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/ms_rcnn/ms-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 8 | 220 |
mmdetection | configs/ms_rcnn/ms-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './ms-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 15 | 417 |
mmdetection | configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './ms-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 15 | 417 |
mmdetection | configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256... | 17 | 515 |
mmdetection | configs/yolact/yolact_r50_8xb8-55e_coco.py | .py | _base_ = 'yolact_r50_1xb8-55e_coco.py'
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(lr=8e-3),
clip_grad=dict(max_norm=35, norm_type=2))
# learning rate
max_epochs = 55
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
... | 24 | 652 |
mmdetection | configs/yolact/yolact_r101_1xb8-55e_coco.py | .py | _base_ = './yolact_r50_1xb8-55e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 197 |
mmdetection | configs/yolact/yolact_r50_1xb8-55e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_instance.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
# model settings
input_size = 550
model = dict(
type='YOLACT',
data_preprocessor=dict(
type='DetDataPreprocessor',
... | 171 | 5,373 |
mmdetection | configs/free_anchor/freeanchor_r50_fpn_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='FreeAnchorRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
... | 23 | 753 |
mmdetection | configs/free_anchor/freeanchor_r101_fpn_1x_coco.py | .py | _base_ = './freeanchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 198 |
mmdetection | configs/free_anchor/freeanchor_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './freeanchor_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,
style='pytorch',
init_cfg=dict(
type='Pretrained', ... | 14 | 366 |
mmdetection | configs/v3det/deformable-detr-refine-twostage_r50_8xb4_sample1e-3_v3det_50e.py | .py | _base_ = '../deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py' # noqa
model = dict(
bbox_head=dict(num_classes=13204),
test_cfg=dict(max_per_img=300),
)
data_root = 'data/V3Det/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='L... | 109 | 3,490 |
mmdetection | configs/v3det/deformable-detr-refine-twostage_swin_16xb2_sample1e-3_v3det_50e.py | .py | _base_ = 'deformable-detr-refine-twostage_r50_8xb4_sample1e-3_v3det_50e.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=128,
... | 28 | 821 |
mmdetection | configs/v3det/faster_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'./faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py',
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth' # noqa
# model settings
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embe... | 28 | 791 |
mmdetection | configs/v3det/dino-4scale_swin_16xb1_sample1e-3_v3det_36e.py | .py | _base_ = 'dino-4scale_r50_8xb2_sample1e-3_v3det_36e.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=128,
depths=[2, 2, 18, ... | 28 | 786 |
mmdetection | configs/v3det/cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py', '../_base_/datasets/v3det.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
rpn_head=dict(
loss_bbox=dict(_delete_=True, type='L1Loss', loss_weight=1.0)),
roi_head=dict(bbox_head=[
... | 172 | 5,907 |
mmdetection | configs/v3det/dino-4scale_r50_8xb2_sample1e-3_v3det_36e.py | .py | _base_ = '../dino/dino-4scale_r50_8xb2-36e_coco.py'
model = dict(
bbox_head=dict(num_classes=13204),
test_cfg=dict(max_per_img=300),
)
data_root = 'data/V3Det/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
di... | 110 | 3,467 |
mmdetection | configs/v3det/cascade_rcnn_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'./cascade_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py',
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth' # noqa
# model settings
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
emb... | 28 | 792 |
mmdetection | configs/v3det/fcos_swinb_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'./fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py',
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth' # noqa
# model settings
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=... | 28 | 812 |
mmdetection | configs/v3det/fcos_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'../_base_/datasets/v3det.py', '../_base_/schedules/schedule_2x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bg... | 117 | 3,345 |
mmdetection | configs/v3det/faster_rcnn_r50_fpn_8x4_sample1e-3_mstrain_v3det_2x.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/v3det.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=13204,
reg_class_agnostic=True,
cl... | 73 | 2,212 |
mmdetection | configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py | .py | _base_ = './rpn_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 | 413 |
mmdetection | configs/rpn/rpn_r50-caffe-c4_1x_coco.py | .py | _base_ = [
