Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
mmdetection | mmdetection-master/configs/paa/paa_r50_fpn_1.5x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
lr_config = dict(step=[12, 16])
runner = dict(type='EpochBasedRunner', max_epochs=18)
| 122 | 29.75 | 53 | py |
mmdetection | mmdetection-master/configs/paa/paa_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 2,120 | 28.873239 | 79 | py |
mmdetection | mmdetection-master/configs/paa/paa_r50_fpn_2x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 122 | 29.75 | 53 | py |
mmdetection | mmdetection-master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
... | 747 | 34.619048 | 77 | py |
mmdetection | mmdetection-master/configs/pafpn/README.md | # PAFPN
> [Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534)
<!-- [ALGORITHM] -->
## Abstract
The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-... | 3,233 | 91.4 | 1,037 | md |
mmdetection | mmdetection-master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
| 200 | 21.333333 | 56 | py |
mmdetection | mmdetection-master/configs/pafpn/metafile.yml | Collections:
- Name: PAFPN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- PAFPN
Paper:
URL: https://arxiv.org/abs/1803.01534
Title: 'Path Aggregation Network for ... | 1,165 | 28.897436 | 170 | yml |
mmdetection | mmdetection-master/configs/panoptic_fpn/README.md | # Panoptic FPN
> [Panoptic feature pyramid networks](https://arxiv.org/abs/1901.02446)
<!-- [ALGORITHM] -->
## Abstract
The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff clas... | 6,704 | 103.765625 | 1,184 | md |
mmdetection | mmdetection-master/configs/panoptic_fpn/metafile.yml | Collections:
- Name: PanopticFPN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- PanopticFPN
Paper:
URL: https://arxiv.org/pdf/1901.02446
Title: 'Panoptic feature ... | 2,903 | 33.571429 | 178 | yml |
mmdetection | mmdetection-master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 200 | 27.714286 | 61 | py |
mmdetection | mmdetection-master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py | _base_ = './panoptic_fpn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 208 | 28.857143 | 61 | py |
mmdetection | mmdetection-master/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=50,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='res2net50_v1b_26w_4s-3cf99910.pth')))
fp16 = dict(loss_scale='dynamic')
| 312 | 23.076923 | 61 | py |
mmdetection | mmdetection-master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
semantic_head=dict(
type='PanopticFPNHead',
num_things_classes=80,
num_stuff_cla... | 1,035 | 29.470588 | 73 | py |
mmdetection | mmdetection-master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train... | 1,933 | 30.193548 | 79 | py |
mmdetection | mmdetection-master/configs/pascal_voc/README.md | # Pascal VOC
> [The Pascal Visual Object Classes (VOC) Challenge](https://link.springer.com/article/10.1007/s11263-009-0275-4)
<!-- [DATASET] -->
## Abstract
The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning... | 6,642 | 161.02439 | 723 | md |
mmdetection | mmdetection-master/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to... | 2,590 | 30.597561 | 72 | py |
mmdetection | mmdetection-master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learn... | 506 | 32.8 | 79 | py |
mmdetection | mmdetection-master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', ... | 2,524 | 32.223684 | 79 | py |
mmdetection | mmdetection-master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actu... | 489 | 31.666667 | 77 | py |
mmdetection | mmdetection-master/configs/pascal_voc/ssd300_voc0712.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
0.9))))
# dataset settings
dat... | 2,409 | 31.133333 | 79 | py |
mmdetection | mmdetection-master/configs/pascal_voc/ssd512_voc0712.py | _base_ = 'ssd300_voc0712.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
... | 1,954 | 32.706897 | 79 | py |
mmdetection | mmdetection-master/configs/pisa/README.md | # PISA
> [Prime Sample Attention in Object Detection](https://arxiv.org/abs/1904.04821)
<!-- [ALGORITHM] -->
## Abstract
It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful s... | 11,300 | 220.588235 | 1,160 | md |
mmdetection | mmdetection-master/configs/pisa/metafile.yml | Collections:
- Name: PISA
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- PISA
- RPN
- ResNet
- RoIPool
Paper:
URL: https://arxiv.o... | 3,459 | 30.171171 | 158 | yml |
mmdetection | mmdetection-master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=20... | 926 | 28.903226 | 77 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per... | 933 | 29.129032 | 77 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
... | 922 | 28.774194 | 77 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per_img... | 929 | 29 | 77 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 265 | 32.25 | 73 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
type='PISARetinaHead',
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
| 272 | 33.125 | 73 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_ssd300_coco.py | _base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 247 | 26.555556 | 71 | py |
