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/centernet/centernet_tta.py | .py | # This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, backend_args=None),
dict(
type='TestTimeAug',
transform... | 40 | 1,361 |
mmdetection | configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py | .py | _base_ = './centernet_r18-dcnv2_8xb16-crop512-140e_coco.py'
model = dict(neck=dict(use_dcn=False))
| 4 | 100 |
mmdetection | configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 220 |
mmdetection | configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0,... | 106 | 3,029 |
mmdetection | configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... | 137 | 4,299 |
mmdetection | configs/instaboost/mask-rcnn_r101_fpn_instaboost-4x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 208 |
mmdetection | configs/instaboost/cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
d... | 41 | 1,106 |
mmdetection | configs/instaboost/cascade-mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_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),
... | 15 | 438 |
mmdetection | configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
... | 41 | 1,095 |
mmdetection | configs/instaboost/cascade-mask-rcnn_r101_fpn_instaboost-4x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 217 |
mmdetection | configs/instaboost/mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_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='... | 15 | 430 |
mmdetection | configs/condinst/condinst_r50_fpn_ms-poly-90k_coco_instance.py | .py | _base_ = '../common/ms-poly-90k_coco-instance.py'
# model settings
model = dict(
type='CondInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32)... | 86 | 2,492 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_2x_coco.py | .py | _base_ = './faster-rcnn_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 | 421 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe-c4_ms-1x_coco.py | .py | _base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1... | 15 | 501 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py | .py | _base_ = './faster-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(requires_grad=False),
... | 32 | 1,084 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 201 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_bounded-iou_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))
| 7 | 207 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 215 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py | .py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-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(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.3... | 24 | 784 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_c4-1x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 182 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 15 | 421 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_soft-nms_1x_coco.py | .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(
test_cfg=dict(
rcnn=dict(
score_thr=0.05,
nms=dict(type='soft_nms', iou_threshold=0.5),
... | 13 | 347 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_iou_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
| 7 | 200 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_amp-1x_coco.py | .py | _base_ = './faster-rcnn_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 | 225 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_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)
#... | 21 | 712 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py | .py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-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=dict(type='BN', requires_... | 15 | 457 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py | .py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-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=dict(type='BN', requires_... | 15 | 457 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './faster-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 | 224 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = './faster-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(requires_grad=False),
... | 16 | 480 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 6 | 177 |
mmdetection | configs/faster_rcnn/faster-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './faster-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 | 239 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person-bicycle-car.py | .py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
metainfo = {
'classes': ('person', 'bicycle', 'car'),
'palette': [
(220, 20, 60),
(119, 11, 32),
(0, 0, 142),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
... | 17 | 642 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 12 | 311 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 199 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_ohem_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
| 3 | 118 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 177 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_2x_coco.py | .py | _base_ = './faster-rcnn_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 | 421 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_fcos-rpn_1x_coco.py | .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(
# copied from configs/fcos/fcos_r50-caffe_fpn_gn-head_1x_coco.py
neck=dict(
start_level=1,
add_extra_con... | 49 | 1,520 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_1x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 183 |
mmdetection | configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 199 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_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(requires_grad=False),
