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/dsdl/objects365v2.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'../_base_/datasets/dsdl.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com... | 55 | 1,609 |
mmdetection | configs/dsdl/voc0712.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py',
# '../_base_/datasets/dsdl.py'
]
# model setting
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](ht... | 133 | 4,518 |
mmdetection | configs/dsdl/voc07.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com/)
# to downloaded dsdl dataset.
dataset_type = 'DSDLDetDatas... | 95 | 2,946 |
mmdetection | configs/dsdl/coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'../_base_/datasets/dsdl.py'
]
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com/)
# to downloaded dsdl dataset.
data_root = 'data/COCO2017'
i... | 34 | 988 |
mmdetection | configs/sparse_rcnn/sparse-rcnn_r101_fpn_ms-480-800-3x_coco.py | .py | _base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 211 |
mmdetection | configs/sparse_rcnn/sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py | .py | _base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(type='LoadImageFr... | 44 | 1,845 |
mmdetection | configs/sparse_rcnn/sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py | .py | _base_ = './sparse-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(60... | 33 | 953 |
mmdetection | configs/sparse_rcnn/sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py | .py | _base_ = './sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 230 |
mmdetection | configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='SparseRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[5... | 102 | 3,572 |
mmdetection | configs/grounding_dino/grounding_dino_swin-t_finetune_16xb2_1x_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/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth' # noqa
lang_model_name = 'bert-base-uncased'
model = dict(
type=... | 205 | 7,119 |
mmdetection | configs/grounding_dino/grounding_dino_r50_scratch_8xb2_1x_coco.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='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type=... | 209 | 7,232 |
mmdetection | configs/grounding_dino/grounding_dino_swin-b_pretrain_mixeddata.py | .py | _base_ = [
'./grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py',
]
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_... | 17 | 379 |
mmdetection | configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py | .py | _base_ = 'grounding_dino_swin-t_finetune_16xb2_1x_coco.py'
data_root = 'data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
model = dict(bbox_head=dict(num_classes=num_classes))
train_dataloader = dict(
dataset=dict(
data_root=data_... | 57 | 1,515 |
mmdetection | configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.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='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type... | 129 | 4,143 |
mmdetection | configs/grounding_dino/grounding_dino_swin-b_finetune_16xb2_1x_coco.py | .py | _base_ = [
'./grounding_dino_swin-t_finetune_16xb2_1x_coco.py',
]
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth' # noqa
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
... | 18 | 506 |
mmdetection | configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py | .py | _base_ = '../grounding_dino_swin-b_pretrain_mixeddata.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',
... | 797 | 29,423 |
mmdetection | configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa
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', 'tex... | 797 | 29,440 |
mmdetection | configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py | .py | _base_ = '../grounding_dino_swin-b_pretrain_mixeddata.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 | 11,100 |
mmdetection | configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa
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', 'tex... | 339 | 11,117 |
mmdetection | configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
# 30 is an empirical value, just set it to the maximum value
# without affecting the evaluation result
model = dict(test_cfg=dict(max_per_img=30))
data_root = 'data/coco/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=Non... | 229 | 6,628 |
mmdetection | configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py | .py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_refexp.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True),
... | 15 | 367 |
mmdetection | configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py | .py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_lvis.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True),
... | 15 | 365 |
mmdetection | configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.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='annota... | 25 | 590 |
mmdetection | configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.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='annota... | 26 | 642 |
mmdetection | configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py | .py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True... | 15 | 370 |
mmdetection | configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
data_root = 'data/d3/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, 1333),
keep_ratio=True,
backend='p... | 79 | 2,414 |
mmdetection | configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True)... | 15 | 369 |
mmdetection | configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py | .py | _base_ = 'grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py'
model = dict(test_cfg=dict(chunked_size=1))
| 4 | 110 |
mmdetection | configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
dataset_type = 'Flickr30kDataset'
data_root = 'data/flickr30k_entities/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, ... | 58 | 1,694 |
mmdetection | configs/yolof/yolof_r50-c5_8xb8-1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=Fals... | 117 | 3,591 |
mmdetection | configs/yolof/yolof_r50-c5_8xb8-iter-1x_coco.py | .py | _base_ = './yolof_r50-c5_8xb8-1x_coco.py'
