repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
mmdetection | configs/fast_rcnn/fast-rcnn_r101_fpn_2x_coco.py | .py | _base_ = './fast-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/fast-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadProposals', num_max_proposals=200... | 40 | 1,353 |
mmdetection | configs/fast_rcnn/fast-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/fast_rcnn/fast-rcnn_r50_fpn_2x_coco.py | .py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
train_cfg = dict(max_epochs=24)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
milestones=[16, 22],
gamm... | 15 | 329 |
mmdetection | configs/fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False)... | 17 | 490 |
mmdetection | configs/fast_rcnn/fast-rcnn_r101-caffe_fpn_1x_coco.py | .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')))
| 8 | 222 |
mmdetection | configs/htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py | .py | _base_ = './htc_x101-32x4d_fpn_16xb1-20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
groups=64,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 8 | 226 |
mmdetection | configs/htc/htc_r50_fpn_20e_coco.py | .py | _base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 19],
... | 17 | 373 |
mmdetection | configs/htc/htc_x101-32x4d_fpn_16xb1-20e_coco.py | .py | _base_ = './htc_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),
norm_eval=True,
... | 33 | 828 |
mmdetection | configs/htc/htc_x101-64x4d-dconv-c3-c5_fpn_ms-400-1400-16xb1-20e_coco.py | .py | _base_ = './htc_x101-64x4d_fpn_16xb1-20e_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotatio... | 21 | 616 |
mmdetection | configs/htc/htc_r101_fpn_20e_coco.py | .py | _base_ = './htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 192 |
mmdetection | configs/htc/htc_r50_fpn_1x_coco.py | .py | _base_ = './htc-without-semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... | 34 | 1,195 |
mmdetection | configs/htc/htc-without-semantic_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12... | 224 | 7,857 |
mmdetection | configs/pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=20... | 31 | 926 |
mmdetection | configs/pisa/mask-rcnn_r50_fpn_pisa_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
... | 31 | 922 |
mmdetection | configs/pisa/ssd300_pisa_coco.py | .py | _base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| 8 | 224 |
mmdetection | configs/pisa/faster-rcnn_x101-32x4d_fpn_pisa_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per... | 31 | 933 |
mmdetection | configs/pisa/ssd512_pisa_coco.py | .py | _base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| 8 | 224 |
mmdetection | configs/pisa/retinanet_x101-32x4d_fpn_pisa_1x_coco.py | .py | _base_ = '../retinanet/retinanet_x101-32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 8 | 272 |
mmdetection | configs/pisa/mask-rcnn_x101-32x4d_fpn_pisa_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img... | 31 | 929 |
mmdetection | configs/pisa/retinanet-r50_fpn_pisa_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 8 | 265 |
mmdetection | configs/timm_example/retinanet_timm-tv-resnet50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | 23 | 674 |
mmdetection | configs/timm_example/retinanet_timm-efficientnet-b1_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | 24 | 687 |
mmdetection | configs/paa/paa_r50_fpn_ms-3x_coco.py | .py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 36
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[28, 34],
ga... | 30 | 777 |
mmdetection | configs/paa/paa_r101_fpn_1x_coco.py | .py | _base_ = './paa_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 191 |
mmdetection | configs/paa/paa_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='PAA',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 81 | 2,384 |
mmdetection | configs/paa/paa_r101_fpn_2x_coco.py | .py | _base_ = './paa_r101_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
g... | 19 | 400 |
mmdetection | configs/paa/paa_r50_fpn_1.5x_coco.py | .py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 18
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[12, 16],
ga... | 19 | 401 |
mmdetection | configs/paa/paa_r101_fpn_ms-3x_coco.py | .py | _base_ = './paa_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 194 |
mmdetection | configs/paa/paa_r50_fpn_2x_coco.py | .py | _base_ = './paa_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
ga... | 19 | 399 |
mmdetection | configs/dino/dino-5scale_swin-l_8xb2-36e_coco.py | .py | _base_ = './dino-5scale_swin-l_8xb2-12e_coco.py'
max_epochs = 36
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[27, 33],
gamma=0... | 14 | 326 |
mmdetection | configs/dino/dino-4scale_r50_8xb2-24e_coco.py | .py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[20],
gamma=0.1)
]
| 14 | 319 |
mmdetection | configs/dino/dino-4scale_r50_8xb2-12e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DINO',
num_queries=900, # num_matching_queries
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... | 164 | 5,783 |
mmdetection | configs/dino/dino-4scale_r50_improved_8xb2-12e_coco.py | .py | _base_ = ['dino-4scale_r50_8xb2-12e_coco.py']