... | 16 | 481 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-90k_coco.py | .py | _base_ = 'faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 24 | 561 |
mmdetection | configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './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='pytorch',... | 15 | 421 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-tnr-pre_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | 15 | 569 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_ciou_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
| 7 | 201 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50_fpn_giou_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))
| 7 | 201 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py | .py | _base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(13... | 15 | 500 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_90k_coco.py | .py | _base_ = 'faster-rcnn_r50-caffe_fpn_1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 23 | 557 |
mmdetection | configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py | .py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
metainfo = {
'classes': ('person', ),
'palette': [
(220, 20, 60),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
val_dataloader = dict(dataset=dict(metainfo=metainfo))
test_... | 15 | 582 |
mmdetection | configs/sabl/sabl-retinanet_r101_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 settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... | 56 | 1,785 |
mmdetection | configs/sabl/sabl-cascade-rcnn_r101_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint... | 91 | 3,296 |
mmdetection | configs/sabl/sabl-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'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... | 52 | 1,644 |
mmdetection | configs/sabl/sabl-cascade-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
... | 87 | 3,155 |
mmdetection | configs/sabl/sabl-retinanet_r50-gn_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 settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
t... | 54 | 1,733 |
mmdetection | configs/sabl/sabl-retinanet_r101-gn_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 settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 58 | 1,874 |
mmdetection | configs/sabl/sabl-faster-rcnn_r101_fpn_1x_coco.py | .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(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | 39 | 1,369 |
mmdetection | configs/sabl/sabl-retinanet_r101-gn_fpn_ms-480-960-2x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 69 | 2,270 |
mmdetection | configs/sabl/sabl-faster-rcnn_r50_fpn_1x_coco.py | .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(
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
... | 35 | 1,228 |
mmdetection | configs/sabl/sabl-retinanet_r101-gn_fpn_ms-640-800-2x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 69 | 2,270 |
mmdetection | configs/objects365/faster-rcnn_r50-syncbn_fpn_1350k_objects365v1.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
roi_head=dict(bbox_head=dict(num_classes=365)))... | 50 | 1,371 |
mmdetection | configs/objects365/retinanet_r50_fpn_1x_objects365v2.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=365))
# Using 8 GPUS while training
optim_wrapper = dict(
type='OptimWrapper',
optimize... | 36 | 926 |
mmdetection | configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v1.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v1_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
train_dataloader = dict(
batch_size=4, # using 16 GPUS while traini... | 40 | 1,051 |
mmdetection | configs/objects365/retinanet_r50_fpn_1x_objects365v1.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v1_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=365))
# Using 8 GPUS while training
optim_wrapper = dict(
type='OptimWrapper',
optimize... | 36 | 926 |
mmdetection | configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v2.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
train_dataloader = dict(
batch_size=4, # using 16 GPUS while traini... | 40 | 1,051 |
mmdetection | configs/objects365/retinanet_r50-syncbn_fpn_1350k_objects365v1.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
bbox_head=dict(num_classes=365))
# training sche... | 50 | 1,355 |
mmdetection | configs/tood/tood_x101-64x4d-dconv-c4-c5_fpn_ms-2x_coco.py | .py | _base_ = './tood_x101-64x4d_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True),
),
bbox_head=dict(num_dcn=2))
| 8 | 248 |
mmdetection | configs/tood/tood_r101_fpn_ms-2x_coco.py | .py | _base_ = './tood_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 196 |
mmdetection | configs/tood/tood_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='TOOD',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 81 | 2,482 |
mmdetection | configs/tood/tood_r50_fpn_anchor-based_1x_coco.py | .py | _base_ = './tood_r50_fpn_1x_coco.py'
model = dict(bbox_head=dict(anchor_type='anchor_based'))
| 3 | 94 |
mmdetection | configs/tood/tood_r101-dconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './tood_r101_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(num_dcn=2))
| 8 | 236 |
mmdetection | configs/tood/tood_r50_fpn_ms-2x_coco.py | .py | _base_ = './tood_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],
g... | 31 | 801 |
mmdetection | configs/tood/tood_x101-64x4d_fpn_ms-2x_coco.py | .py | _base_ = './tood_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,
... | 17 | 442 |
mmdetection | configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... | 46 | 1,400 |
mmdetection | configs/panoptic_fpn/panoptic-fpn_r101_fpn_1x_coco.py | .py | _base_ = './panoptic-fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 200 |