mmdetection | mmdetection-master/configs/pisa/pisa_ssd512_coco.py | _base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 247 | 26.555556 | 71 | py |
mmdetection | mmdetection-master/configs/point_rend/README.md | # PointRend
> [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
<!-- [ALGORITHM] -->
## Abstract
We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersa... | 4,070 | 118.735294 | 1,199 | md |
mmdetection | mmdetection-master/configs/point_rend/metafile.yml | Collections:
- Name: PointRend
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- PointRend
- FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1912.08193
... | 1,742 | 30.690909 | 166 | yml |
mmdetection | mmdetection-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
... | 1,453 | 31.311111 | 75 | py |
mmdetection | mmdetection-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 161 | 31.4 | 56 | py |
mmdetection | mmdetection-master/configs/pvt/README.md | # PVT
> [Pyramid vision transformer: A versatile backbone for dense prediction without convolutions](https://arxiv.org/abs/2102.12122)
<!-- [BACKBONE] -->
## Abstract
Although using convolutional neural networks (CNNs) as backbones achieves great successes in computer vision, this work investigates a simple backbon... | 9,123 | 156.310345 | 1,492 | md |
mmdetection | mmdetection-master/configs/pvt/metafile.yml | Models:
- Name: retinanet_pvt-t_fpn_1x_coco
In Collection: RetinaNet
Config: configs/pvt/retinanet_pvt-t_fpn_1x_coco.py
Metadata:
Training Memory (GB): 8.5
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resource... | 8,674 | 34.553279 | 155 | yml |
mmdetection | mmdetection-master/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')))
fp16 = dict(loss_scale=dict(init_scale=512))
| 283 | 34.5 | 66 | py |
mmdetection | mmdetection-master/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')))
| 239 | 33.285714 | 66 | py |
mmdetection | mmdetection-master/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')))
| 237 | 33 | 66 | py |
mmdetection | mmdetection-master/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, ... | 593 | 33.941176 | 77 | py |
mmdetection | mmdetection-master/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,
... | 618 | 33.388889 | 77 | py |
mmdetection | mmdetection-master/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]))
| 278 | 33.875 | 66 | py |
mmdetection | mmdetection-master/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]))
| 311 | 33.666667 | 66 | py |
mmdetection | mmdetection-master/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]))
| 312 | 33.777778 | 66 | py |
mmdetection | mmdetection-master/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... | 668 | 34.210526 | 70 | py |
mmdetection | mmdetection-master/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... | 701 | 34.1 | 70 | py |
mmdetection | mmdetection-master/configs/queryinst/README.md | # QueryInst
> [Instances as Queries](https://openaccess.thecvf.com/content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html)
<!-- [ALGORITHM] -->
## Abstract
We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest ... | 6,665 | 179.162162 | 994 | md |
mmdetection | mmdetection-master/configs/queryinst/metafile.yml | Collections:
- Name: QueryInst
Metadata:
Training Data: COCO
Training Techniques:
- AdamW
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- QueryInst
Paper:
URL: https://openaccess.thecvf.com/content/ICCV2021/pa... | 3,570 | 34.356436 | 223 | yml |
mmdetection | mmdetection-master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py | _base_ = './queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 233 | 28.25 | 76 | py |
mmdetection | mmdetection-master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py | _base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 214 | 25.875 | 61 | py |
mmdetection | mmdetection-master/configs/queryinst/queryinst_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='QueryInst',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 4,956 | 34.661871 | 79 | py |
mmdetection | mmdetection-master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py | _base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True,
rpn=None,
rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.... | 2,254 | 40 | 78 | py |
mmdetection | mmdetection-master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py | _base_ = './queryinst_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_... | 879 | 35.666667 | 77 | py |
mmdetection | mmdetection-master/configs/regnet/README.md | # RegNet
> [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)
<!-- [BACKBONE] -->
## Abstract
In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing o... | 29,864 | 243.795082 | 1,145 | md |
mmdetection | mmdetection-master/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py | _base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... | 534 | 28.722222 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_... | 2,005 | 30.34375 | 77 | py |
