repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/htc_r50_sac_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True)))
| 245 | 26.333333 | 50 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/detectors_htc_r50_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_ste... | 916 | 30.62069 | 57 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... | 851 | 28.37931 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/htc_r50_rfp_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dic... | 714 | 27.6 | 57 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_c... | 1,904 | 32.421053 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| 128 | 42 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
bbox_head=dict(
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=False,
... | 1,780 | 31.381818 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1... | 1,331 | 32.3 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval... | 1,966 | 31.245902 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
| 224 | 27.125 | 66 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)... | 1,550 | 31.3125 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 3,281 | 29.672897 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py | # TODO: Remove this config after benchmarking all related configs
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
data = dict(samples_per_gpu=4, workers_per_gpu=4)
| 166 | 32.4 | 65 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', ... | 1,971 | 31.327869 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'nor... | 1,640 | 31.176471 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/common/mstrain_3x_coco_instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
... | 2,466 | 31.038961 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/common/mstrain-poly_3x_coco_instance.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
... | 2,516 | 30.074074 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/common/mstrain_3x_coco.py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
... | 2,428 | 30.545455 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 200 | 27.714286 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train... | 1,933 | 30.193548 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py | _base_ = './panoptic_fpn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 28.857143 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
semantic_head=dict(
type='PanopticFPNHead',
num_classes=54,
in_channels=256,
... | 997 | 29.242424 | 73 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r101_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 194 | 26.857143 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 440 | 26.5625 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 169 | 41.5 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r50_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 142 | 27.6 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/scnet/scnet_r50_fpn_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='SCNet',
roi_head=dict(
_delete_=True,
type='SCNetRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=... | 5,020 | 35.649635 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py | _base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
a... | 2,750 | 34.269231 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... | 3,490 | 36.537634 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
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=False),
norm_eval=True,
style='caffe',
in... | 2,833 | 35.333333 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='FasterRCNN',
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
rp... | 1,385 | 34.538462 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 2,791 | 33.9 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='Le... | 1,238 | 34.4 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
type='RetinaHead',
anchor_generator=dict(
type='LegacyAnchorGenerator',
... | 617 | 33.333333 | 73 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py | _base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet50_caffe')))
# use caffe img_norm
img_norm_c... | 1,413 | 32.666667 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/legacy_1.x/ssd300_coco_v1.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGene... | 2,659 | 32.25 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256... | 515 | 29.352941 | 58 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
... | 509 | 29 | 52 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 417 | 26.866667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 150 | 29.2 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 417 | 26.866667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py | _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 220 | 26.625 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 147 | 23.666667 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
... | 1,710 | 33.918367 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/fast_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rg... | 1,944 | 35.698113 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=... | 482 | 36.153846 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,296 | 30.634146 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,181 | 30.105263 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py | _base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 154 | 24.833333 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
typ... | 461 | 37.5 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158 | 30.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='... | 457 | 37.166667 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py | _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py | _base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 2,333 | 31.873239 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
t... | 463 | 37.666667 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,291 | 30.512195 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type... | 459 | 40.818182 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cf... | 484 | 39.416667 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py | _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 158 | 30.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153 | 29.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
t... | 462 | 37.583333 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', c... | 443 | 39.363636 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 151 | 29.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pr... | 455 | 37 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 1,185 | 30.210526 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
img_norm_cfg = dict(
mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 64... | 1,337 | 32.45 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py | _base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 158 | 30.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py | _base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='o... | 431 | 38.272727 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py | _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init... | 487 | 36.538462 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py | _base_ = './htc_hrnetv2p_w40_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
| 146 | 28.4 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py | _base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 153 | 29.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py | _base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_ch... | 1,170 | 29.815789 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoi... | 436 | 38.727273 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py | _base_ = './htc_hrnetv2p_w32_20e_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
type='Pr... | 456 | 37.083333 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... | 575 | 32.882353 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
... | 575 | 32.882353 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_m_8x8_300e_coco.py | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
bbox_head=dict(in_channels=192, feat_channels=192),
)
| 266 | 28.666667 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_s_8x8_300e_coco.py | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
model = dict(
type='YOLOX',
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
n... | 4,236 | 28.423611 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_l_8x8_300e_coco.py | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.0, widen_factor=1.0),
neck=dict(
in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
bbox_head=dict(in_channels=256, feat_channels=256))
| 272 | 29.333333 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_x_8x8_300e_coco.py | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
neck=dict(
in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
bbox_head=dict(in_channels=320, feat_channels=320))
| 274 | 29.555556 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_nano_8x8_300e_coco.py | _base_ = './yolox_tiny_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
neck=dict(
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=True),
bbox_head=dict(in_channels=64, fea... | 356 | 28.75 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolox/yolox_tiny_8x8_300e_coco.py | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
# dataset settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], ... | 2,389 | 28.875 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
ty... | 3,305 | 34.934783 | 123 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py | _base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 318 | 44.571429 | 124 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... | 1,301 | 29.27907 | 123 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py | _base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# you need to set mode='dynamic' if you are using pytorch<=1.5.0
fp16 = dict(loss_scale=dict(init_scale=512))
| 169 | 41.5 | 64 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True... | 464 | 26.352941 | 62 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
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',
... | 546 | 33.1875 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 3,240 | 29.009259 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
n... | 602 | 30.736842 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
n... | 585 | 31.555556 | 76 | py |
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