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/pvt/retinanet_pvt-t_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'
]
model = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformer',
num_layers=[2, 2, ... | 19 | 627 |
mmdetection | configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b3.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 9 | 312 |
mmdetection | configs/pvt/retinanet_pvtv2-b0_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'
]
model = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | 20 | 652 |
mmdetection | configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 6, 40, 3],
mlp_ratios=(4, 4, 4, 4),
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b5.pth')),
neck=dict(in_channe... | 22 | 734 |
mmdetection | configs/retinanet/retinanet_r50_fpn_ms-640-800-3x_coco.py | .py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 5 | 188 |
mmdetection | configs/retinanet/retinanet_r101-caffe_fpn_1x_coco.py | .py | _base_ = './retinanet_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/retinanet/retinanet_r50-caffe_fpn_ms-2x_coco.py | .py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... | 17 | 388 |
mmdetection | configs/retinanet/retinanet_r18_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'
]
# model
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
n... | 21 | 682 |
mmdetection | configs/retinanet/retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
# ... | 22 | 711 |
mmdetection | configs/retinanet/retinanet_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './retinanet_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',
... | 15 | 419 |
mmdetection | configs/retinanet/retinanet_r50_fpn_90k_coco.py | .py | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# training schedule for 90k
train_cfg = dict(
_delete_=True,
type='IterBasedTrainLoop',
max_iters=90000,
val_interval=10000)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
d... | 25 | 639 |
mmdetection | configs/retinanet/retinanet_tta.py | .py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
img_scales = [(1333, 800), (666, 400), (2000, 1200)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[[
dict... | 24 | 881 |
mmdetection | configs/retinanet/retinanet_r18_fpn_1xb8-1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
train_dataloader = dict(batch_size=8)
# model
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained'... | 25 | 797 |
mmdetection | configs/retinanet/retinanet_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './retinanet_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',
... | 15 | 419 |
mmdetection | configs/retinanet/retinanet_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 237 |
mmdetection | configs/retinanet/retinanet_r50-caffe_fpn_ms-3x_coco.py | .py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,
... | 18 | 389 |
mmdetection | configs/retinanet/retinanet_x101-64x4d_fpn_ms-640-800-3x_coco.py | .py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
optim_wrapper... | 12 | 365 |
mmdetection | configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py | .py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
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(req... | 17 | 507 |
mmdetection | configs/retinanet/retinanet_r50_fpn_2x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_... | 26 | 624 |
mmdetection | configs/retinanet/retinanet_x101-64x4d_fpn_2x_coco.py | .py | _base_ = './retinanet_r50_fpn_2x_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',
... | 15 | 419 |
mmdetection | configs/retinanet/retinanet_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 213 |
mmdetection | configs/retinanet/retinanet_r101_fpn_ms-640-800-3x_coco.py | .py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9,... | 10 | 343 |
mmdetection | configs/retinanet/retinanet_x101-32x4d_fpn_2x_coco.py | .py | _base_ = './retinanet_r50_fpn_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),
style='pytorch',
... | 15 | 419 |
mmdetection | configs/retinanet/retinanet_r50_fpn_amp-1x_coco.py | .py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict(type='AmpOptimWrapper')
_base_.optim_wrapper.type = 'AmpOptimWrapper'
| 7 | 223 |
mmdetection | configs/retinanet/retinanet_r50-caffe_fpn_ms-1x_coco.py | .py | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
ke... | 16 | 472 |
mmdetection | configs/retinanet/retinanet_r101_fpn_1x_coco.py | .py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/retinanet/retinanet_r101_fpn_2x_coco.py | .py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/retinanet/retinanet_r101-caffe_fpn_ms-3x_coco.py | .py | _base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py'
# learning policy
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 9 | 243 |
mmdetection | configs/retinanet/retinanet_r50_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',
'./retinanet_tta.py'
]
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 11 | 312 |
mmdetection | configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-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(
_d... | 45 | 1,448 |
mmdetection | configs/point_rend/point-rend_r50-caffe_fpn_ms-3x_coco.py | .py | _base_ = './point-rend_r50-caffe_fpn_ms-1x_coco.py'
max_epochs = 36
# learning policy
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=[... | 19 | 391 |
mmdetection | configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'./qdtrack_faster-rcnn_r50_fpn_4e_base.py',
'../_base_/datasets/mot_challenge.py',
]
# evaluator
val_evaluator = [
dict(type='CocoVideoMetric', metric=['bbox'], classwise=True),
dict(type='MOTChallengeMetric', metric=['HOTA', 'CLEAR', 'Identity'])
]
test_evaluator = val_evaluator
# The fluc... | 15 | 376 |
mmdetection | configs/qdtrack/qdtrack_faster-rcnn_r50_fpn_4e_base.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py'
]
detector = _base_.model
detector.pop('data_preprocessor')
detector['backbone'].update(
dict(
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained'... | 119 | 3,950 |
mmdetection | configs/vfnet/vfnet_x101-32x4d_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_ms-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),
norm_eval=True,
... | 16 | 442 |
mmdetection | configs/vfnet/vfnet_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='VFNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 105 | 3,154 |
mmdetection | configs/vfnet/vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-2x_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_e... | 18 | 580 |
mmdetection | configs/vfnet/vfnet_r101_fpn_2x_coco.py | .py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
# learning policy
max_epochs = 24
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=... | 21 | 519 |
mmdetection | configs/vfnet/vfnet_r101-mdconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-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',
... | 16 | 541 |
mmdetection | configs/vfnet/vfnet_r50_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
... | 37 | 1,182 |
mmdetection | configs/vfnet/vfnet_x101-32x4d-mdconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-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),
norm_e... | 18 | 580 |
mmdetection | configs/vfnet/vfnet_res2net101-mdconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-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_e... | 19 | 597 |
mmdetection | configs/vfnet/vfnet_res2net-101_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_ms-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,
... | 17 | 459 |
mmdetection | configs/vfnet/vfnet_r101_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 196 |
mmdetection | configs/vfnet/vfnet_x101-64x4d_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_ms-2x_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,
... | 16 | 442 |
mmdetection | configs/vfnet/vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| 7 | 243 |
