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vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
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vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
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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")