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- _base_ = [
- '../_base_/models/retinanet_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- # data
- train_dataloader = dict(batch_size=8)
- # model
- model = dict(
- backbone=dict(
- depth=18,
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
- neck=dict(in_channels=[64, 128, 256, 512]))
- # Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
- optim_wrapper = dict(
- optimizer=dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001))
- # TODO: support auto scaling lr
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (1 GPUs) x (8 samples per GPU)
- # auto_scale_lr = dict(base_batch_size=8)
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