yolov10_l_syncbn_fast_8xb16-500e_coco.py

_base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']

# ========================Frequently modified parameters======================
# -----data related-----
work_dir = "/****/yolov10/exp/coco_align"
data_root = '/****/Detection/dataset/'  # Root path of data

# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = 'train2017/'  # Prefix of train image path

# Path of val annotation file
val_ann_file = 'annotations/instances_val2017.json'
val_data_prefix = 'val2017/'  # Prefix of val image path

num_classes = 80  # Number of classes for classification
# Batch size of a single GPU during training
train_batch_size_per_gpu = 16
# Worker to pre-fetch data for each single GPU during training
train_num_workers = 12
# persistent_workers must be False if num_workers is 0
persistent_workers = True
log_interval = 5

# -----train val related-----
# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs
base_lr = 0.01
max_epochs = 500  # Maximum training epochs
# Disable mosaic augmentation for final 10 epochs (stage 2)
close_mosaic_epochs = 10

model_test_cfg = dict(
    # The config of multi-label for multi-class prediction.
    multi_label=True,
    # The number of boxes before NMS
    nms_pre=30000,
    one2one_withnms=False,
    one2many_withnms=True,
    score_thr=0.001,  # Threshold to filter out boxes.
    max_per_img=300)  # Max number of detections of each image

# ========================Possible modified parameters========================
# -----data related-----
img_scale = (640, 640)  # width, height
# Dataset type, this will be used to define the dataset
dataset_type = 'YOLOv5CocoDataset'
# Batch size of a single GPU during validation
val_batch_size_per_gpu = 16
# Worker to pre-fetch data for each single GPU during validation
val_num_workers = 8

# Config of batch shapes. Only on val.
# We tested YOLOv8-m will get 0.02 higher than not using it.
batch_shapes_cfg = None
# You can turn on `batch_shapes_cfg` by uncommenting the following lines.
# batch_shapes_cfg = dict(
#     type='BatchShapePolicy',
#     batch_size=val_batch_size_per_gpu,
#     img_size=img_scale[0],
#     # The image scale of padding should be divided by pad_size_divisor
#     size_divisor=32,
#     # Additional paddings for pixel scale
#     extra_pad_ratio=0.5)

# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 1
# The scaling factor that controls the width of the network structure
widen_factor = 1
# Strides of multi-scale prior box
strides = [8, 16, 32]
# The output channel of the last stage
last_stage_out_channels = 512
num_det_layers = 3  # The number of model output scales
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)  # Normalization config

# -----train val related-----
affine_scale = 0.5  # YOLOv5RandomAffine scaling ratio
# YOLOv5RandomAffine aspect ratio of width and height thres to filter bboxes
max_aspect_ratio = 100
one2many_tal_topk = 10  # Number of bbox selected in each level
one2many_tal_alpha = 0.5  # A Hyper-parameter related to alignment_metrics
one2many_tal_beta = 6.0  # A Hyper-parameter related to alignment_metrics

one2one_tal_topk = 1  # Number of bbox selected in each level
one2one_tal_alpha = 0.5  # A Hyper-parameter related to alignment_metrics
one2one_tal_beta = 6.0  # A Hyper-parameter related to alignment_metrics

infer_type = "one2one"

# TODO: Automatically scale loss_weight based on number of detection layers
loss_cls_weight = 0.5
loss_bbox_weight = 7.5
# Since the dfloss is implemented differently in the official
# and mmdet, we're going to divide loss_weight by 4.
loss_dfl_weight = 1.5 / 4
lr_factor = 0.01  # Learning rate scaling factor
weight_decay = 0.0005
# Save model checkpoint and validation intervals in stage 1
save_epoch_intervals = 10
# validation intervals in stage 2
val_interval_stage2 = 1
# The maximum checkpoints to keep.
max_keep_ckpts = 2
# Single-scale training is recommended to
# be turned on, which can speed up training.
env_cfg = dict(cudnn_benchmark=True)

