repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/paa/paa_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 2,082 | 28.338028 | 73 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py | _base_ = './paa_r50_fpn_mstrain_3x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 121 | 39.666667 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/paa/paa_r101_fpn_1x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 113 | 37 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/yolact/yolact_r50_1x8_coco.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 5,065 | 30.465839 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/yolact/yolact_r101_1x8_coco.py | _base_ = './yolact_r50_1x8_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 114 | 27.75 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
... | 1,453 | 31.311111 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 161 | 31.4 | 56 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/detectors/detectors_cascade_rcnn_r50_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'),
sac=dict(type='SAC', use_def... | 1,053 | 30.939394 | 72 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(
norm_on_bbox=True,
... | 1,841 | 32.490909 | 74 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://detectron2/resnet50_caffe',
bbox_head=dict(
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=False,
center_sampling=True,
conv_bias=True,
loss_bbox=dict(type='GIoUL... | 1,697 | 31.653846 | 72 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://resnext101_64x4d',
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(t... | 1,915 | 30.933333 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron/resnet101_caffe',
backbone=dict(depth=101))
| 152 | 29.6 | 56 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://detectron/resnet101_caffe',
backbone=dict(depth=101))
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='L... | 1,478 | 31.866667 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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',
pretrained='open-mmlab://detectron/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 3,248 | 29.650943 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/scnet/scnet_r101_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 116 | 38 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', re... | 389 | 25 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
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),
... | 2,770 | 35.460526 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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',
pretrained='torchvision://resnet50',
rpn_head=dict(
type='RPNHead',
anchor_genera... | 1,327 | 33.947368 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,... | 2,753 | 33.425 | 79 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py | _base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
pretrained='open-mmlab://detectron/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb... | 1,334 | 34.131579 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', r... | 366 | 25.214286 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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', r... | 366 | 25.214286 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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 |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 148 | 28.8 | 57 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 150 | 29.2 | 57 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
... | 1,639 | 34.652174 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BO... | 2,282 | 31.614286 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='open-mmlab://res2net101_v1d_26w_4s',
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(t... | 401 | 25.8 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='torchvision://resnet101',
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),
n... | 486 | 31.466667 | 74 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 3,232 | 28.66055 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://res2net101_v1d_26w_4s',
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
no... | 539 | 30.764706 | 74 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 123 | 40.333333 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/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(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cf... | 534 | 30.470588 | 74 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_1x_coco.py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 115 | 37.666667 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_2x_coco.py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 201 | 39.4 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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='... | 396 | 25.466667 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cf... | 534 | 30.470588 | 74 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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='... | 396 | 25.466667 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 1,574 | 28.716981 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12,... | 973 | 33.785714 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochB... | 348 | 30.727273 | 69 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_8.0gf',
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 470 | 26.705882 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_1.6gf',
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 469 | 26.647059 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_12gf',
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 469 | 26.647059 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_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,
... | 469 | 26.647059 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_4.0gf',
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 470 | 26.705882 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.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(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regne... | 2,099 | 31.8125 | 73 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx... | 1,953 | 32.118644 | 73 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.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(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regne... | 1,869 | 31.807018 | 73 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-3.2GF_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'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_... | 1,964 | 32.87931 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_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'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_... | 2,210 | 32.5 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_6.4gf',
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 471 | 26.764706 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://resnest50',
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=T... | 1,909 | 29.31746 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://resnest50',
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_s... | 4,217 | 34.445378 | 79 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://resnest50',
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,... | 2,030 | 30.246154 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
pretrained='open-mmlab://resnest50',
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride... | 4,089 | 33.957265 | 79 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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', requ... | 363 | 25 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/fsaf/fsaf_r101_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 114 | 37.333333 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 128 | 31.25 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='py... | 329 | 24.384615 | 56 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch')... | 646 | 25.958333 | 72 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='GridRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1... | 4,277 | 31.409091 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
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,
... | 1,723 | 27.262295 | 56 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/faster_rcnn_r50_fpn.py | model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
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'... | 3,577 | 32.12963 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/cascade_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
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,
... | 6,287 | 33.933333 | 79 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/rpn_r50_caffe_c4.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type... | 1,725 | 29.280702 | 72 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
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,
... | 6,912 | 34.091371 | 79 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/fast_rcnn_r50_fpn.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
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,
... | 2,022 | 31.111111 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
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,
... | 4,016 | 32.198347 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indic... | 3,416 | 31.855769 | 77 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/rpn_r50_fpn.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
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,
styl... | 1,770 | 28.516667 | 72 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/ssd300.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 1,487 | 28.176471 | 60 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/faster_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2,... | 3,631 | 31.141593 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/_base_/models/mask_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, )... | 3,998 | 31.25 | 78 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 127 | 41.666667 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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(ty... | 376 | 25.928571 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 150 | 29.2 | 57 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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',... | 368 | 25.357143 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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',... | 368 | 25.357143 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fals... | 1,473 | 33.27907 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 160 | 31.2 | 55 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=Fals... | 1,329 | 34 | 75 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 160 | 31.2 | 55 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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',... | 368 | 25.357143 | 53 | py |
MDE-biological-vision-systems | MDE-biological-vision-systems-master/Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
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