repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages.py | configs/openimages/faster-rcnn_r50_fpn_32xb2-1x_openimages.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
# Using 32 GPUS while training
optim_wrapper = dict(
type='OptimWrappe... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py | configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py | configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py | _base_ = './rpn_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',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r101_fpn_1x_coco.py | configs/rpn/rpn_r101_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py | configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py | _base_ = './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',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r101_fpn_2x_coco.py | configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r50-caffe_fpn_1x_coco.py | configs/rpn/rpn_r50-caffe_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
# use caffe img_norm
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=Fal... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r50-caffe-c4_1x_coco.py | configs/rpn/rpn_r50-caffe-c4_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py | configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py | _base_ = './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',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py | configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py | _base_ = './rpn_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',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r50_fpn_2x_coco.py | configs/rpn/rpn_r50_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begi... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r101-caffe_fpn_1x_coco.py | configs/rpn/rpn_r101-caffe_fpn_1x_coco.py | _base_ = './rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/rpn/rpn_r50_fpn_1x_coco.py | configs/rpn/rpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
# inference on val dataset and dump the proposals with evaluate metric
# data... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py | configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py | configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './centernet-update_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]))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py | configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0,... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py | configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet_tta.py | configs/centernet/centernet_tta.py | # This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, backend_args=None),
dict(
type='TestTimeAug',
transform... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py | configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py | _base_ = './centernet_r18-dcnv2_8xb16-crop512-140e_coco.py'
model = dict(neck=dict(use_dcn=False))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py | configs/centernet/centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py | configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deepfashion/mask-rcnn_r50_fpn_15e_deepfashion.py | configs/deepfashion/mask-rcnn_r50_fpn_15e_deepfashion.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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_swin-b_pretrain_mixeddata.py | configs/grounding_dino/grounding_dino_swin-b_pretrain_mixeddata.py | _base_ = [
'./grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py',
]
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_r50_scratch_8xb2_1x_coco.py | configs/grounding_dino/grounding_dino_r50_scratch_8xb2_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
lang_model_name = 'bert-base-uncased'
model = dict(
type='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type=... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py | configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
lang_model_name = 'bert-base-uncased'
model = dict(
type='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_swin-t_finetune_16xb2_1x_coco.py | configs/grounding_dino/grounding_dino_swin-t_finetune_16xb2_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth' # noqa
lang_model_name = 'bert-base-uncased'
model = dict(
type=... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py | configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py | _base_ = 'grounding_dino_swin-t_finetune_16xb2_1x_coco.py'
data_root = 'data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(220, 20, 60)])
model = dict(bbox_head=dict(num_classes=num_classes))
train_dataloader = dict(
dataset=dict(
data_root=data_... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/grounding_dino_swin-b_finetune_16xb2_1x_coco.py | configs/grounding_dino/grounding_dino_swin-b_finetune_16xb2_1x_coco.py | _base_ = [
'./grounding_dino_swin-t_finetune_16xb2_1x_coco.py',
]
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth' # noqa
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py | configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw13.py | _base_ = '../grounding_dino_swin-b_pretrain_mixeddata.py'
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'text',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py | configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw13.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'tex... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py | configs/grounding_dino/odinw/grounding_dino_swin-b_pretrain_odinw35.py | _base_ = '../grounding_dino_swin-b_pretrain_mixeddata.py'
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'text',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py | configs/grounding_dino/odinw/grounding_dino_swin-t_pretrain_odinw35.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py' # noqa
dataset_type = 'CocoDataset'
data_root = 'data/odinw/'
base_test_pipeline = _base_.test_pipeline
base_test_pipeline[-1]['meta_keys'] = ('img_id', 'img_path', 'ori_shape',
'img_shape', 'scale_factor', 'tex... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/odinw/override_category.py | configs/grounding_dino/odinw/override_category.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,
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py | configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_lvis.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annota... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py | configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_mini-lvis.py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py | configs/grounding_dino/lvis/grounding_dino_swin-b_pretrain_zeroshot_lvis.py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_lvis.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True),
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py | configs/grounding_dino/lvis/grounding_dino_swin-t_pretrain_zeroshot_mini-lvis.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
model = dict(test_cfg=dict(
max_per_img=300,
chunked_size=40,
))
dataset_type = 'LVISV1Dataset'
data_root = 'data/coco/'
val_dataloader = dict(
dataset=dict(
data_root=data_root,
type=dataset_type,
ann_file='annota... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py | configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
dataset_type = 'Flickr30kDataset'
data_root = 'data/flickr30k_entities/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, ... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py | configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py | _base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True)... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py | configs/grounding_dino/dod/grounding_dino_swin-b_pretrain_zeroshot_parallel_dod.py | _base_ = 'grounding_dino_swin-b_pretrain_zeroshot_concat_dod.py'
model = dict(test_cfg=dict(chunked_size=1))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py | configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_parallel_dod.py | _base_ = 'grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py'
model = dict(test_cfg=dict(chunked_size=1))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py | configs/grounding_dino/dod/grounding_dino_swin-t_pretrain_zeroshot_concat_dod.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
data_root = 'data/d3/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=None,
imdecode_backend='pillow'),
dict(
type='FixScaleResize',
scale=(800, 1333),
keep_ratio=True,
backend='p... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py | configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py | _base_ = './grounding_dino_swin-t_pretrain_zeroshot_refexp.py'
model = dict(
type='GroundingDINO',
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=12,
drop_path_rate=0.3,
patch_norm=True),
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py | configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py | _base_ = '../grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py'
# 30 is an empirical value, just set it to the maximum value
# without affecting the evaluation result
model = dict(test_cfg=dict(max_per_img=30))
data_root = 'data/coco/'
test_pipeline = [
dict(