mmdetection | mmdetection-master/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py | _base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... | 533 | 28.666667 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py | _base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... | 535 | 28.777778 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py | _base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... | 534 | 28.722222 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... | 528 | 28.388889 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... | 1,920 | 32.12069 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py | _base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 140 | 34.25 | 53 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 1,888 | 29.467742 | 77 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... | 527 | 28.333333 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... | 529 | 28.444444 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... | 528 | 28.388889 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... | 760 | 27.185185 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
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... | 520 | 27.944444 | 72 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... | 2,015 | 33.169492 | 77 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py | _base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| 305 | 37.25 | 74 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.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(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... | 2,261 | 32.761194 | 77 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_400mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... | 759 | 27.148148 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 521 | 28 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... | 761 | 27.222222 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 522 | 28.055556 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... | 760 | 27.185185 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 521 | 28 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/metafile.yml | Models:
- Name: mask_rcnn_regnetx-3.2GF_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
Metadata:
Training Memory (GB): 5.0
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
... | 27,623 | 33.616541 | 200 | yml |
mmdetection | mmdetection-master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 520 | 27.944444 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/retinanet_regnetx-3.2GF_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(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... | 2,004 | 32.416667 | 73 | py |
mmdetection | mmdetection-master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | 520 | 27.944444 | 73 | py |
mmdetection | mmdetection-master/configs/reppoints/README.md | # RepPoints
> [RepPoints: Point Set Representation for Object Detection](https://arxiv.org/abs/1904.11490)
<!-- [ALGORITHM] -->
## Abstract
Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. Th... | 11,035 | 182.933333 | 1,061 | md |
mmdetection | mmdetection-master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True))
| 140 | 46 | 77 | py |
mmdetection | mmdetection-master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(
bbox_head=dict(transform_method='minmax', use_grid_points=True),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(
_delete_=True,
type='MaxIoUAssigner',
... | 452 | 31.357143 | 68 | py |
mmdetection | mmdetection-master/configs/reppoints/metafile.yml | Collections:
- Name: RepPoints
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Group Normalization
- FPN
- RepPoints
- ResNet
Paper:
URL: https:/... | 6,299 | 33.615385 | 214 | yml |
mmdetection | mmdetection-master/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
| 118 | 38.666667 | 61 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvis... | 340 | 36.888889 | 72 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 30.142857 | 61 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 2,065 | 29.382353 | 79 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
optimizer = dict(lr=0.01)
| 215 | 42.2 | 77 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 148 | 36.25 | 61 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 562 | 32.117647 | 76 | py |
mmdetection | mmdetection-master/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| 126 | 41.333333 | 63 | py |
mmdetection | mmdetection-master/configs/res2net/README.md | # Res2Net
> [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169)
<!-- [BACKBONE] -->
## Abstract
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronge... | 10,383 | 132.128205 | 1,236 | md |
mmdetection | mmdetection-master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 299 | 26.272727 | 64 | py |
mmdetection | mmdetection-master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 294 | 25.818182 | 62 | py |
mmdetection | mmdetection-master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 291 | 25.545455 | 62 | py |
mmdetection | mmdetection-master/configs/res2net/htc_r2_101_fpn_20e_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
# learning policy
lr_config = dict(step=[16, ... | 379 | 26.142857 | 62 | py |
mmdetection | mmdetection-master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| 287 | 25.181818 | 62 | py |