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/fpg/faster-rcnn_r50_fpg_crop640-50e_coco.py | .py | _base_ = 'faster-rcnn_r50_fpn_crop640-50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
stack_times=9,
paths=['bu'] * 9,
... | 49 | 1,452 |
mmdetection | configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py | .py | _base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(
_delete_=True,
type='FPG',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
inter_channels=256,
num_outs=5,
add_extra_c... | 54 | 1,574 |
mmdetection | configs/resnest/cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... | 94 | 3,394 |
mmdetection | configs/resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... | 47 | 1,402 |
mmdetection | configs/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | .py | _base_ = './cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 8 | 256 |
mmdetection | configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | .py | _base_ = './faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 8 | 256 |
mmdetection | configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | .py | _base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 8 | 248 |
mmdetection | configs/resnest/cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type... | 102 | 3,590 |
mmdetection | configs/resnest/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | .py | _base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 8 | 257 |
mmdetection | configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeS... | 40 | 1,214 |
mmdetection | configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v0.5_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)),
test_cfg=dict(
rcnn... | 14 | 424 |
mmdetection | configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.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),
s... | 15 | 436 |
mmdetection | configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 214 |
mmdetection | configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.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/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.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),
... | 15 | 438 |
mmdetection | configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.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),
s... | 15 | 436 |
mmdetection | configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=d... | 14 | 422 |
mmdetection | configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5.py | .py | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 216 |
mmdetection | configs/yolox/yolox_l_8xb8-300e_coco.py | .py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.0, widen_factor=1.0),
neck=dict(
in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
bbox_head=dict(in_channels=256, feat_channels=256))
| 9 | 273 |
mmdetection | configs/yolox/yolox_tiny_8xb8-300e_coco.py | .py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... | 55 | 1,829 |
mmdetection | configs/yolox/yolox_m_8xb8-300e_coco.py | .py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
bbox_head=dict(in_channels=192, feat_channels=192),
)
| 9 | 267 |
mmdetection | configs/yolox/yolox_tta.py | .py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | 37 | 1,240 |
mmdetection | configs/yolox/yolox_x_8xb8-300e_coco.py | .py | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
neck=dict(
in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
bbox_head=dict(in_channels=320, feat_channels=320))
| 9 | 275 |
mmdetection | configs/yolox/yolox_s_8xb8-300e_coco.py | .py | _base_ = [
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./yolox_tta.py'
]
img_scale = (640, 640) # width, height
# model settings
model = dict(
type='YOLOX',
data_preprocessor=dict(
type='DetDataPreprocessor',
pad_size_divisor=32,
batch_augments=[
... | 251 | 7,648 |
mmdetection | configs/yolox/yolox_nano_8xb8-300e_coco.py | .py | _base_ = './yolox_tiny_8xb8-300e_coco.py'
# model settings
model = dict(
backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
neck=dict(
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=True),
bbox_head=dict(in_channels=64, fe... | 12 | 357 |
mmdetection | configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.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(
bbox_head=dict(
type='RetinaHead',
anchor_generator=dict(
type='LegacyAnchorGenerator',
... | 18 | 617 |
mmdetection | configs/legacy_1.x/mask-rcnn_r50_fpn_1x_coco_v1.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(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='Le... | 35 | 1,238 |
mmdetection | configs/legacy_1.x/cascade-mask-rcnn_r50_fpn_1x_coco_v1.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'
]
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 79 | 2,744 |
mmdetection | configs/legacy_1.x/ssd300_coco_v1.py | .py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGene... | 21 | 709 |
