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/deformable_detr/deformable-detr-refine_r50_16xb2-50e_coco.py | .py | _base_ = 'deformable-detr_r50_16xb2-50e_coco.py'
model = dict(with_box_refine=True)
| 3 | 84 |
mmdetection | configs/deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py | .py | _base_ = 'deformable-detr-refine_r50_16xb2-50e_coco.py'
model = dict(as_two_stage=True)
| 3 | 88 |
mmdetection | configs/detr/detr_r50_8xb2-500e_coco.py | .py | _base_ = './detr_r50_8xb2-150e_coco.py'
# learning policy
max_epochs = 500
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=10)
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[334],
... | 25 | 613 |
mmdetection | configs/detr/detr_r18_8xb2-500e_coco.py | .py | _base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[512]))
| 8 | 206 |
mmdetection | configs/detr/detr_r50_8xb2-150e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DETR',
num_queries=100,
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... | 156 | 5,433 |
mmdetection | configs/detr/detr_r101_8xb2-500e_coco.py | .py | _base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 196 |
mmdetection | configs/crowddet/crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py | .py | _base_ = ['../_base_/default_runtime.py']
model = dict(
type='CrowdDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
pad_size_divisor=64,
# This option is set according to http... | 228 | 7,480 |
mmdetection | configs/crowddet/crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py | .py | _base_ = './crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py'
model = dict(roi_head=dict(bbox_head=dict(with_refine=True)))
| 4 | 121 |
mmdetection | configs/deepfashion/mask-rcnn_r50_fpn_15e_deepfashion.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
# runtime settings
max_epochs = 15
train_c... | 24 | 663 |
mmdetection | configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channel... | 24 | 845 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| 6 | 179 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_20e_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='py... | 15 | 428 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pyt... | 15 | 427 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_20e_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_20e_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 | 423 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_x101_64x4d_fpn_20e_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
type='CascadeRCNN',
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)... | 16 | 447 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_20e_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 206 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... | 15 | 430 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r101_fpn_20e_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 201 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 240 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_ms-3x_coco.py | .py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
| 5 | 105 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py | .py | _base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires... | 19 | 502 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 178 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 216 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... | 25 | 758 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py | .py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| 6 | 183 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './cascade-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 | 225 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 200 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 6 | 182 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
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(req... | 17 | 483 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 8 | 230 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_20e_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='py... | 15 | 428 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_ms-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 208 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
type='CascadeRCNN',
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),... | 16 | 446 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# disable allowed_border to avoid potential errors.
model = dict(
data_preprocessor=dict(batch_augments=batch_augments... | 24 | 819 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = ['./cascade-mask-rcnn_r50_fpn_1x_coco.py']
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=... | 15 | 424 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 205 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 8 | 233 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './cascade-mask-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='pyt... | 15 | 427 |
mmdetection | configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './cascade-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 | 422 |
mmdetection | configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py | .py | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_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='... | 15 | 430 |