type='LoadImageFromFile', backend_args=Non... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17train_test-mot17test.py | configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17train_test-mot17test.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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py | configs/deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/albu_example/mask-rcnn_r50_fpn_albu-1x_coco.py | configs/albu_example/mask-rcnn_r50_fpn_albu-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py | configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py | # We follow the original implementation which
# adopts the Caffe pre-trained backbone.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='AutoAssign',
data_preprocessor=dict(
type='DetData... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/boxinst/boxinst_r50_fpn_ms-90k_coco.py | configs/boxinst/boxinst_r50_fpn_ms-90k_coco.py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='BoxInst',
data_preprocessor=dict(
type='BoxInstDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
mask_stride=4,
pa... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py | configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py | _base_ = './boxinst_r50_fpn_ms-90k_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/retinanet_r50-syncbn_fpn_1350k_objects365v1.py | 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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v1.py | 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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/retinanet_r50_fpn_1x_objects365v2.py | 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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/faster-rcnn_r50-syncbn_fpn_1350k_objects365v1.py | 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)))... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/retinanet_r50_fpn_1x_objects365v1.py | 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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/objects365/faster-rcnn_r50_fpn_16xb4-1x_objects365v2.py | 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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/timm_example/retinanet_timm-efficientnet-b1_fpn_1x_coco.py | configs/timm_example/retinanet_timm-efficientnet-b1_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'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/timm_example/retinanet_timm-tv-resnet50_fpn_1x_coco.py | configs/timm_example/retinanet_timm-tv-resnet50_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'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | _base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-400e_coco.py | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-400e_coco.py | _base_ = './mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
train_dataloader = dict(dataset=dict(times=4 * 4))
param_scheduler = [
dict(
type='LinearLR',
star... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_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)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py | configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_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)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It c... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py | 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))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/crowddet/crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py | configs/crowddet/crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py | _base_ = './crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py'
model = dict(roi_head=dict(bbox_head=dict(with_refine=True)))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/crowddet/crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py | configs/crowddet/crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.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... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deformable_detr/deformable-detr-refine_r50_16xb2-50e_coco.py | configs/deformable_detr/deformable-detr-refine_r50_16xb2-50e_coco.py | _base_ = 'deformable-detr_r50_16xb2-50e_coco.py'
model = dict(with_box_refine=True)
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py | configs/deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py | _base_ = 'deformable-detr-refine_r50_16xb2-50e_coco.py'
model = dict(as_two_stage=True)
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py | configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
num_queries=300,
num_feature_levels=4,
with_box_refine=False,
as_two_stage=False,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/yolact/yolact_r50_8xb8-55e_coco.py | configs/yolact/yolact_r50_8xb8-55e_coco.py | _base_ = 'yolact_r50_1xb8-55e_coco.py'
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(lr=8e-3),
clip_grad=dict(max_norm=35, norm_type=2))
# learning rate
max_epochs = 55
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/yolact/yolact_r50_1xb8-55e_coco.py | configs/yolact/yolact_r50_1xb8-55e_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
# model settings
input_size = 550
model = dict(
type='YOLACT',
data_preprocessor=dict(
type='DetDataPreprocessor',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/yolact/yolact_r101_1xb8-55e_coco.py | configs/yolact/yolact_r101_1xb8-55e_coco.py | _base_ = './yolact_r50_1xb8-55e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_x101-32x4d-dconv-c4-c5_fpn_ms-2x_coco.py | configs/gfl/gfl_x101-32x4d-dconv-c4-c5_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
dcn=d... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_x101-32x4d_fpn_ms-2x_coco.py | configs/gfl/gfl_x101-32x4d_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
type='GFL',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_r50_fpn_1x_coco.py | configs/gfl/gfl_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_r50_fpn_ms-2x_coco.py | configs/gfl/gfl_r50_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_1x_coco.py'
max_epochs = 24
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py | configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False)... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/gfl/gfl_r101_fpn_ms-2x_coco.py | configs/gfl/gfl_r101_fpn_ms-2x_coco.py | _base_ = './gfl_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | configs/resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | configs/resnest/cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeS... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | configs/resnest/cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = './faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | configs/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py | _base_ = './cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/resnest/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | configs/resnest/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py | _base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py | configs/pvt/retinanet_pvt-l_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_large.pth')))
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = ... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b4.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
# optimi... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py | configs/pvt/retinanet_pvt-t_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 = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformer',
num_layers=[2, 2, ... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py | configs/pvt/retinanet_pvt-m_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_medium.pth')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b1.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b3.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b0_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 = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b2.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py | configs/pvt/retinanet_pvt-s_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_small.pth')))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py | configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 6, 40, 3],
mlp_ratios=(4, 4, 4, 4),
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b5.pth')),
neck=dict(in_channe... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/solo/solo_r18_fpn_8xb8-lsj-200e_coco.py | configs/solo/solo_r18_fpn_8xb8-lsj-200e_coco.py | _base_ = './solo_r50_fpn_8xb8-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]))
| python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/solo/decoupled-solo_r50_fpn_1x_coco.py | configs/solo/decoupled-solo_r50_fpn_1x_coco.py | _base_ = './solo_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOHead',
num_classes=80,
in_channels=256,
stacked_convs=7,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 96), (48, 192), (96, 384), (192, 76... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
open-mmlab/mmdetection | https://github.com/open-mmlab/mmdetection/blob/cfd5d3a985b0249de009b67d04f37263e11cdf3d/configs/solo/decoupled-solo-light_r50_fpn_3x_coco.py | configs/solo/decoupled-solo-light_r50_fpn_3x_coco.py | _base_ = './decoupled-solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64... | python | Apache-2.0 | cfd5d3a985b0249de009b67d04f37263e11cdf3d | 2026-01-04T14:42:57.197037Z | false |
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