mmdetection | configs/legacy_1.x/faster-rcnn_r50_fpn_1x_coco_v1.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(
type='FasterRCNN',
backbone=dict(
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
rp... | 39 | 1,385 |
mmdetection | configs/legacy_1.x/retinanet_r50-caffe_fpn_1x_coco_v1.py | .py | _base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
mean=[102.9801, 115.9465, 122.7717],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=di... | 17 | 512 |
mmdetection | configs/regnet/faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 18 | 522 |
mmdetection | configs/regnet/mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py | .py | _base_ = 'mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| 8 | 305 |
mmdetection | configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_ms-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'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | 61 | 1,826 |
mmdetection | configs/regnet/mask-rcnn_regnetx-4GF_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 18 | 521 |
mmdetection | configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py | .py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 26 | 831 |
mmdetection | configs/regnet/faster-rcnn_regnetx-3.2GF_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(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.... | 31 | 968 |
mmdetection | configs/regnet/mask-rcnn_regnetx-4GF_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(
_delete_=True,
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 27 | 741 |
mmdetection | configs/regnet/retinanet_regnetx-3.2GF_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(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.67... | 32 | 1,012 |
mmdetection | configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py | .py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 18 | 520 |
mmdetection | configs/regnet/mask-rcnn_regnetx-3.2GF_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(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | 31 | 965 |
mmdetection | configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py | .py | _base_ = './faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 391 |
mmdetection | configs/regnet/mask-rcnn_regnetx-1.6GF_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(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 27 | 740 |
mmdetection | configs/regnet/mask-rcnn_regnetx-400MF_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(
_delete_=True,
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 27 | 739 |
mmdetection | configs/regnet/cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py | .py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 18 | 529 |
mmdetection | configs/regnet/mask-rcnn_regnetx-12GF_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... | 18 | 520 |
mmdetection | configs/regnet/cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py | .py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 18 | 528 |
mmdetection | configs/regnet/cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py | .py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 18 | 529 |
mmdetection | configs/regnet/mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 18 | 522 |
mmdetection | configs/regnet/faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 18 | 524 |
mmdetection | configs/regnet/mask-rcnn_regnetx-8GF_fpn_1x_coco.py | .py | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 18 | 521 |
mmdetection | configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py | .py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 18 | 520 |
mmdetection | configs/regnet/faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 18 | 523 |
mmdetection | configs/regnet/mask-rcnn_regnetx-800MF_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(
_delete_=True,
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=Tr... | 27 | 740 |
mmdetection | configs/regnet/cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py | .py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
... | 29 | 856 |
mmdetection | configs/regnet/faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py | .py | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | 18 | 523 |
mmdetection | configs/regnet/cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py | .py | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | 18 | 530 |
mmdetection | configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 11 | 291 |
mmdetection | configs/res2net/cascade-mask-rcnn_res2net-101_fpn_20e_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 11 | 299 |
mmdetection | configs/res2net/mask-rcnn_res2net-101_fpn_2x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 11 | 287 |
mmdetection | configs/res2net/cascade-rcnn_res2net-101_fpn_20e_coco.py | .py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 11 | 294 |