mmdetection | configs/panoptic_fpn/panoptic-fpn_r101_fpn_ms-3x_coco.py | .py | _base_ = './panoptic-fpn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 203 |
mmdetection | configs/panoptic_fpn/panoptic-fpn_r50_fpn_ms-3x_coco.py | .py | _base_ = './panoptic-fpn_r50_fpn_1x_coco.py'
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(... | 36 | 939 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_r101_fpn_8xb1-12e_youtubevis2021.py | .py | _base_ = ['./masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py']
model = dict(
detector=dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='torchvision://resnet101')),
init_cfg=dict(
type='Pretrained',
... | 29 | 988 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_x101_fpn_8xb1-12e_youtubevis2019.py | .py | _base_ = ['./masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py']
model = dict(
detector=dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
init_cfg=dict(
type='Pretrained',
checkpoint=... | 17 | 633 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_r101_fpn_8xb1-12e_youtubevis2019.py | .py | _base_ = ['./masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py']
model = dict(
detector=dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='torchvision://resnet101')),
init_cfg=dict(
type='Pretrained',
... | 13 | 522 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2021.py | .py | _base_ = ['./masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py']
data_root = 'data/youtube_vis_2021/'
dataset_version = data_root[-5:-1]
# dataloader
train_dataloader = dict(
dataset=dict(
data_root=data_root,
dataset_version=dataset_version,
ann_file='annotations/youtube_vis_202... | 18 | 541 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/youtube_vis.py', '../_base_/default_runtime.py'
]
detector = _base_.model
detector.pop('data_preprocessor')
detector.roi_head.bbox_head.update(dict(num_classes=40))
detector.roi_head.mask_head.update(dict(num_classes=40))
detector.train_cf... | 131 | 4,009 |
mmdetection | configs/masktrack_rcnn/masktrack-rcnn_mask-rcnn_x101_fpn_8xb1-12e_youtubevis2021.py | .py | _base_ = ['./masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2019.py']
model = dict(
detector=dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
init_cfg=dict(
type='Pretrained',
checkpoint=... | 33 | 1,099 |
mmdetection | configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_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'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.m... | 97 | 3,359 |
mmdetection | configs/convnext/cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py | .py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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=['mmpr... | 155 | 5,627 |
mmdetection | configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.open... | 27 | 927 |
mmdetection | configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-270k_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_270k_coco-instance.py',
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncB... | 32 | 1,201 |
mmdetection | configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-90k_coco.py | .py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-270k_coco.py' # noqa
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, ... | 19 | 491 |
mmdetection | configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-90k_coco.py | .py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py' # noqa
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin... | 19 | 495 |
mmdetection | configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='Sy... | 32 | 1,204 |
mmdetection | configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DynamicRoIHead',
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_... | 29 | 1,051 |
mmdetection | configs/yolo/yolov3_d53_8xb8-amp-ms-608-273e_coco.py | .py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
| 4 | 132 |
mmdetection | configs/yolo/yolov3_d53_8xb8-ms-416-273e_coco.py | .py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0, 0, 0], to_rgb=True, rat... | 29 | 1,153 |
mmdetection | configs/yolo/yolov3_mobilenetv2_8xb24-320-300e_coco.py | .py | _base_ = ['./yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
... | 43 | 1,505 |
mmdetection | configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py | .py | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
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)
model = dict(
type='YOLOV3',
data_preproce... | 177 | 5,645 |
mmdetection | configs/yolo/yolov3_d53_8xb8-ms-608-273e_coco.py | .py | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[0, 0, 0],
std=[255., 255., 255.],
bgr_to_rgb=True,
pad_size_divisor=32)
model = dict(
type='YOLOV3',
data_preprocessor=data_preprocesso... | 168 | 5,442 |
mmdetection | configs/yolo/yolov3_d53_8xb8-320-273e_coco.py | .py | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
input_size = (320, 320)
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0,... | 30 | 1,157 |
mmdetection | configs/ms_rcnn/ms-rcnn_r101-caffe_fpn_2x_coco.py | .py | _base_ = './ms-rcnn_r101-caffe_fpn_1x_coco.py'
# learning policy
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 | 433 |
mmdetection | configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_2x_coco.py | .py | _base_ = './ms-rcnn_x101-64x4d_fpn_1x_coco.py'
# learning policy
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 | 433 |
mmdetection | configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_2x_coco.py | .py | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py'
# learning policy
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 | 432 |
mmdetection | configs/ms_rcnn/ms-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
... | 17 | 509 |
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