# ===============================Unmodified in most cases====================
model = dict(
    type='YOLODetector',
    data_preprocessor=dict(
        type='YOLOv5DetDataPreprocessor',
        mean=[0., 0., 0.],
        std=[255., 255., 255.],
        bgr_to_rgb=True),
    backbone=dict(
        type='YOLOv10Backbone',
        arch='P5',
        last_stage_out_channels=last_stage_out_channels,
        use_c2fcib=True, # Set to False only in YOLOv10 Nano
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)),
    neck=dict(
        type='YOLOv10PAFPN',
        deepen_factor=deepen_factor,
        widen_factor=widen_factor,
        in_channels=[256, 512, last_stage_out_channels],
        out_channels=[256, 512, last_stage_out_channels],
        num_csp_blocks=3,
        norm_cfg=norm_cfg,
        act_cfg=dict(type='SiLU', inplace=True)),
    bbox_head=dict(
        type='YOLOv10Head',
        head_module=dict(
            type='YOLOv10HeadModule',
            num_classes=num_classes,
            in_channels=[256, 512, last_stage_out_channels],
            widen_factor=widen_factor,
            reg_max=16,
            norm_cfg=norm_cfg,
            act_cfg=dict(type='SiLU', inplace=True),
            featmap_strides=strides),
        infer_type=infer_type,
        prior_generator=dict(
            type='mmdet.MlvlPointGenerator', offset=0.5, strides=strides),
        bbox_coder=dict(type='DistancePointBBoxCoder'),
        # scaled based on number of detection layers
        loss_cls=dict(
            type='mmdet.CrossEntropyLoss',
            use_sigmoid=True,
            reduction='none',
            loss_weight=loss_cls_weight),
        loss_bbox=dict(
            type='IoULoss',
            iou_mode='ciou',
            bbox_format='xyxy',
            reduction='sum',
            loss_weight=loss_bbox_weight,
            return_iou=False),
        loss_dfl=dict(
            type='mmdet.DistributionFocalLoss',
            reduction='mean',
            loss_weight=loss_dfl_weight)),
    train_cfg=dict(
        one2many_assigner=dict(
            type='BatchTaskAlignedAssigner',
            num_classes=num_classes,
            use_ciou=True,
            topk=one2many_tal_topk,
            alpha=one2many_tal_alpha,
            beta=one2many_tal_beta,
            eps=1e-9),
        one2one_assigner=dict(
            type='BatchTaskAlignedAssigner',
            num_classes=num_classes,
            use_ciou=True,
            topk=one2one_tal_topk,
            alpha=one2one_tal_alpha,
            beta=one2one_tal_beta,
            eps=1e-9)
    ),
    test_cfg=model_test_cfg)

albu_train_transforms = [
    dict(type='Blur', p=0.01),
    dict(type='MedianBlur', p=0.01),
    dict(type='ToGray', p=0.01),
    dict(type='CLAHE', p=0.01)
]

pre_transform = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='LoadAnnotations', with_bbox=True)
]

last_transform = [
    dict(
        type='mmdet.Albu',
        transforms=albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        }),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_pipeline = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        max_aspect_ratio=max_aspect_ratio,
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114)),
    *last_transform
]

train_pipeline_stage2 = [
    *pre_transform,
    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
    dict(
        type='LetterResize',
        scale=img_scale,
        allow_scale_up=True,
        pad_val=dict(img=114.0)),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
        max_aspect_ratio=max_aspect_ratio,
        border_val=(114, 114, 114)), *last_transform
]

train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    collate_fn=dict(type='yolov5_collate'),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file=train_ann_file,
        data_prefix=dict(img=train_data_prefix),
        filter_cfg=dict(filter_empty_gt=False, min_size=32),
        pipeline=train_pipeline))

test_pipeline = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
    dict(
        type='LetterResize',
        scale=img_scale,
        allow_scale_up=False,
        pad_val=dict(img=114)),
    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'pad_param'))
]

val_dataloader = dict(
    batch_size=val_batch_size_per_gpu,
    num_workers=val_num_workers,
    persistent_workers=persistent_workers,
    pin_memory=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        test_mode=True,
        data_prefix=dict(img=val_data_prefix),
        ann_file=val_ann_file,
        pipeline=test_pipeline,
        batch_shapes_cfg=batch_shapes_cfg))

test_dataloader = val_dataloader

param_scheduler = None
optim_wrapper = dict(
    type='OptimWrapper',
    clip_grad=dict(max_norm=10.0),
    optimizer=dict(
        type='SGD',
        lr=base_lr,
        momentum=0.937,
        weight_decay=weight_decay,
        nesterov=True,
        batch_size_per_gpu=train_batch_size_per_gpu),
    constructor='YOLOv5OptimizerConstructor')

default_hooks = dict(
    param_scheduler=dict(
        type='YOLOv5ParamSchedulerHook',
        scheduler_type='linear',
        lr_factor=lr_factor,
        max_epochs=max_epochs),
    checkpoint=dict(
        type='CheckpointHook',
        interval=save_epoch_intervals,
        save_best='auto',
        max_keep_ckpts=max_keep_ckpts))

custom_hooks = [
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0001,
        update_buffers=True,
        strict_load=False,
        priority=49),
    dict(
        type='mmdet.PipelineSwitchHook',
        switch_epoch=max_epochs - close_mosaic_epochs,
        switch_pipeline=train_pipeline_stage2)
]

val_evaluator = dict(
    type='mmdet.CocoMetric',
    proposal_nums=(100, 1, 10),
    ann_file=data_root + val_ann_file,
    metric='bbox')
test_evaluator = val_evaluator

train_cfg = dict(
    type='EpochBasedTrainLoop',
    max_epochs=max_epochs,
    val_interval=save_epoch_intervals,
    dynamic_intervals=[((max_epochs - close_mosaic_epochs),
                        val_interval_stage2)])

val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')