'../_base_/models/rpn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
| 9 | 251 |
mmdetection | configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './rpn_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 | 413 |
mmdetection | configs/rpn/rpn_r101_fpn_2x_coco.py | .py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 191 |
mmdetection | configs/rpn/rpn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 8 | 216 |
mmdetection | configs/rpn/rpn_r101_fpn_1x_coco.py | .py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 191 |
mmdetection | configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './rpn_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 | 413 |
mmdetection | configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py | .py | _base_ = './rpn_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 | 413 |
mmdetection | configs/rpn/rpn_r50-caffe_fpn_1x_coco.py | .py | _base_ = './rpn_r50_fpn_1x_coco.py'
# use caffe img_norm
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=Fal... | 17 | 493 |
mmdetection | configs/rpn/rpn_r50_fpn_2x_coco.py | .py | _base_ = './rpn_r50_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',
begi... | 18 | 422 |
mmdetection | configs/lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
model = dict(
type='LAD',
data_preprocess... | 127 | 3,934 |
mmdetection | configs/lad/lad_r101-paa-r50_fpn_2xb8_coco_1x.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
model = dict(
type='LAD',
data_preprocesso... | 128 | 3,956 |
mmdetection | configs/detectors/cascade-rcnn_r50-sac_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 = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 13 | 382 |
mmdetection | configs/detectors/htc_r50-rfp_1x_coco.py | .py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dic... | 25 | 714 |
mmdetection | configs/detectors/detectors_htc-r50_1x_coco.py | .py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_ste... | 29 | 916 |
mmdetection | configs/detectors/htc_r50-sac_1x_coco.py | .py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True)))
| 9 | 245 |
mmdetection | configs/detectors/cascade-rcnn_r50-rfp_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 = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... | 29 | 851 |
mmdetection | configs/detectors/detectors_htc-r101_20e_coco.py | .py | _base_ = '../htc/htc_r101_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_s... | 29 | 920 |
mmdetection | configs/detectors/detectors_cascade-rcnn_r50_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 = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 33 | 1,053 |
mmdetection | configs/solo/solo_r50_fpn_3x_coco.py | .py | _base_ = './solo_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... | 36 | 903 |
mmdetection | configs/solo/decoupled-solo-light_r50_fpn_3x_coco.py | .py | _base_ = './decoupled-solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... | 51 | 1,718 |
mmdetection | configs/solo/solo_r50_fpn_8xb8-lsj-200e_coco.py | .py | _base_ = '../common/lsj-200e_coco-instance.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# model settings
model = dict(
type='SOLO',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12... | 72 | 2,213 |
mmdetection | configs/solo/decoupled-solo_r50_fpn_1x_coco.py | .py | _base_ = './solo_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOHead',
num_classes=80,
in_channels=256,
stacked_convs=7,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 96), (48, 192), (96, 384), (192, 76... | 25 | 774 |
mmdetection | configs/solo/solo_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='SOLO',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 63 | 1,817 |
mmdetection | configs/solo/solo_r18_fpn_8xb8-lsj-200e_coco.py | .py | _base_ = './solo_r50_fpn_8xb8-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 | 228 |
mmdetection | configs/solo/solo_r101_fpn_8xb8-lsj-200e_coco.py | .py | _base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 204 |
mmdetection | configs/solo/decoupled-solo_r50_fpn_3x_coco.py | .py | _base_ = './solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOHead',
num_classes=80,
in_channels=256,
stacked_convs=7,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 96), (48, 192), (96, 384), (192, 7... | 26 | 775 |
mmdetection | configs/albu_example/mask-rcnn_r50_fpn_albu-1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.... | 67 | 1,954 |
mmdetection | configs/_base_/default_runtime.py | .py | default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(typ... | 25 | 759 |
mmdetection | configs/_base_/schedules/schedule_20e.py | .py | # training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='M... | 29 | 816 |
mmdetection | configs/_base_/schedules/schedule_1x.py | .py | # training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='Mu... | 29 | 814 |
mmdetection | configs/_base_/schedules/schedule_2x.py | .py | # training schedule for 2x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='Mu... | 29 | 815 |
