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- _base_ = './rtmdet-ins_s_8xb32-300e_coco.py'
- checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
- model = dict(
- backbone=dict(
- deepen_factor=0.167,
- widen_factor=0.375,
- init_cfg=dict(
- type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
- neck=dict(in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1),
- bbox_head=dict(in_channels=96, feat_channels=96))
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(
- type='LoadAnnotations',
- with_bbox=True,
- with_mask=True,
- poly2mask=False),
- dict(
- type='CachedMosaic',
- img_scale=(640, 640),
- pad_val=114.0,
- max_cached_images=20,
- random_pop=False),
- dict(
- type='RandomResize',
- scale=(1280, 1280),
- ratio_range=(0.5, 2.0),
- keep_ratio=True),
- dict(type='RandomCrop', crop_size=(640, 640)),
- dict(type='YOLOXHSVRandomAug'),
- dict(type='RandomFlip', prob=0.5),
- dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
- dict(
- type='CachedMixUp',
- img_scale=(640, 640),
- ratio_range=(1.0, 1.0),
- max_cached_images=10,
- random_pop=False,
- pad_val=(114, 114, 114),
- prob=0.5),
- dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1)),
- dict(type='PackDetInputs')
- ]
- train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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