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/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | 14 | 418 |
mmdetection | configs/scratch/mask-rcnn_r50-scratch_fpn_gn-all_6x_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='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residua... | 41 | 1,084 |
mmdetection | configs/scratch/faster-rcnn_r50-scratch_fpn_gn-all_6x_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='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_resi... | 40 | 1,044 |
mmdetection | configs/scnet/scnet_r50_fpn_20e_coco.py | .py | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 19],
... | 16 | 374 |
mmdetection | configs/scnet/scnet_x101-64x4d_fpn_20e_coco.py | .py | _base_ = './scnet_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),
norm_eval=True,
... | 16 | 440 |
mmdetection | configs/scnet/scnet_r50_fpn_1x_coco.py | .py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='SCNet',
roi_head=dict(
_delete_=True,
type='SCNetRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=... | 139 | 5,063 |
mmdetection | configs/scnet/scnet_r101_fpn_20e_coco.py | .py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 194 |
mmdetection | configs/scnet/scnet_x101-64x4d_fpn_8xb1-20e_coco.py | .py | _base_ = './scnet_x101-64x4d_fpn_20e_coco.py'
train_dataloader = dict(batch_size=1, num_workers=1)
optim_wrapper = dict(optimizer=dict(lr=0.01))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_bat... | 9 | 331 |
mmdetection | configs/ssd/ssd300_coco.py | .py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations... | 72 | 2,414 |
mmdetection | configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
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)
... | 159 | 5,043 |
mmdetection | configs/ssd/ssd512_coco.py | .py | _base_ = 'ssd300_coco.py'
# model settings
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, 2... | 61 | 2,132 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-b_pretrain_all.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-b_pretrain_obj365_goldg_v3det/grounding_dino_swin-b_pretrain_obj365_goldg_v3de-f83eef00.pth' # noqa
model = dict(
use_autocast=True,
backbone=dict(
_d... | 336 | 11,486 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-l_pretrain_all.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-l_pretrain_obj365_goldg/grounding_dino_swin-l_pretrain_obj365_goldg-34dcdc53.pth' # noqa
num_levels = 5
model = dict(
use_autocast=True,
num_feature_levels=nu... | 541 | 19,785 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-l_pretrain_obj365_goldg.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 5
model = dict(
use_autocast=True,
num_feature_levels=num_levels,
backbone=dict(
_delete_=True,
... | 228 | 8,035 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | 102 | 3,606 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_finetune_8xb4_20e_cat.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.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_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Loa... | 103 | 3,442 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365.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_tiny_patch4_window7_224.pth' # noqa
lang_model_name = 'bert-base-uncased'
model = dict(
type='Gro... | 248 | 8,407 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | 118 | 4,051 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_flickr30k.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, 1333),
keep_ratio=True,
backend='pillow'),
dict(type='LoadTextAnnotat... | 43 | 1,186 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_pseudo-labeling_cat.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, 1333),
keep_ratio=True,
backend='pillow'),
dict(type='LoadTextAnnotat... | 44 | 1,176 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | 56 | 1,600 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-b_pretrain_obj365_goldg_v3det.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
use_autocast=True,
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=3... | 144 | 4,758 |
mmdetection | configs/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | 39 | 1,153 |
mmdetection | configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[
[
dict... | 86 | 3,034 |
mmdetection | configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_sft_coco.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[
[
dict... | 94 | 3,410 |
mmdetection | configs/mm_grounding_dino/coco/grounding_dino_swin-t_finetune_16xb4_1x_coco_48_17.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
base_classes = ('person', 'bicycle', 'car', 'motorcycle', 'train', 'truck',
'boat', 'bench', 'bird', 'horse', 'sheep', 'bear', 'zebra',
'giraffe', 'backpack', 'handbag', 'suitcase', 'frisbee',
... | 158 | 5,977 |
mmdetection | configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.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', 'text',
... | 795 | 29,292 |
mmdetection | configs/mm_grounding_dino/odinw/override_category.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmengine
def parse_args():
parser = argparse.ArgumentParser(description='Override Category')
parser.add_argument('data_root')
return parser.parse_args()
def main():
args = parse_args()
ChessPieces = [{
'id': 1,
... | 110 | 2,922 |
mmdetection | configs/mm_grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.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', 'text',
... | 339 | 11,069 |
mmdetection | configs/mm_grounding_dino/people_in_painting/grounding_dino_swin-t_finetune_8xb4_50e_people_in_painting.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
# https://universe.roboflow.com/roboflow-100/people-in-paintings/dataset/2
data_root = 'data/people_in_painting_v2/'
class_name = ('Human', )
palette = [(220, 20, 60)]
metainfo = dict(classes=class_name, palette=palette)
train_pipeline = [
dict(type='LoadIma... | 110 | 3,824 |
mmdetection | configs/mm_grounding_dino/rtts/grounding_dino_swin-t_finetune_8xb4_1x_rtts.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/RTTS/'
class_name = ('bicycle', 'bus', 'car', 'motorbike', 'person')
palette = [(255, 97, 0), (0, 201, 87), (176, 23, 31), (138, 43, 226),
(30, 144, 255)]
metainfo = dict(classes=class_name, palette=palette)
train_pipeline = [
di... | 107 | 3,735 |
mmdetection | configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco_plus.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
# change this
dict(type='RandomFlip', prob=0.0),
dict(
type='RandomChoice',
... | 168 | 5,468 |
mmdetection | configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.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=None,
i... | 229 | 6,616 |
mmdetection | configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcoco.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
# change this
dict(type='RandomFlip', prob=0.0),
dict(
type='RandomChoice',
... | 168 | 5,461 |
mmdetection | configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_grefcoco.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
# change this
dict(type='RandomFlip', prob=0.0),
dict(
type='RandomChoice',
... | 171 | 5,534 |
mmdetection | configs/mm_grounding_dino/refcoco/grounding_dino_swin-t_finetune_8xb4_5e_refcocog.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
# change this
dict(type='RandomFlip', prob=0.0),
dict(
type='RandomChoice',
... | 146 | 4,916 |
mmdetection | configs/mm_grounding_dino/brain_tumor/grounding_dino_swin-t_finetune_8xb4_50e_brain_tumor.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
# https://universe.roboflow.com/roboflow-100/brain-tumor-m2pbp/dataset/2
data_root = 'data/brain_tumor_v2/'
class_name = ('label0', 'label1', 'label2')
label_name = '_annotations.coco.json'
palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142)]
metainfo = dict(clas... | 113 | 3,873 |
mmdetection | configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
ty... | 121 | 4,119 |
mmdetection | configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annotations/lvis_o... | 25 | 578 |
mmdetection | configs/mm_grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annotations/lvis_v... | 26 | 630 |
mmdetection | configs/mm_grounding_dino/lvis/grounding_dino_swin-t_finetune_16xb4_1x_lvis_866_337.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
ty... | 121 | 4,144 |
mmdetection | configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py | .py | _base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py'
model = dict(test_cfg=dict(chunked_size=1))
| 4 | 110 |
mmdetection | configs/mm_grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.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='pillow'),
... | 79 | 2,402 |
mmdetection | configs/mm_grounding_dino/cityscapes/grounding_dino_swin-t_finetune_8xb4_50e_cityscapes.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/cityscapes/'
class_name = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100),
(0, 80, 100), (0, 0, 230), (119, 11, 32)... | 111 | 4,012 |
mmdetection | configs/mm_grounding_dino/ruod/grounding_dino_swin-t_finetune_8xb4_1x_ruod.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/RUOD/'
class_name = ('holothurian', 'echinus', 'scallop', 'starfish', 'fish',
'corals', 'diver', 'cuttlefish', 'turtle', 'jellyfish')
palette = [(235, 211, 70), (106, 90, 205), (160, 32, 240), (176, 23, 31),
(142, 0, 0), ... | 109 | 3,916 |
mmdetection | configs/mm_grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_flickr30k.py | .py | _base_ = '../grounding_dino_swin-t_pretrain_obj365.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, 1333),
... | 58 | 1,682 |
mmdetection | configs/solov2/solov2_r50_fpn_ms-3x_coco.py | .py | _base_ = './solov2_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... | 36 | 905 |
mmdetection | configs/solov2/solov2_r101-dcn_fpn_ms-3x_coco.py | .py | _base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dic... | 14 | 457 |
mmdetection | configs/solov2/solov2-light_r50_fpn_ms-3x_coco.py | .py | _base_ = './solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# dataset settings
train_pipeline = [
dict(... | 57 | 1,623 |
mmdetection | configs/solov2/solov2-light_r34_fpn_ms-3x_coco.py | .py | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=34, init_cfg=dict(checkpoint='torchvision://resnet34')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 218 |
mmdetection | configs/solov2/solov2_x101-dcn_fpn_ms-3x_coco.py | .py | _base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_c... | 18 | 560 |
mmdetection | configs/solov2/solov2-light_r50-dcn_fpn_ms-3x_coco.py | .py | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dict(
feat_channels=256,
stacked_convs=3,
scale_range... | 15 | 525 |
mmdetection | configs/solov2/solov2_r101_fpn_ms-3x_coco.py | .py | _base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
| 7 | 166 |
mmdetection | configs/solov2/solov2_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='SOLOv2',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],... | 71 | 2,046 |
mmdetection | configs/solov2/solov2-light_r18_fpn_ms-3x_coco.py | .py | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=18, init_cfg=dict(checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| 8 | 218 |
mmdetection | configs/guided_anchoring/ga-faster-rcnn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './ga-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),
style='pytorc... | 15 | 424 |
mmdetection | configs/guided_anchoring/ga-rpn_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './ga-rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 15 | 416 |
mmdetection | configs/guided_anchoring/ga-faster-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scal... | 65 | 2,385 |
mmdetection | configs/guided_anchoring/ga-faster-rcnn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './ga-faster-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 | 227 |
mmdetection | configs/guided_anchoring/ga-rpn_r50_fpn_1x_coco.py | .py | _base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,
... | 58 | 1,999 |
mmdetection | configs/guided_anchoring/ga-faster-rcnn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './ga-faster-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='pytorc... | 15 | 424 |
mmdetection | configs/guided_anchoring/ga-fast-rcnn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../fast_rcnn/fast-rcnn_r50_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=False),
norm_eval=True,
style='caffe',
in... | 67 | 2,441 |
mmdetection | configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50-caffe_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | 62 | 2,032 |
mmdetection | configs/guided_anchoring/ga-faster-rcnn_r50_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per... | 65 | 2,379 |
mmdetection | configs/guided_anchoring/ga-rpn_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './ga-rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 15 | 416 |
mmdetection | configs/guided_anchoring/ga-rpn_r50-caffe_fpn_1x_coco.py | .py | _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scales_per_octave=3,... | 58 | 2,005 |
mmdetection | configs/guided_anchoring/ga-retinanet_r50_fpn_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | 62 | 2,026 |
mmdetection | configs/guided_anchoring/ga-retinanet_x101-32x4d_fpn_1x_coco.py | .py | _base_ = './ga-retinanet_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/guided_anchoring/ga-rpn_r101-caffe_fpn_1x_coco.py | .py | _base_ = './ga-rpn_r50-caffe_fpn_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 9 | 236 |
mmdetection | configs/guided_anchoring/ga-retinanet_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './ga-retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... | 15 | 422 |
mmdetection | configs/guided_anchoring/ga-retinanet_r101-caffe_fpn_ms-2x.py | .py | _base_ = './ga-retinanet_r101-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip... | 35 | 869 |
mmdetection | configs/guided_anchoring/ga-retinanet_r101-caffe_fpn_1x_coco.py | .py | _base_ = './ga-retinanet_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/carafe/mask-rcnn_r50_fpn-carafe_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | 31 | 887 |
mmdetection | configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | 21 | 584 |
mmdetection | configs/resnet_strikes_back/cascade-mask-rcnn_r50-rsb-pre_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'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
m... | 16 | 620 |
mmdetection | configs/resnet_strikes_back/retinanet_r50-rsb-pre_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = ... | 16 | 613 |
mmdetection | configs/resnet_strikes_back/mask-rcnn_r50-rsb-pre_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = d... | 16 | 612 |
mmdetection | configs/resnet_strikes_back/faster-rcnn_r50-rsb-pre_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model ... | 16 | 615 |
mmdetection | configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 26 | 834 |
mmdetection | configs/groie/grid-rcnn_r50_fpn_gn-head-groie_1x_coco.py | .py | _base_ = '../grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=25... | 46 | 1,534 |
mmdetection | configs/groie/mask-rcnn_r50_fpn_syncbn-r4-gcb-c3-c5-groie_1x_coco.py | .py | _base_ = '../gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_cha... | 46 | 1,542 |
mmdetection | configs/groie/mask-rcnn_r101_fpn_syncbn-r4-gcb_c3-c5-groie_1x_coco.py | .py | _base_ = '../gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_ch... | 46 | 1,543 |
mmdetection | configs/groie/mask-rcnn_r50_fpn_groie_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 46 | 1,526 |
mmdetection | configs/ghm/retinanet_x101-32x4d_fpn_ghm-1x_coco.py | .py | _base_ = './retinanet_r50_fpn_ghm-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 | 423 |
mmdetection | configs/ghm/retinanet_x101-64x4d_fpn_ghm-1x_coco.py | .py | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | 15 | 423 |
mmdetection | configs/ghm/retinanet_r101_fpn_ghm-1x_coco.py | .py | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 201 |
mmdetection | configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
loss_cls=dict(
_delete_=True,
type='GHMC',
bins=30,
momentum=0.75,
use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(
_delete_=True,... | 19 | 509 |
mmdetection | configs/mask2former/mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
d... | 43 | 1,614 |
mmdetection | configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_16xb1-lsj-100e_coco-panoptic.py | .py | _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(
embed_dims=192,
num_heads=[6, 12, 24, 48],
init_cfg=dict(... | 26 | 999 |
mmdetection | configs/mask2former/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco.py | .py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | 38 | 1,462 |
mmdetection | configs/mask2former/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
... | 59 | 1,978 |
mmdetection | configs/mask2former/mask2former_r101_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = './mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 215 |
mmdetection | configs/mask2former/mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | 38 | 1,471 |
mmdetection | configs/mask2former/mask2former_r101_8xb2-lsj-50e_coco.py | .py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 208 |
mmdetection | configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco.py | .py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... | 101 | 2,968 |
mmdetection | configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
seg_pad_value=2... | 252 | 8,206 |
mmdetection | configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_8xb2-lsj-50e_coco-panoptic.py | .py | _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 6 | 295 |
mmdetection | configs/mask2former/mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py | .py | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
em... | 57 | 1,967 |
mmdetection | configs/dsdl/openimagesv6.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py',
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# dsdl dataset settings
# please visit our platform [OpenDataLab](https://opendatalab.com/)
# to downloaded dsdl datas... | 95 | 2,799 |
mmdetection | configs/dsdl/coco_instance.py | .py | _base_ = [
'../_base_/models/mask-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'
im... | 63 | 1,898 |
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