# from deformable detr hyper
model = dict(
backbone=dict(frozen_stages=-1),
bbox_head=dict(loss_cls=dict(loss_weight=2.0)),
positional_encoding=dict(offset=-0.5, temperature=10000),
dn_cfg=dict(group_cfg=dict(num_dn_queries=300)))
# optimizer
optim_wrapper... | 19 | 562 |
mmdetection | configs/dino/dino-4scale_r50_8xb2-36e_coco.py | .py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
max_epochs = 36
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[30],
gamma=0.1)
]
| 14 | 319 |
mmdetection | configs/dino/dino-5scale_swin-l_8xb2-12e_coco.py | .py | _base_ = './dino-4scale_r50_8xb2-12e_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 5
model = dict(
num_feature_levels=num_levels,
backbone=dict(
_delete_=True,
type='SwinTransformer',
... | 31 | 1,119 |
mmdetection | configs/reppoints/reppoints-bbox_r50-center_fpn-gn_head-gn-grid_1x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True))
| 3 | 138 |
mmdetection | configs/reppoints/reppoints-moment_x101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_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),
... | 17 | 560 |
mmdetection | configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
| 4 | 189 |
mmdetection | configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 75 | 2,282 |
mmdetection | configs/reppoints/reppoints-bbox_r50_fpn-gn_head-gn-grid_1x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(
bbox_head=dict(transform_method='minmax', use_grid_points=True),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(
_delete_=True,
type='MaxIoUAssigner',
... | 14 | 450 |
mmdetection | configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | 18 | 429 |
mmdetection | configs/reppoints/reppoints-minmax_r50_fpn-gn_head-gn_1x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
| 3 | 116 |
mmdetection | configs/reppoints/reppoints-partial-minmax_r50_fpn-gn_head-gn_1x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| 3 | 124 |
mmdetection | configs/reppoints/reppoints-moment_r101_fpn-gn_head-gn_2x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 215 |
mmdetection | configs/reppoints/reppoints-moment_r101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py | .py | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | 9 | 338 |
mmdetection | configs/openimages/ssd300_32xb8-36e_openimages.py | .py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... | 89 | 3,014 |
mmdetection | configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py | .py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 6 | 195 |
mmdetection | configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages.py | .py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| 6 | 185 |
mmdetection | configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optim_wrapper = dict(
type='OptimWrappe... | 36 | 941 |
mmdetection | configs/openimages/retinanet_r50_fpn_32xb2-1x_openimages.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=601))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor... | 36 | 905 |
mmdetection | configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py | .py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file=... | 40 | 1,712 |
mmdetection | configs/dab_detr/dab-detr_r50_8xb2-50e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DABDETR',
num_queries=300,
with_random_refpoints=False,
num_patterns=0,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395,... | 160 | 5,406 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco.py | .py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, T... | 12 | 390 |
mmdetection | configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, Tru... | 12 | 376 |
mmdetection | configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | 12 | 369 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True,... | 12 | 386 |
mmdetection | configs/gcnet/mask-rcnn_r50-syncbn_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 5 | 162 |
mmdetection | configs/gcnet/mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 9 | 258 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True... | 12 | 387 |
mmdetection | configs/gcnet/mask-rcnn_r101-syncbn_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 5 | 163 |
mmdetection | configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True... | 12 | 375 |
mmdetection | configs/gcnet/mask-rcnn_r50-gcb-r16-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| 9 | 257 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 5 | 183 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco.py | .py | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, Tr... | 12 | 389 |
mmdetection | configs/gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... | 12 | 368 |
mmdetection | configs/gcnet/mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | 12 | 370 |
mmdetection | configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 5 | 180 |
mmdetection | configs/gcnet/mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 9 | 256 |
mmdetection | configs/gcnet/mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| 9 | 257 |
