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- _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
- model = dict(
- backbone=dict(
- embed_dims=64,
- num_layers=[3, 8, 27, 3],
- init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
- 'releases/download/v2/pvt_v2_b4.pth')),
- neck=dict(in_channels=[64, 128, 320, 512]))
- # optimizer
- optim_wrapper = dict(
- optimizer=dict(
- _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001))
- # dataset settings
- train_dataloader = dict(batch_size=1, num_workers=1)
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (1 samples per GPU)
- auto_scale_lr = dict(base_batch_size=8)
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