mmdetection | configs/atss/atss_r50_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 82 | 2,536 |
mmdetection | configs/atss/atss_r101_fpn_1x_coco.py | .py | _base_ = './atss_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 192 |
mmdetection | configs/atss/atss_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 72 | 2,164 |
mmdetection | configs/atss/atss_r18_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 232 |
mmdetection | configs/atss/atss_r101_fpn_8xb8-amp-lsj-200e_coco.py | .py | _base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 208 |
mmdetection | configs/common/lsj-200e_coco-instance.py | .py | _base_ = './lsj-100e_coco-instance.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_epo... | 19 | 379 |
mmdetection | configs/common/ms-poly-90k_coco-instance.py | .py | _base_ = '../_base_/default_runtime.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/d... | 131 | 3,896 |
mmdetection | configs/common/ms-90k_coco.py | .py | _base_ = '../_base_/default_runtime.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/d... | 123 | 3,754 |
mmdetection | configs/common/ssj_scp_270k_coco-instance.py | .py | _base_ = 'ssj_270k_coco-instance.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# 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_... | 61 | 1,949 |
mmdetection | configs/common/lsj-200e_coco-detection.py | .py | _base_ = './lsj-100e_coco-detection.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_ep... | 19 | 380 |
mmdetection | configs/common/lsj-100e_coco-detection.py | .py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# 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)
# dat... | 123 | 3,769 |
mmdetection | configs/common/ms_3x_coco.py | .py | _base_ = '../_base_/default_runtime.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/... | 109 | 3,449 |
mmdetection | configs/common/ssj_270k_coco-instance.py | .py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# 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)
# da... | 126 | 3,943 |
mmdetection | configs/common/lsj-100e_coco-instance.py | .py | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# 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)
# dat... | 123 | 3,811 |
mmdetection | configs/common/ms-poly_3x_coco-instance.py | .py | _base_ = '../_base_/default_runtime.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/d... | 119 | 3,680 |
mmdetection | configs/common/ms_3x_coco-instance.py | .py | _base_ = '../_base_/default_runtime.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/... | 109 | 3,481 |
mmdetection | configs/cityscapes/faster-rcnn_r50_fpn_1x_cityscapes.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
num_classes=8,
loss_bb... | 42 | 1,286 |
mmdetection | configs/cityscapes/mask-rcnn_r50_fpn_1x_cityscapes.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
... | 44 | 1,354 |
mmdetection | configs/nas_fcos/nas-fcos_r50-caffe_fpn_fcoshead-gn-head_4xb4-1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='NASFCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
... | 76 | 2,179 |
mmdetection | configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='NASFCOS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
... | 75 | 2,157 |
mmdetection | configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py | .py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | 117 | 4,108 |
mmdetection | configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | .py | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 6 | 218 |
mmdetection | configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 6 | 199 |
mmdetection | configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 226 |
mmdetection | configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 248 |
mmdetection | configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... | 96 | 3,534 |
mmdetection | configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 6 | 195 |
mmdetection | configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 6 | 222 |
mmdetection | configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 222 |
mmdetection | configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 252 |
mmdetection | configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', ... | 39 | 1,237 |
mmdetection | configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.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(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', tem... | 60 | 1,811 |
mmdetection | configs/rtmdet/rtmdet_l_8xb32-300e_coco.py | .py | _base_ = [
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py',
'../_base_/datasets/coco_detection.py', './rtmdet_tta.py'
]
model = dict(
type='RTMDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395... | 180 | 5,272 |
mmdetection | configs/rtmdet/rtmdet_x_p6_4xb8-300e_coco.py | .py | _base_ = './rtmdet_x_8xb32-300e_coco.py'
model = dict(
backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)),
neck=dict(in_channels=[320, 640, 960, 1280]),
bbox_head=dict(
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32, 64])))
train_pipeline = [
dict(ty... | 133 | 4,158 |
mmdetection | configs/rtmdet/rtmdet_l_convnext_b_4xb32-100e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
