|
- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- # model settings
- model = dict(
- type='FOVEA',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- num_outs=5,
- add_extra_convs='on_input'),
- bbox_head=dict(
- type='FoveaHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- strides=[8, 16, 32, 64, 128],
- base_edge_list=[16, 32, 64, 128, 256],
- scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
- sigma=0.4,
- with_deform=False,
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=1.50,
- alpha=0.4,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
- # training and testing settings
- train_cfg=dict(),
- test_cfg=dict(
- nms_pre=1000,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100))
- data = dict(samples_per_gpu=4, workers_per_gpu=4)
- # optimizer
- optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|