mmdetection | configs/gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
... | 12 | 369 |
mmdetection | configs/gcnet/mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| 5 | 169 |
mmdetection | configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.05-coco.py | .py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 5% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@5.json'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 10 | 445 |
mmdetection | configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py',
'../_base_/datasets/semi_coco_detection.py'
]
detector = _base_.model
detector.data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
... | 85 | 2,511 |
mmdetection | configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.01-coco.py | .py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 1% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@1.json'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 10 | 445 |
mmdetection | configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.02-coco.py | .py | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py']
# 2% coco train2017 is set as labeled dataset
labeled_dataset = _base_.labeled_dataset
unlabeled_dataset = _base_.unlabeled_dataset
labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@2.json'
unlabeled_dataset.ann_file = 'semi_anns/insta... | 10 | 445 |
mmdetection | configs/misc/d2_retinanet_r50-caffe_fpn_ms-90k_coco.py | .py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookresearch/detectr... | 49 | 1,987 |
mmdetection | configs/misc/d2_mask-rcnn_r50-caffe_fpn_ms-90k_coco.py | .py | _base_ = '../common/ms-poly-90k_coco-instance.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookre... | 84 | 3,229 |
mmdetection | configs/misc/d2_faster-rcnn_r50-caffe_fpn_ms-90k_coco.py | .py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookresearch/detectr... | 76 | 2,940 |
mmdetection | configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py | .py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_pr... | 182 | 5,563 |
mmdetection | configs/hrnet/cascade-mask-rcnn_hrnetv2p-w18_20e_coco.py | .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... | 12 | 462 |
mmdetection | configs/hrnet/cascade-rcnn_hrnetv2p-w18-20e_coco.py | .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='... | 12 | 457 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w18-1x_coco.py | .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... | 11 | 436 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py | .py | _base_ = '../fcos/fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
... | 44 | 1,360 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w40_1x_coco.py | .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... | 12 | 461 |
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w40_2x_coco.py | .py | _base_ = './faster-rcnn_hrnetv2p-w40-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 386 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w32-1x_coco.py | .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, ),
... | 38 | 1,181 |
mmdetection | configs/hrnet/cascade-mask-rcnn_hrnetv2p-w40-20e_coco.py | .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... | 13 | 487 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w32-gn-head_4xb4-2x_coco.py | .py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 392 |
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w18-2x_coco.py | .py | _base_ = './faster-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 386 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-2x_coco.py | .py | _base_ = './fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 392 |
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w32-1x_coco.py | .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, ),
... | 38 | 1,185 |
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w40-1x_coco.py | .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... | 12 | 463 |
mmdetection | configs/hrnet/cascade-rcnn_hrnetv2p-w32-20e_coco.py | .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, ),
... | 52 | 1,523 |
mmdetection | configs/hrnet/htc_hrnetv2p-w32_20e_coco.py | .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... | 38 | 1,170 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w18-2x_coco.py | .py | _base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 17 | 384 |
mmdetection | configs/hrnet/htc_hrnetv2p-w40_20e_coco.py | .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... | 12 | 456 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w32-2x_coco.py | .py | _base_ = './mask-rcnn_hrnetv2p-w32-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 17 | 384 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w18-gn-head_ms-640-800-4xb4-2x_coco.py | .py | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_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='Pr... | 11 | 455 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py | .py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnot... | 36 | 933 |
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