mmdetection | configs/vfnet/vfnet_r101_fpn_1x_coco.py | .py | _base_ = './vfnet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 193 |
mmdetection | configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
n... | 42 | 1,268 |
mmdetection | configs/libra_rcnn/libra-retinanet_r50_fpn_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
dict(
... | 27 | 674 |
mmdetection | configs/libra_rcnn/libra-fast-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... | 53 | 1,775 |
mmdetection | configs/libra_rcnn/libra-faster-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 205 |
mmdetection | configs/libra_rcnn/libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './libra-faster-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='pyt... | 15 | 427 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_amp-1x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 5 | 154 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 6 | 174 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 174 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_x101-32x4d_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='pyto... | 15 | 426 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_poly-1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='LoadAnnotations',
wi... | 19 | 581 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101_fpn_2x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 197 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
depth=101,
norm_c... | 20 | 519 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101_fpn_ms-poly-3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 11 | 258 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './mask-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/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | 32 | 942 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_r101_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',
... | 15 | 420 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.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=di... | 19 | 480 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco.py | .py | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(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=24,
by_epoch=True,
mil... | 16 | 359 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_ms-poly-3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
| 5 | 102 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 23 | 683 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8... | 23 | 730 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.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=di... | 19 | 480 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py | .py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 5... | 26 | 742 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_2x_coco.py | .py | _base_ = './mask-rcnn_x101-32x4d_fpn_2x_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='pyto... | 15 | 426 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | 29 | 894 |
mmdetection | configs/mask_rcnn/mask-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 213 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50_fpn_1x-wandb_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... | 17 | 551 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_poly-1x_coco_v1.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | 32 | 1,019 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | 14 | 414 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_2x_coco.py | .py | _base_ = './mask-rcnn_r101_fpn_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),
style='pytorch',
... | 15 | 420 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py | .py | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(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=24,
by_epoch=True,
mil... | 16 | 359 |
mmdetection | configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco.py | .py | _base_ = './mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 41 | 1,212 |
mmdetection | configs/mask_rcnn/mask-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 237 |
mmdetection | configs/mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 179 |
mmdetection | configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py | .py | # We follow the original implementation which
# adopts the Caffe pre-trained backbone.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='AutoAssign',
data_preprocessor=dict(
type='DetData... | 70 | 1,923 |
mmdetection | configs/ddq/ddq-detr-4scale_r50_8xb2-12e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DDQDETR',
num_queries=900, # num_matching_queries
# ratio of num_dense queries to num_queries
dense_topk_ratio=1.5,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
... | 171 | 5,879 |
mmdetection | configs/ddq/ddq-detr-4scale_swinl_8xb2-30e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa: E501
model = dict(
type='DDQDETR',
num_queries=900, # num_matching_queries
# ratio of nu... | 178 | 6,072 |
mmdetection | configs/ddq/ddq-detr-5scale_r50_8xb2-12e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DDQDETR',
num_queries=900, # num_matching_queries
# ratio of num_dense queries to num_queries
dense_topk_ratio=1.5,
with_box_refine=True,
as_two_stage=True,
num_feature_levels=5,
... | 172 | 5,888 |
mmdetection | configs/ddod/ddod_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='DDOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rg... | 73 | 2,223 |
mmdetection | configs/fcos/fcos_r101_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 257 |
mmdetection | configs/fcos/fcos_r50-dcn-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py | .py | _base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
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(
dcn=dict(type='DCNv2'... | 46 | 1,212 |
mmdetection | configs/fcos/fcos_x101-64x4d_fpn_gn-head_ms-640-800-2x_coco.py | .py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNeXt'... | 53 | 1,430 |
mmdetection | configs/fcos/fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | .py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# model settings
model = dict(
type='FCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.1... | 76 | 2,377 |
mmdetection | configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py | .py | _base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model setting
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(
init_cfg=dict(
... | 44 | 1,087 |
mmdetection | configs/fcos/fcos_r50-caffe_fpn_gn-head_ms-640-800-2x_coco.py | .py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 800)],
keep_ratio... | 31 | 815 |
mmdetection | configs/fcos/fcos_r50-caffe_fpn_gn-head_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[102.9801, 115.9465, 122.7717],
std=[1.0, 1.0, 1.0],
... | 76 | 2,093 |
mmdetection | configs/fcos/fcos_r18_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 281 |
mmdetection | configs/fcos/fcos_r101-caffe_fpn_gn-head-1x_coco.py | .py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
| 10 | 242 |
mmdetection | configs/fcos/fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py | .py | # TODO: Remove this config after benchmarking all related configs
_base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_dataloader = dict(batch_size=4, num_workers=4)
| 6 | 188 |
mmdetection | configs/fcos/fcos_r101-caffe_fpn_gn-head_ms-640-800-2x_coco.py | .py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', back... | 39 | 1,006 |
mmdetection | configs/fcos/fcos_r50-caffe_fpn_gn-head-center_1x_coco.py | .py | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| 5 | 146 |
mmdetection | configs/centernet/centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 84 | 2,397 |
mmdetection | configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 244 |
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