

dist_params = dict(backend="nccl")

log_level = "INFO"

load_from = None

resume_from = None

workflow = [("train", 1)]

model = dict(
    type="VFNet",
    backbone=dict(
        type="ResNet",
        depth=101,
        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://resnet101"),
        dcn=dict(type="DCNv2", deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True),
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",
        num_outs=5,
        relu_before_extra_convs=True,
    ),
    bbox_head=dict(
        type="VFNetHead",
        num_classes=5,
        in_channels=256,
        stacked_convs=3,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        center_sampling=False,
        dcn_on_last_conv=True,
        use_atss=True,
        use_vfl=True,
        loss_cls=dict(
            type="VarifocalLoss",
            use_sigmoid=True,
            alpha=0.75,
            gamma=2.0,
            iou_weighted=True,
            loss_weight=1.0,
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=1.5),
        loss_bbox_refine=dict(type="GIoULoss", loss_weight=2.0),
    ),
    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,
    ),
)

classes = ("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask")

dataset_type = "COCODataset"

data_root = "data/coco/"

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)

train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="Resize",
        img_scale=[(1333, 480), (1333, 960)],
        multiscale_mode="range",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(
        type="Normalize",
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True,
    ),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]

test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(
                type="Normalize",
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="DefaultFormatBundle"),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type="CocoDataset",
        ann_file="data/coco/train.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(type="LoadAnnotations", with_bbox=True),
            dict(
                type="Resize",
                img_scale=[(1333, 480), (1333, 960)],
                multiscale_mode="range",
                keep_ratio=True,
            ),
            dict(type="RandomFlip", flip_ratio=0.5),
            dict(
                type="Normalize",
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True,
            ),
            dict(type="Pad", size_divisor=32),
            dict(type="DefaultFormatBundle"),
            dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
    val=dict(
        type="CocoDataset",
        ann_file="data/coco/test.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="DefaultFormatBundle"),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
    test=dict(
        type="CocoDataset",
        ann_file="data/coco/test.json",
        img_prefix="data/images/",
        pipeline=[
            dict(type="LoadImageFromFile"),
            dict(
                type="MultiScaleFlipAug",
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type="Resize", keep_ratio=True),
                    dict(type="RandomFlip"),
                    dict(
                        type="Normalize",
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True,
                    ),
                    dict(type="Pad", size_divisor=32),
                    dict(type="DefaultFormatBundle"),
                    dict(type="Collect", keys=["img"]),
                ],
            ),
        ],
        classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
    ),
)

evaluation = dict(interval=1, metric="bbox")

optimizer = dict(
    type="SGD",
    lr=0.01,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0),
)

optimizer_config = dict(grad_clip=None)

lr_config = dict(
    policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.1, step=[16, 22]
)

runner = dict(type="EpochBasedRunner", max_epochs=24)

checkpoint_config = dict(interval=1)

log_config = dict(
    interval=50, hooks=[dict(type="TensorboardLoggerHook"), dict(type="TextLoggerHook")]
)

custom_hooks = [dict(type="NumClassCheckHook")]

dist_params = dict(backend="nccl")

log_level = "INFO"

load_from = None

resume_from = None

workflow = [("train", 1)]

model = dict(
    type="VFNet",
    backbone=dict(
        type="ResNet",
        depth=101,
        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://resnet101"),
        dcn=dict(type="DCNv2", deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, True, True, True),
    ),
    neck=dict(
        type="FPN",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",
        num_outs=5,
        relu_before_extra_convs=True,
    ),
    bbox_head=dict(
        type="VFNetHead",
        num_classes=5,
        in_channels=256,
        stacked_convs=3,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        center_sampling=False,
        dcn_on_last_conv=True,
        use_atss=True,
        use_vfl=True,
        loss_cls=dict(
            type="VarifocalLoss",
            use_sigmoid=True,
            alpha=0.75,
            gamma=2.0,
            iou_weighted=True,
            loss_weight=1.0,
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=1.5),
        loss_bbox_refine=dict(type="GIoULoss", loss_weight=2.0),
    ),
    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,
    ),
)

classes = ("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask")