repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-3x_coco.py | _base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | 505 | 25.631579 | 88 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-tnr-pre_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | 569 | 37 | 77 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | 481 | 29.125 | 66 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_amp-1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict(type='AmpOptimWrapper')
_base_.optim_wrapper.type = 'AmpOptimWrapper'
| 225 | 31.285714 | 59 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_2x_coco.py | _base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_2x_coco.py | _base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_90k_coco.py | _base_ = 'faster-rcnn_r50-caffe_fpn_1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 557 | 23.26087 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person-bicycle-car.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
metainfo = {
'classes': ('person', 'bicycle', 'car'),
'palette': [
(220, 20, 60),
(119, 11, 32),
(0, 0, 142),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
... | 642 | 36.823529 | 209 | py |
ERD | ERD-main/configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.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,
... | 1,450 | 28.612245 | 64 | py |
ERD | ERD-main/configs/fpg/faster-rcnn_r50_fpn_crop640-50e_coco.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... | 2,325 | 30.432432 | 79 | py |
ERD | ERD-main/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.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... | 1,574 | 28.166667 | 64 | py |
ERD | ERD-main/configs/fpg/faster-rcnn_r50_fpg-chn128_crop640-50e_coco.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)))
| 314 | 30.5 | 52 | py |
ERD | ERD-main/configs/fpg/mask-rcnn_r50_fpg-chn128_crop640-50e_coco.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),
... | 357 | 31.545455 | 52 | py |
ERD | ERD-main/configs/fpg/faster-rcnn_r50_fpg_crop640-50e_coco.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,
... | 1,452 | 28.653061 | 64 | py |
ERD | ERD-main/configs/fpg/mask-rcnn_r50_fpn_crop640-50e_coco.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 =... | 2,501 | 30.275 | 79 | py |
ERD | ERD-main/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.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))
| 154 | 24.833333 | 52 | py |
ERD | ERD-main/configs/maskformer/maskformer_swin-l-p4-w12_64xb1-ms-300e_coco.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... | 2,106 | 27.472973 | 129 | py |
ERD | ERD-main/configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.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,
... | 7,430 | 33.24424 | 79 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r50-gn_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
t... | 1,733 | 31.111111 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-cascade-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
... | 3,155 | 35.275862 | 79 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... | 1,644 | 30.634615 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-cascade-rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint... | 3,296 | 35.230769 | 79 | py |
ERD | ERD-main/configs/sabl/sabl-faster-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
... | 1,228 | 34.114286 | 77 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_fpn_ms-640-800-2x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,270 | 31.913043 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-faster-rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | 1,369 | 34.128205 | 77 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 1,874 | 31.327586 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... | 1,785 | 30.892857 | 75 | py |
ERD | ERD-main/configs/sabl/sabl-retinanet_r101-gn_fpn_ms-480-960-2x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | 2,270 | 31.913043 | 75 | py |
ERD | ERD-main/configs/objects365/faster-rcnn_r50-syncbn_fpn_1350k_objects365v1.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
roi_head=dict(bbox_head=dict(num_classes=365)))... | 1,371 | 26.44 | 75 | py |
ERD | ERD-main/configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v1.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v1_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
train_dataloader = dict(
batch_size=4, # using 16 GPUS while traini... | 1,051 | 25.3 | 78 | py |
ERD | ERD-main/configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v2.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
train_dataloader = dict(
batch_size=4, # using 16 GPUS while traini... | 1,051 | 25.3 | 78 | py |
ERD | ERD-main/configs/objects365/retinanet_r50_fpn_1x_objects365v2.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=365))
# Using 8 GPUS while training
optim_wrapper = dict(
type='OptimWrapper',
optimize... | 926 | 24.75 | 75 | py |
ERD | ERD-main/configs/objects365/retinanet_r50-syncbn_fpn_1350k_objects365v1.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v2_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
bbox_head=dict(num_classes=365))
# training sche... | 1,355 | 26.12 | 75 | py |
ERD | ERD-main/configs/objects365/retinanet_r50_fpn_1x_objects365v1.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/objects365v1_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=365))
# Using 8 GPUS while training
optim_wrapper = dict(
type='OptimWrapper',
optimize... | 926 | 24.75 | 75 | py |
ERD | ERD-main/configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
| 200 | 21.333333 | 56 | py |
ERD | ERD-main/configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epoch... | 1,040 | 27.916667 | 79 | py |
ERD | ERD-main/configs/pascal_voc/faster-rcnn_r50-caffe-c4_ms-18k_voc0712.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_arg... | 2,857 | 31.850575 | 79 | py |
ERD | ERD-main/configs/pascal_voc/ssd512_voc0712.py | _base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),... | 3,059 | 35.86747 | 79 | py |
ERD | ERD-main/configs/pascal_voc/ssd300_voc0712.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
... | 3,578 | 33.747573 | 79 | py |
ERD | ERD-main/configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712-cocofmt.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | 3,378 | 32.455446 | 79 | py |
ERD | ERD-main/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg =... | 1,022 | 28.228571 | 77 | py |
ERD | ERD-main/configs/queryinst/queryinst_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py | _base_ = './queryinst_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')))
| 228 | 27.625 | 71 | py |
ERD | ERD-main/configs/queryinst/queryinst_r101_fpn_ms-480-800-3x_coco.py | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 209 | 25.25 | 61 | py |
ERD | ERD-main/configs/queryinst/queryinst_r50_fpn_ms-480-800-3x_coco.py | _base_ = './queryinst_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
... | 967 | 28.333333 | 79 | py |
ERD | ERD-main/configs/queryinst/queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py | _base_ = './queryinst_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, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train_pipe... | 1,896 | 40.23913 | 75 | py |
ERD | ERD-main/configs/queryinst/queryinst_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='QueryInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.... | 5,345 | 33.269231 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './mask-rcnn_r101_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',
... | 420 | 27.066667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.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)
]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8... | 730 | 30.782609 | 69 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco.py | _base_ = './mask-rcnn_r101_fpn_1x_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(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 1,212 | 28.585366 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 174 | 28.166667 | 72 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_2x_coco.py | _base_ = './mask-rcnn_x101-32x4d_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pyto... | 426 | 27.466667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './mask-rcnn_x101-32x4d_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='pyto... | 426 | 27.466667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 174 | 28.166667 | 72 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 480 | 24.315789 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 894 | 29.862069 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 213 | 25.75 | 61 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(max_epochs=24)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
mil... | 359 | 21.5 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 179 | 29 | 72 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 942 | 28.46875 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_1x_coco.py | _base_ = './mask-rcnn_r101_fpn_1x_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(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | 683 | 28.73913 | 68 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_1x-wandb_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandBVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... | 551 | 31.470588 | 75 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_poly-1x_coco_v1.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 1,019 | 30.875 | 78 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/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(
depth=101,
norm_c... | 519 | 25 | 67 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_amp-1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 154 | 30 | 65 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 480 | 24.315789 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.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_grad=False),
style='caffe',
init_cfg=dict(
... | 414 | 28.642857 | 66 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_poly-1x_coco.py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='LoadAnnotations',
wi... | 581 | 29.631579 | 73 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
| 102 | 19.6 | 44 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './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')))
| 222 | 26.875 | 67 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_2x_coco.py | _base_ = './mask-rcnn_r101_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 420 | 27.066667 | 76 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101_fpn_2x_coco.py | _base_ = './mask-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py | _base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 5... | 742 | 27.576923 | 68 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './mask-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]))
| 237 | 28.75 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco.py | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(max_epochs=36)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
mil... | 359 | 21.5 | 79 | py |
ERD | ERD-main/configs/mask_rcnn/mask-rcnn_r101_fpn_ms-poly-3x_coco.py | _base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 258 | 22.545455 | 61 | py |
ERD | ERD-main/configs/pisa/mask-rcnn_x101-32x4d_fpn_pisa_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img... | 929 | 29 | 77 | py |
ERD | ERD-main/configs/pisa/mask-rcnn_r50_fpn_pisa_1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
... | 922 | 28.774194 | 77 | py |
ERD | ERD-main/configs/pisa/ssd512_pisa_coco.py | _base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| 224 | 27.125 | 71 | py |
ERD | ERD-main/configs/pisa/ssd300_pisa_coco.py | _base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| 224 | 27.125 | 71 | py |
ERD | ERD-main/configs/pisa/retinanet-r50_fpn_pisa_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 265 | 32.25 | 73 | py |
ERD | ERD-main/configs/pisa/faster-rcnn_x101-32x4d_fpn_pisa_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per... | 933 | 29.129032 | 77 | py |
ERD | ERD-main/configs/pisa/retinanet_x101-32x4d_fpn_pisa_1x_coco.py | _base_ = '../retinanet/retinanet_x101-32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 272 | 33.125 | 73 | py |
ERD | ERD-main/configs/pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=20... | 926 | 28.903226 | 77 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_1x_coco.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=... | 424 | 27.333333 | 66 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.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... | 819 | 33.166667 | 69 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 182 | 29.5 | 72 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| 179 | 29 | 73 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_1x_coco.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')))
| 230 | 27.875 | 67 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.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')))
| 233 | 28.25 | 67 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r101_fpn_20e_coco.py | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.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... | 427 | 27.533333 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.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]))
| 240 | 29.125 | 79 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_20e_coco.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... | 423 | 27.266667 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r101-caffe_fpn_1x_coco.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')))
| 225 | 27.25 | 67 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.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... | 428 | 27.6 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 178 | 28.833333 | 72 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-rcnn_x101_64x4d_fpn_20e_coco.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)... | 447 | 27 | 76 | py |
ERD | ERD-main/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_20e_coco.py | _base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 28.571429 | 61 | py |
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