mmdetection | configs/res2net/htc_res2net-101_fpn_20e_coco.py | .py | _base_ = '../htc/htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 11 | 276 |
mmdetection | configs/sort/faster-rcnn_r50_fpn_8xb2-8e_mot20halftrain_test-mot20halfval.py | .py | _base_ = ['./faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval']
model = dict(
rpn_head=dict(bbox_coder=dict(clip_border=True)),
roi_head=dict(
bbox_head=dict(bbox_coder=dict(clip_border=True), num_classes=1)))
# data
data_root = 'data/MOT20/'
train_dataloader = dict(dataset=dict(data_root=da... | 30 | 913 |
mmdetection | configs/sort/faster-rcnn_r50_fpn_8xb2-8e_mot20train_test-mot20train.py | .py | _base_ = ['./faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval']
model = dict(
rpn_head=dict(bbox_coder=dict(clip_border=True)),
roi_head=dict(
bbox_head=dict(bbox_coder=dict(clip_border=True), num_classes=1)))
# data
data_root = 'data/MOT20/'
train_dataloader = dict(
dataset=dict(
... | 33 | 1,009 |
mmdetection | configs/sort/sort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/mot_challenge.py', '../_base_/default_runtime.py'
]
default_hooks = dict(
logger=dict(type='LoggerHook', interval=1),
visualization=dict(type='TrackVisualizationHook', draw=False))
vis_backends = [dict(type='LocalVisBackend')]
v... | 55 | 1,641 |
mmdetection | configs/sort/faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/mot_challenge_det.py', '../_base_/default_runtime.py'
]
model = dict(
rpn_head=dict(
bbox_coder=dict(clip_border=False),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
... | 42 | 1,309 |
mmdetection | configs/sort/faster-rcnn_r50_fpn_8xb2-4e_mot17train_test-mot17train.py | .py | _base_ = ['./faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval']
# data
data_root = 'data/MOT17/'
train_dataloader = dict(
dataset=dict(ann_file='annotations/train_cocoformat.json'))
val_dataloader = dict(
dataset=dict(ann_file='annotations/train_cocoformat.json'))
test_dataloader = val_dataloader
v... | 12 | 429 |
mmdetection | configs/sort/sort_faster-rcnn_r50_fpn_8xb2-4e_mot17train_test-mot17test.py | .py | _base_ = [
'./sort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain'
'_test-mot17halfval.py'
]
# dataloader
val_dataloader = dict(
dataset=dict(ann_file='annotations/train_cocoformat.json'))
test_dataloader = dict(
dataset=dict(
ann_file='annotations/test_cocoformat.json',
data_prefix=dict(im... | 16 | 426 |
mmdetection | configs/wider_face/ssd300_8xb32-24e_widerface.py | .py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_2x.py'
]
model = dict(bbox_head=dict(num_classes=1))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations... | 65 | 2,087 |
mmdetection | configs/wider_face/retinanet_r50_fpn_1x_widerface.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/wider_face.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
# model settings
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, ... | 11 | 342 |
mmdetection | configs/glip/glip_atss_swin-t_a_fpn_dyhead_16xb2_ms-2x_funtune_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_a_mmdet-b3654169.pth' # noqa
lang_model_name = 'bert-base-uncased'
model = dict(
type='GLIP',
data_prepr... | 156 | 4,922 |
mmdetection | configs/glip/glip_atss_swin-t_b_fpn_dyhead_pretrain_obj365.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
model = dict(bbox_head=dict(early_fuse=True))
| 4 | 109 |
mmdetection | configs/glip/glip_atss_swin-l_fpn_dyhead_pretrain_mixeddata.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
model = dict(
backbone=dict(
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
drop_path_rate=0.4,
),
neck=dict(in_channels=[384, 768, 1536]),
bbox_head=dict(early_fuse=T... | 13 | 347 |
mmdetection | configs/glip/glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
lang_model_name = 'bert-base-uncased'
model = dict(
type='GLIP',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57... | 91 | 2,578 |
mmdetection | configs/glip/glip_atss_swin-t_b_fpn_dyhead_16xb2_ms-2x_funtune_coco.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_16xb2_ms-2x_funtune_coco.py'
model = dict(bbox_head=dict(early_fuse=True, use_checkpoint=True))
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_b_mmdet-6dfbd102.pth' # noqa
optim_wrapper = dict(
optimizer=dict(lr=0.00001),
clip_grad=dict(... | 10 | 361 |
mmdetection | configs/glip/glip_atss_swin-t_fpn_dyhead_16xb2_ms-2x_funtune_coco.py | .py | _base_ = './glip_atss_swin-t_b_fpn_dyhead_16xb2_ms-2x_funtune_coco.py'
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_mmdet-c24ce662.pth' # noqa
| 4 | 176 |
mmdetection | configs/glip/glip_atss_swin-l_fpn_dyhead_16xb2_ms-2x_funtune_coco.py | .py | _base_ = './glip_atss_swin-t_b_fpn_dyhead_16xb2_ms-2x_funtune_coco.py'
model = dict(
backbone=dict(
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
drop_path_rate=0.4,
),
neck=dict(in_channels=[384, 768, 1536]),
bbox_head=dict(ear... | 15 | 479 |