mmdetection | configs/_base_/datasets/coco_caption.py | .py | # data settings
dataset_type = 'CocoCaptionDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method ... | 61 | 2,124 |
mmdetection | configs/_base_/datasets/objects365v1_detection.py | .py | # dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | 75 | 2,481 |
mmdetection | configs/_base_/datasets/ade20k_semantic.py | .py | dataset_type = 'ADE20KSegDataset'
data_root = 'data/ADEChallengeData2016/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016... | 49 | 1,527 |
mmdetection | configs/_base_/datasets/refcocog.py | .py | # dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_b... | 56 | 1,578 |
mmdetection | configs/_base_/datasets/coco_detection.py | .py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 96 | 3,187 |
mmdetection | configs/_base_/datasets/semi_coco_detection.py | .py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 179 | 5,918 |
mmdetection | configs/_base_/datasets/cityscapes_instance.py | .py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | 114 | 3,725 |
mmdetection | configs/_base_/datasets/mot_challenge_det.py | .py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/MOT17/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args, to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(1088, 1088),
r... | 67 | 2,110 |
mmdetection | configs/_base_/datasets/deepfashion.py | .py | # dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/... | 96 | 3,198 |
mmdetection | configs/_base_/datasets/coco_instance_semantic.py | .py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 79 | 2,574 |
mmdetection | configs/_base_/datasets/coco_instance.py | .py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 96 | 3,238 |
mmdetection | configs/_base_/datasets/lvis_v0.5_instance.py | .py | # dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/lvis_v0.5/'
#... | 80 | 2,646 |
mmdetection | configs/_base_/datasets/refcoco.py | .py | # dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_b... | 56 | 1,589 |
mmdetection | configs/_base_/datasets/lvis_v1_instance.py | .py | # dataset settings
_base_ = 'lvis_v0.5_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
train_dataloader = dict(
dataset=dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/lvis_v1_train.json',
data_prefix=... | 23 | 656 |
mmdetection | configs/_base_/datasets/ade20k_panoptic.py | .py | # dataset settings
dataset_type = 'ADE20KPanopticDataset'
data_root = 'data/ADEChallengeData2016/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
dict(type='LoadPanopticAnnotations', backend_args=bac... | 39 | 1,189 |
mmdetection | configs/_base_/datasets/objects365v2_detection.py | .py | # dataset settings
dataset_type = 'Objects365V2Dataset'
data_root = 'data/Objects365/Obj365_v2/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | 74 | 2,464 |
mmdetection | configs/_base_/datasets/coco_semantic.py | .py | # dataset settings
dataset_type = 'CocoSegDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2:... | 79 | 2,360 |
mmdetection | configs/_base_/datasets/coco_panoptic.py | .py | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... | 95 | 3,277 |
mmdetection | configs/_base_/datasets/cityscapes_detection.py | .py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | 85 | 2,729 |
mmdetection | configs/_base_/datasets/voc0712.py | .py | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically Infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/segmentation/VOCde... | 93 | 3,434 |
mmdetection | configs/_base_/datasets/dsdl.py | .py | dataset_type = 'DSDLDetDataset'
data_root = 'path to dataset folder'
train_ann = 'path to train yaml file'
val_ann = 'path to val yaml file'
backend_args = None
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': "s3://open_data/",
# 'data/': "s3://open_data/"
# })... | 63 | 1,938 |
mmdetection | configs/_base_/datasets/openimages_detection.py | .py | # dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# ... | 82 | 2,979 |
mmdetection | configs/_base_/datasets/mot_challenge_reid.py | .py | # dataset settings
dataset_type = 'ReIDDataset'
data_root = 'data/MOT17/'
backend_args = None
# data pipeline
train_pipeline = [
dict(
type='TransformBroadcaster',
share_random_params=False,
transforms=[
dict(
type='LoadImageFromFile',
backend_arg... | 62 | 1,825 |
mmdetection | configs/_base_/datasets/mot_challenge.py | .py | # dataset settings
dataset_type = 'MOTChallengeDataset'
data_root = 'data/MOT17/'
img_scale = (1088, 1088)
backend_args = None
# data pipeline
train_pipeline = [
dict(
type='UniformRefFrameSample',
num_ref_imgs=1,
frame_range=10,
filter_key_img=True),
dict(
type='Transfo... | 91 | 2,782 |
mmdetection | configs/_base_/datasets/refcoco+.py | .py | # dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_b... | 56 | 1,593 |
mmdetection | configs/_base_/datasets/wider_face.py | .py | # dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/cityscapes/'