# We implemented the iter-based config according to the source code.
# COCO dataset has 117266 images after filtering. We use 8 gpu and
# 8 batch size training, so 22500 is equivalent to
# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
# 20000 is equivalent... | 33 | 1,030 |
mmdetection | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py | .py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
| 6 | 231 |
mmdetection | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | .py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 5 | 188 |
mmdetection | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-400e_coco.py | .py | _base_ = './mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
train_dataloader = dict(dataset=dict(times=4 * 4))
param_scheduler = [
dict(
type='LinearLR',
star... | 21 | 543 |
mmdetection | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | 69 | 2,276 |
mmdetection | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | .py | _base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 5 | 202 |
mmdetection | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It c... | 31 | 1,123 |
mmdetection | configs/tridentnet/tridentnet_r50-caffe_ms-1x_coco.py | .py | _base_ = 'tridentnet_r50-caffe_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),
(133... | 16 | 505 |
mmdetection | configs/tridentnet/tridentnet_r50-caffe_ms-3x_coco.py | .py | _base_ = 'tridentnet_r50-caffe_ms-1x_coco.py'
# learning rate
max_epochs = 36
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',
... | 19 | 431 |
mmdetection | configs/tridentnet/tridentnet_r50-caffe_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'
]
model = dict(
type='TridentFasterRCNN',
backbone=dict(
type='TridentResNet',
trident_dilations=(1, 2, 3),
... | 23 | 748 |
mmdetection | configs/empirical_attention/faster-rcnn_r50-attn0010-dcn_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
... | 17 | 575 |
mmdetection | configs/empirical_attention/faster-rcnn_r50-attn1111-dcn_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... | 17 | 575 |
mmdetection | configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... | 14 | 403 |
mmdetection | configs/empirical_attention/faster-rcnn_r50-attn0010_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
... | 14 | 403 |
mmdetection | configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
image_size = (896, 896)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
norm_cfg = dict(type='BN', requires_grad=True)
checkp... | 95 | 3,148 |
mmdetection | configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_20-23-24e_coco.py | .py | _base_ = './mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | 18 | 411 |
mmdetection | configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/... | 17 | 577 |
mmdetection | configs/gn+ws/faster-rcnn_x101-32x4d_fpn_gn-ws-all_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 19 | 546 |
mmdetection | configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices... | 20 | 561 |
mmdetection | configs/gn+ws/mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... | 34 | 988 |
mmdetection | configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
| 7 | 207 |
mmdetection | configs/gn+ws/mask-rcnn_r50_fpn_gn-ws-all_20-23-24e_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=... | 18 | 405 |
mmdetection | configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py'
# model settings
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=... | 20 | 559 |
mmdetection | configs/gn+ws/faster-rcnn_x50-32x4d_fpn_gn-ws-all_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 19 | 544 |
mmdetection | configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_20-23-24e_coco.py | .py | _base_ = './mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | 18 | 412 |
mmdetection | configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_20-23-24e_coco.py | .py | _base_ = './mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end... | 18 | 406 |
mmdetection | configs/gn+ws/faster-rcnn_r101_fpn_gn-ws-all_1x_coco.py | .py | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
| 7 | 209 |
mmdetection | configs/mask2former_vis/mask2former_r101_8xb2-8e_youtubevis2019.py | .py | _base_ = './mask2former_r50_8xb2-8e_youtubevis2019.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
init_cfg=dict(
type='Pretrained',
checkpoint='https://download.openmmlab.com/mmdetection/... | 13 | 461 |
mmdetection | configs/mask2former_vis/mask2former_r50_8xb2-8e_youtubevis2019.py | .py | _base_ = ['../_base_/datasets/youtube_vis.py', '../_base_/default_runtime.py']
num_classes = 40
num_frames = 2
model = dict(
type='Mask2FormerVideo',
data_preprocessor=dict(
type='TrackDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=Tru... | 175 | 6,050 |
mmdetection | configs/mask2former_vis/mask2former_r101_8xb2-8e_youtubevis2021.py | .py | _base_ = './mask2former_r50_8xb2-8e_youtubevis2021.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
init_cfg=dict(
type='Pretrained',
checkpoint='https://download.openmmlab.com/mmdetection/... | 13 | 461 |
mmdetection | configs/mask2former_vis/mask2former_swin-l-p4-w12-384-in21k_8xb2-8e_youtubevis2021.py | .py | _base_ = ['./mask2former_r50_8xb2-8e_youtubevis2021.py']
depths = [2, 2, 18, 2]
model = dict(
type='Mask2FormerVideo',
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=384,
embed_dims=192,
depths=depths,
num_heads=[6, 12, 24, 48],
wi... | 65 | 2,208 |
mmdetection | configs/mask2former_vis/mask2former_r50_8xb2-8e_youtubevis2021.py | .py | _base_ = './mask2former_r50_8xb2-8e_youtubevis2019.py'
dataset_type = 'YouTubeVISDataset'
data_root = 'data/youtube_vis_2021/'
dataset_version = data_root[-5:-1] # 2019 or 2021
train_dataloader = dict(
dataset=dict(
data_root=data_root,
dataset_version=dataset_version,
ann_file='annotatio... | 38 | 979 |
mmdetection | configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot20train_test-mot20test.py | .py | _base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot20train_test-mot20test.py'
]
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
val_cfg = dict(type='ValLoop', fp16=True)
test_cfg = dict(type='TestLoop', fp16=True)
| 9 | 258 |
mmdetection | configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17test.py | .py | _base_ = [
'./bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-'
'mot17halftrain_test-mot17halfval.py'
]
test_dataloader = dict(
dataset=dict(
data_root='data/MOT17/',