mmdetection | configs/glip/glip_atss_swin-t_c_fpn_dyhead_16xb2_ms-2x_funtune_coco.py | .py | _base_ = './glip_atss_swin-t_b_fpn_dyhead_16xb2_ms-2x_funtune_coco.py'
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/glip/glip_tiny_c_mmdet-2fc427dd.pth' # noqa
| 4 | 178 |
mmdetection | configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw13.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py'
model = dict(bbox_head=dict(early_fuse=True))
| 4 | 110 |
mmdetection | configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py | .py | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'text',
... | 795 | 29,292 |
mmdetection | configs/glip/odinw/glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw13.py | .py | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'text',
... | 339 | 11,105 |
mmdetection | configs/glip/odinw/glip_atss_swin-t_bc_fpn_dyhead_pretrain_odinw35.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_odinw35.py'
model = dict(bbox_head=dict(early_fuse=True))
| 4 | 110 |
mmdetection | configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_mini-lvis.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py'
model = dict(bbox_head=dict(early_fuse=True))
| 4 | 121 |
mmdetection | configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_lvis.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py'
model = dict(
backbone=dict(
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
drop_path_rate=0.4,
),
neck=dict(in_channels=[384, 768, 1536]),
bbox_head=dict(early... | 13 | 354 |
mmdetection | configs/glip/lvis/glip_atss_swin-l_fpn_dyhead_pretrain_zeroshot_mini-lvis.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py'
model = dict(
backbone=dict(
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
drop_path_rate=0.4,
),
neck=dict(in_channels=[384, 768, 1536]),
bbox_head=dict(... | 13 | 359 |
mmdetection | configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_mini-lvis.py | .py | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annotation... | 26 | 638 |
mmdetection | configs/glip/lvis/glip_atss_swin-t_bc_fpn_dyhead_pretrain_zeroshot_lvis.py | .py | _base_ = './glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py'
model = dict(bbox_head=dict(early_fuse=True))
| 4 | 116 |
mmdetection | configs/glip/lvis/glip_atss_swin-t_a_fpn_dyhead_pretrain_zeroshot_lvis.py | .py | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annotation... | 25 | 586 |
mmdetection | configs/glip/flickr30k/glip_atss_swin-t_c_fpn_dyhead_pretrain_obj365-goldg_zeroshot_flickr30k.py | .py | _base_ = '../glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py'
lang_model_name = 'bert-base-uncased'
model = dict(bbox_head=dict(early_fuse=True))
dataset_type = 'Flickr30kDataset'
data_root = 'data/flickr30k_entities/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecod... | 62 | 1,780 |
mmdetection | configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py | .py | _base_ = './gfl_r50_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),
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False)... | 16 | 524 |
mmdetection | configs/gfl/gfl_r50_fpn_ms-2x_coco.py | .py | _base_ = './gfl_r50_fpn_1x_coco.py'
max_epochs = 24
# 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=[16, 22],
... | 29 | 774 |
mmdetection | configs/gfl/gfl_r101_fpn_ms-2x_coco.py | .py | _base_ = './gfl_r50_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',
init_cfg=dict(... | 14 | 401 |
mmdetection | configs/gfl/gfl_x101-32x4d-dconv-c4-c5_fpn_ms-2x_coco.py | .py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
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),
dcn=d... | 19 | 580 |
mmdetection | configs/gfl/gfl_x101-32x4d_fpn_ms-2x_coco.py | .py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
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_... | 17 | 456 |
mmdetection | configs/gfl/gfl_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=... | 67 | 1,986 |
mmdetection | configs/ocsort/ocsort_yolox_x_8xb4-amp-80e_crowdhuman-mot20train_test-mot20test.py | .py | _base_ = [
'../bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py', # noqa: E501
]
model = dict(
type='OCSORT',
tracker=dict(
_delete_=True,
type='OCSORTTracker',
motion=dict(type='KalmanFilter'),
obj_score_thr=0.3,
init_track_th... | 19 | 507 |
mmdetection | configs/cascade_rpn/cascade-rpn_fast-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type=... | 28 | 1,278 |
mmdetection | configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... | 90 | 3,404 |
mmdetection | configs/cascade_rpn/cascade-rpn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
a... | 77 | 2,727 |
mmdetection | configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
num_queries=300,
num_feature_levels=4,
with_box_refine=False,
as_two_stage=False,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28... | 157 | 5,467 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.