... | 74 | 2,338 |
mmdetection | configs/_base_/datasets/ade20k_instance.py | .py | # dataset settings
dataset_type = 'ADE20KInstanceDataset'
data_root = 'data/ADEChallengeData2016/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detect... | 54 | 1,735 |
mmdetection | configs/_base_/datasets/v3det.py | .py | # dataset settings
dataset_type = 'V3DetDataset'
data_root = 'data/V3Det/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (133... | 70 | 2,207 |
mmdetection | configs/_base_/datasets/youtube_vis.py | .py | dataset_type = 'YouTubeVISDataset'
data_root = 'data/youtube_vis_2019/'
dataset_version = data_root[-5:-1] # 2019 or 2021
backend_args = None
# dataset settings
train_pipeline = [
dict(
type='UniformRefFrameSample',
num_ref_imgs=1,
frame_range=100,
filter_key_img=True),
dict(
... | 67 | 2,099 |
mmdetection | configs/_base_/datasets/isaid_instance.py | .py | # dataset settings
dataset_type = 'iSAIDDataset'
data_root = 'data/iSAID/'
backend_args = None
# Please see `projects/iSAID/README.md` for data preparation
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type=... | 60 | 1,992 |
mmdetection | configs/_base_/models/cascade-mask-rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='CascadeRCNN',
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),
backbone=dict(
type='ResNet',
... | 204 | 7,169 |
mmdetection | configs/_base_/models/rpn_r50_fpn.py | .py | # model settings
model = dict(
type='RPN',
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='ResNet',
depth=50,
num_stage... | 65 | 2,004 |
mmdetection | configs/_base_/models/mask-rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='MaskRCNN',
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),
backbone=dict(
type='ResNet',
... | 128 | 4,273 |
mmdetection | configs/_base_/models/ssd300.py | .py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
bgr_to_rgb=True,
pad_size_divisor=1),
backbone=dict(
type='SSDVGG',
depth=16,... | 64 | 1,959 |
mmdetection | configs/_base_/models/faster-rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='FasterRCNN',
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='ResNet',
depth=50,
nu... | 115 | 3,828 |
mmdetection | configs/_base_/models/cascade-rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='CascadeRCNN',
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='ResNet',
depth=50,
n... | 186 | 6,521 |
mmdetection | configs/_base_/models/mask-rcnn_r50-caffe-c4.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_mask=True,
pad_size_divisor=32),
ba... | 133 | 4,275 |
mmdetection | configs/_base_/models/retinanet_r50_fpn.py | .py | # model settings
model = dict(
type='RetinaNet',
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='ResNet',
depth=50,
num... | 69 | 2,059 |
mmdetection | configs/_base_/models/rpn_r50-caffe-c4.py | .py | # model settings
model = dict(
type='RPN',
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(
type='ResNet',
depth=50,
num_stages=3,
... | 65 | 1,980 |
mmdetection | configs/_base_/models/faster-rcnn_r50-caffe-c4.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
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(
... | 124 | 4,018 |
mmdetection | configs/_base_/models/fast-rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='FastRCNN',
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='ResNet',
depth=50,
num_... | 69 | 2,256 |
mmdetection | configs/_base_/models/faster-rcnn_r50-caffe-dc5.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
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(
... | 112 | 3,670 |
mmdetection | configs/cornernet/cornernet_hourglass104_10xb5-crop511-210e-mstest_coco.py | .py | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py'
train_dataloader = dict(batch_size=5)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (10 GPUs) x (5 samples per GPU)
auto_scale_lr = dict(base_batch_size=50)
| 9 | 288 |
mmdetection | configs/cornernet/cornernet_hourglass104_32xb3-210e-mstest_coco.py | .py | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py'
train_dataloader = dict(batch_size=3)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (3 samples per GPU)
auto_scale_lr = dict(base_batch_size=96)
| 9 | 288 |
mmdetection | configs/cornernet/cornernet_hourglass104_8xb6-210e-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... | 184 | 5,555 |
mmdetection | configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_ms-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'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 26 | 813 |
mmdetection | configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_fpn_ms-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'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 26 | 811 |
mmdetection | configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_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'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 14 | 416 |
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