ann_file='annotations/test_cocoformat.json',
data_prefix=dict(img_path='test')))
test_evaluator = dict(
type... | 18 | 523 |
mmdetection | configs/bytetrack/bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py | .py | _base_ = ['../yolox/yolox_x_8xb8-300e_coco.py']
dataset_type = 'MOTChallengeDataset'
data_root = 'data/MOT17/'
img_scale = (1440, 800) # weight, height
batch_size = 4
detector = _base_.model
detector.pop('data_preprocessor')
detector.bbox_head.update(dict(num_classes=1))
detector.test_cfg.nms.update(dict(iou_thresh... | 250 | 7,893 |
mmdetection | configs/bytetrack/bytetrack_yolox_x_8xb4-80e_crowdhuman-mot20train_test-mot20test.py | .py | _base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
dataset_type = 'MOTChallengeDataset'
img_scale = (1600, 896) # weight, height
model = dict(
data_preprocessor=dict(
type='TrackDataPreprocessor',
use_det_processor=True,
pad_size_divisor... | 128 | 4,457 |
mmdetection | configs/bytetrack/yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'../strongsort/yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py' # noqa: E501
]
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
| 7 | 195 |
mmdetection | configs/bytetrack/bytetrack_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
val_cfg = dict(type='ValLoop', fp16=True)
test_cfg = dict(type='TestLoop', fp16=True)
| 10 | 272 |
mmdetection | configs/grid_rcnn/grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py | .py | _base_ = './grid-rcnn_r50_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type='Pretra... | 14 | 373 |
mmdetection | configs/grid_rcnn/grid-rcnn_x101-64x4d_fpn_gn-head_2x_coco.py | .py | _base_ = './grid-rcnn_x101-32x4d_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type=... | 14 | 380 |
mmdetection | configs/grid_rcnn/grid-rcnn_r101_fpn_gn-head_2x_coco.py | .py | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 206 |
mmdetection | configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='GridRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_si... | 161 | 5,068 |
mmdetection | configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py | .py | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
# training schedule
max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
... | 20 | 414 |
mmdetection | configs/ld/ld_r50-gflv1-r101_fpn_1x_coco.py | .py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
model = dict(
backbone=dict(
type='ResNet',
depth=50,
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_c... | 20 | 572 |
mmdetection | configs/ld/ld_r18-gflv1-r101_fpn_1x_coco.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/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
model = dict(
type='Kn... | 71 | 2,361 |
mmdetection | configs/ld/ld_r34-gflv1-r101_fpn_1x_coco.py | .py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
model = dict(
backbone=dict(
type='ResNet',
depth=34,
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_c... | 20 | 569 |
mmdetection | configs/ld/ld_r101-gflv1-r101-dcn_fpn_2x_coco.py | .py | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py... | 50 | 1,608 |
mmdetection | configs/dcn/mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 211 |
mmdetection | configs/dcn/faster-rcnn_r50_fpn_dpool_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='DeformRoIPoolPack',
output_size=7,
output_cha... | 13 | 408 |
mmdetection | configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_amp-1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict... | 11 | 391 |
mmdetection | configs/dcn/cascade-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 216 |
mmdetection | configs/dcn/faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/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),
sty... | 17 | 557 |
mmdetection | configs/dcn/cascade-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 217 |
mmdetection | configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 210 |
mmdetection | configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 214 |
mmdetection | configs/dcn/faster-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 215 |
mmdetection | configs/dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 228 |
mmdetection | configs/dcn/cascade-mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 221 |
mmdetection | configs/dcn/cascade-mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 222 |
mmdetection | configs/dyhead/atss_r50-caffe_fpn_dyhead_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... | 104 | 3,366 |
mmdetection | configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_ms-2x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
type='ATSS',
data_preprocessor=dict(
... | 141 | 4,605 |
mmdetection | configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb... | 73 | 2,213 |
mmdetection | configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
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=1,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
... | 217 | 7,430 |
mmdetection | configs/maskformer/maskformer_swin-l-p4-w12_64xb1-ms-300e_coco.py | .py | _base_ = './maskformer_r50_ms-16xb1-75e_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=384... | 74 | 2,106 |
mmdetection | configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py | .py | _base_ = 'mask-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 | 1,450 |
mmdetection | configs/fpg/mask-rcnn_r50_fpg-chn128_crop640-50e_coco.py | .py | _base_ = 'mask-rcnn_r50_fpg_crop640-50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128),
mask_roi_extractor=dict(out_channels=128),
... | 11 | 357 |
mmdetection | configs/fpg/faster-rcnn_r50_fpn_crop640-50e_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'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
mode... | 74 | 2,325 |
mmdetection | configs/fpg/mask-rcnn_r50_fpn_crop640-50e_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'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model =... | 80 | 2,501 |
mmdetection | configs/fpg/faster-rcnn_r50_fpg-chn128_crop640-50e_coco.py | .py | _base_ = 'faster-rcnn_r50_fpg_crop640-50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128)))
| 10 | 314 |
mmdetection | configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py | .py | _base_ = 'retinanet_r50_fpg_crop640_50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
bbox_head=dict(in_channels=128))
| 6 | 154 |
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