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Wenwei Zhang authored
* Add RegNet, docs/refactor TBD * Support RegNet * Add RegNets * Modify docs and refactor code * fix pre-train bug * Performance ready * Use public model weights * Performance & settings done * Fix unittest bug * Try another way of test config * Add unittest and refactor * Reformat * Finish refactor regnet * merge tests * fix conflicts * Fix resnext CI bug * rename parameter * Add ref link & clean _delete_ * tweaks of doc * try resnext doctest * add expansion in block * remove doctest of resnext
Wenwei Zhang authored* Add RegNet, docs/refactor TBD * Support RegNet * Add RegNets * Modify docs and refactor code * fix pre-train bug * Performance ready * Use public model weights * Performance & settings done * Fix unittest bug * Try another way of test config * Add unittest and refactor * Reformat * Finish refactor regnet * merge tests * fix conflicts * Fix resnext CI bug * rename parameter * Add ref link & clean _delete_ * tweaks of doc * try resnext doctest * add expansion in block * remove doctest of resnext
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retinanet_r50_regnetx-800MF_fpn_1x_coco.py 471 B
_base_ = './retinanet_r50_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_800mf',
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[64, 128, 288, 672],
out_channels=256,
num_outs=5))