yolov7_tiny_syncbn_fast_8x16b-300e_coco.py

_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'

# ========================modified parameters========================

# -----model related-----
# Data augmentation
max_translate_ratio = 0.1  # YOLOv5RandomAffine
scaling_ratio_range = (0.5, 1.6)  # YOLOv5RandomAffine
mixup_prob = 0.05  # YOLOv5MixUp
randchoice_mosaic_prob = [0.8, 0.2]
mixup_alpha = 8.0  # YOLOv5MixUp
mixup_beta = 8.0  # YOLOv5MixUp

# -----train val related-----
loss_cls_weight = 0.5
loss_obj_weight = 1.0

lr_factor = 0.01  # Learning rate scaling factor
# ===============================Unmodified in most cases====================
num_classes = _base_.num_classes
num_det_layers = _base_.num_det_layers
img_scale = _base_.img_scale
pre_transform = _base_.pre_transform
model = dict(
    backbone=dict(
        arch='Tiny', act_cfg=dict(type='LeakyReLU', negative_slope=0.1)),
    neck=dict(
        is_tiny_version=True,
        in_channels=[128, 256, 512],
        out_channels=[64, 128, 256],
        block_cfg=dict(
            _delete_=True, type='TinyDownSampleBlock', middle_ratio=0.25),
        act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
        use_repconv_outs=False),
    bbox_head=dict(
        head_module=dict(in_channels=[128, 256, 512]),
        loss_cls=dict(loss_weight=loss_cls_weight *
                      (num_classes / 80 * 3 / num_det_layers)),
        loss_obj=dict(loss_weight=loss_obj_weight *
                      ((img_scale[0] / 640)**2 * 3 / num_det_layers))))

mosiac4_pipeline = [
    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,
        max_translate_ratio=max_translate_ratio,  # change
        scaling_ratio_range=scaling_ratio_range,  # change
        # img_scale is (width, height)
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114)),
]

mosiac9_pipeline = [
    dict(
        type='Mosaic9',
        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,
        max_translate_ratio=max_translate_ratio,  # change
        scaling_ratio_range=scaling_ratio_range,  # change
        border=(-img_scale[0] // 2, -img_scale[1] // 2),
        border_val=(114, 114, 114)),
]

randchoice_mosaic_pipeline = dict(
    type='RandomChoice',
    transforms=[mosiac4_pipeline, mosiac9_pipeline],
    prob=randchoice_mosaic_prob)

train_pipeline = [
    *pre_transform,
    randchoice_mosaic_pipeline,
    dict(
        type='YOLOv5MixUp',
        alpha=mixup_alpha,
        beta=mixup_beta,
        prob=mixup_prob,  # change
        pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
    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_dataloader = dict(dataset=dict(pipeline=train_pipeline))
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))