custom_imports = dict(
imports=['mmpretrain.models'], allow_failed_imports=False)
norm_cfg = dict(type='GN', num_groups=32)
checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_in1k-384px_20221219-4570f792.pth' # ... | 82 | 2,232 |
mmdetection | configs/rtmdet/rtmdet_l_swin_b_4xb32-100e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
norm_cfg = dict(type='GN', num_groups=32)
checkpoint = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
type='RTMDet',
data_preprocessor=dict(
_delete_=True,
type='DetDataPr... | 79 | 2,109 |
mmdetection | configs/rtmdet/rtmdet_x_8xb32-300e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
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))
| 8 | 260 |
mmdetection | configs/rtmdet/rtmdet_m_8xb32-300e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
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))
| 7 | 250 |
mmdetection | configs/rtmdet/rtmdet_l_swin_b_p6_4xb16-100e_coco.py | .py | _base_ = './rtmdet_l_swin_b_4xb32-100e_coco.py'
model = dict(
backbone=dict(
depths=[2, 2, 18, 2, 1],
num_heads=[4, 8, 16, 32, 64],
strides=(4, 2, 2, 2, 2),
out_indices=(1, 2, 3, 4)),
neck=dict(in_channels=[256, 512, 1024, 2048]),
bbox_head=dict(
anchor_generator=dic... | 115 | 3,758 |
mmdetection | configs/rtmdet/rtmdet_s_8xb32-300e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='bac... | 63 | 2,096 |
mmdetection | configs/rtmdet/rtmdet-ins_m_8xb32-300e_coco.py | .py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
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))
| 7 | 254 |
mmdetection | configs/rtmdet/rtmdet-ins_l_8xb32-300e_coco.py | .py | _base_ = './rtmdet_l_8xb32-300e_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='RTMDetInsSepBNHead',
num_classes=80,
in_channels=256,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=256,
act_cfg=dict(type='SiLU',... | 105 | 3,140 |
mmdetection | configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py | .py | _base_ = './rtmdet-ins_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained',... | 49 | 1,546 |
mmdetection | configs/rtmdet/rtmdet-ins_x_8xb16-300e_coco.py | .py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
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))
base_lr = 0.002
# optimizer
optim_wrapper = dict(optim... | 32 | 795 |
mmdetection | configs/rtmdet/rtmdet-ins_s_8xb32-300e_coco.py | .py | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix=... | 81 | 2,492 |
mmdetection | configs/rtmdet/rtmdet_tta.py | .py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), 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 | 1,230 |
mmdetection | configs/rtmdet/rtmdet_tiny_8xb32-300e_coco.py | .py | _base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', pre... | 44 | 1,435 |
mmdetection | configs/rtmdet/classification/cspnext-x_8xb256-rsb-a1-600e_in1k.py | .py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=1.33, widen_factor=1.25),
head=dict(in_channels=1280))
| 6 | 156 |
mmdetection | configs/rtmdet/classification/cspnext-l_8xb256-rsb-a1-600e_in1k.py | .py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=1, widen_factor=1),
head=dict(in_channels=1024))
| 6 | 150 |
mmdetection | configs/rtmdet/classification/cspnext-s_8xb256-rsb-a1-600e_in1k.py | .py | _base_ = [
'mmpretrain::_base_/datasets/imagenet_bs256_rsb_a12.py',
'mmpretrain::_base_/schedules/imagenet_bs2048_rsb.py',
'mmpretrain::_base_/default_runtime.py'
]
model = dict(
type='ImageClassifier',
backbone=dict(
type='mmdet.CSPNeXt',
arch='P5',
out_indices=(4, ),
... | 65 | 1,644 |
mmdetection | configs/rtmdet/classification/cspnext-tiny_8xb256-rsb-a1-600e_in1k.py | .py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=0.167, widen_factor=0.375),
head=dict(in_channels=384))
| 6 | 157 |
mmdetection | configs/rtmdet/classification/cspnext-m_8xb256-rsb-a1-600e_in1k.py | .py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=0.67, widen_factor=0.75),
head=dict(in_channels=768))
| 6 | 155 |
mmdetection | configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=di... | 37 | 1,003 |
mmdetection | configs/gn/mask-rcnn_r50-contrib_fpn_gn-all_3x_coco.py | .py | _base_ = './mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
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,
... | 19 | 411 |
mmdetection | configs/gn/mask-rcnn_r101_fpn_gn-all_2x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_gn')))
| 8 | 219 |
mmdetection | configs/gn/mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')),
neck=dict(norm_cfg=norm_cfg),
roi_... | 32 | 863 |
mmdetection | configs/gn/mask-rcnn_r50_fpn_gn-all_3x_coco.py | .py | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
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=ma... | 19 | 403 |
mmdetection | configs/gn/mask-rcnn_r101_fpn_gn-all_3x_coco.py | .py | _base_ = './mask-rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
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=m... | 19 | 404 |
mmdetection | configs/deepsort/deepsort_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... | 86 | 2,827 |
mmdetection | configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17train_test-mot17test.py | .py | _base_ = [
'./deepsort_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=dic... | 16 | 430 |
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