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/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 214 | 34.833333 | 72 | py |
mmdetection | mmdetection-master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='DeformRoIPoolPack',
output_size=7,
output_cha... | 408 | 30.461538 | 56 | py |
mmdetection | mmdetection-master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
sty... | 557 | 31.823529 | 76 | py |
mmdetection | mmdetection-master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 211 | 34.333333 | 72 | py |
mmdetection | mmdetection-master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 210 | 34.166667 | 72 | py |
mmdetection | mmdetection-master/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| 240 | 29.125 | 72 | py |
mmdetection | mmdetection-master/configs/dcn/metafile.yml | Collections:
- Name: Deformable Convolutional Networks
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Deformable Convolution
Paper:
URL: https://arxiv.org/abs/1703.0621... | 9,291 | 33.03663 | 183 | yml |
mmdetection | mmdetection-master/configs/dcnv2/README.md | # DCNv2
> [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/abs/1811.11168)
<!-- [ALGORITHM] -->
## Abstract
The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavio... | 6,564 | 171.763158 | 1,323 | md |
mmdetection | mmdetection-master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_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)))
| 216 | 35.166667 | 74 | py |
mmdetection | mmdetection-master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
mmdetection | mmdetection-master/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
o... | 417 | 31.153846 | 56 | py |
mmdetection | mmdetection-master/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_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)))
fp16 = dict(loss_scale=512.)
| 242 | 29.375 | 74 | py |
mmdetection | mmdetection-master/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_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)))
| 212 | 34.5 | 74 | py |
mmdetection | mmdetection-master/configs/dcnv2/metafile.yml | Collections:
- Name: Deformable Convolutional Networks v2
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Deformable Convolution
Paper:
URL: https://arxiv.org/abs/1811.1... | 4,213 | 32.983871 | 182 | yml |
mmdetection | mmdetection-master/configs/ddod/README.md | # DDOD
> [Disentangle Your Dense Object Detector](https://arxiv.org/pdf/2107.02963.pdf)
<!-- [ALGORITHM] -->
## Abstract
Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the cu... | 3,435 | 106.375 | 1,513 | md |
mmdetection | mmdetection-master/configs/ddod/ddod_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='DDOD',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=... | 2,101 | 29.911765 | 79 | py |
mmdetection | mmdetection-master/configs/ddod/metafile.yml | Collections:
- Name: DDOD
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- DDOD
- FPN
- ResNet
Paper:
URL: https://arxiv.org/pdf/2107.02963.pdf
Titl... | 951 | 27 | 136 | yml |
mmdetection | mmdetection-master/configs/deepfashion/README.md | # DeepFashion
> [DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations](https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.html)
<!-- [DATASET] -->
## Abstract
Recent advances in clothes recognition have been driven by the construction... | 4,713 | 65.394366 | 1,083 | md |
mmdetection | mmdetection-master/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
runner = dict(type='Epo... | 351 | 31 | 78 | py |
mmdetection | mmdetection-master/configs/deformable_detr/README.md | # Deformable DETR
> [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159)
<!-- [ALGORITHM] -->
## Abstract
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However,... | 4,895 | 115.571429 | 679 | md |
mmdetection | mmdetection-master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DeformableDETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False)... | 6,666 | 36.455056 | 79 | py |
mmdetection | mmdetection-master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py | _base_ = 'deformable_detr_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(with_box_refine=True))
| 99 | 32.333333 | 50 | py |
mmdetection | mmdetection-master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py | _base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py'
model = dict(bbox_head=dict(as_two_stage=True))
| 103 | 33.666667 | 54 | py |
mmdetection | mmdetection-master/configs/deformable_detr/metafile.yml | Collections:
- Name: Deformable DETR
Metadata:
Training Data: COCO
Training Techniques:
- AdamW
- Multi Scale Train
- Gradient Clip
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
- Transformer
Paper:
URL: https://openreview.net/for... | 2,108 | 36 | 205 | yml |
mmdetection | mmdetection-master/configs/detectors/README.md | # DetectoRS
> [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/abs/2006.02334)
<!-- [ALGORITHM] -->
## Abstract
Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we e... | 7,232 | 102.328571 | 816 | md |
mmdetection | mmdetection-master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=d... | 851 | 28.37931 | 72 | py |
mmdetection | mmdetection-master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 382 | 28.461538 | 72 | py |
mmdetection | mmdetection-master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 1,053 | 30.939394 | 72 | py |
mmdetection | mmdetection-master/configs/detectors/detectors_htc_r101_20e_coco.py | _base_ = '../htc/htc_r101_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_s... | 920 | 30.758621 | 57 | py |
mmdetection | mmdetection-master/configs/detectors/detectors_htc_r50_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_ste... | 916 | 30.62069 | 57 | py |
mmdetection | mmdetection-master/configs/detectors/htc_r50_rfp_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
output_img=True),
neck=dict(
type='RFP',
rfp_steps=2,
aspp_out_channels=64,
aspp_dilations=(1, 3, 6, 1),
rfp_backbone=dic... | 714 | 27.6 | 57 | py |
mmdetection | mmdetection-master/configs/detectors/htc_r50_sac_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True)))
| 245 | 26.333333 | 50 | py |
mmdetection | mmdetection-master/configs/detectors/metafile.yml | Collections:
- Name: DetectoRS
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ASPP
- FPN
- RFP
- RPN
- ResNet
- RoIAlign
- SAC
... | 3,568 | 30.034783 | 153 | yml |
mmdetection | mmdetection-master/configs/detr/README.md | # DETR
> [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872)
<!-- [ALGORITHM] -->
## Abstract
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed co... | 3,299 | 85.842105 | 1,176 | md |
mmdetection | mmdetection-master/configs/detr/detr_r50_8x2_150e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='DETR',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm... | 5,858 | 37.801325 | 79 | py |
mmdetection | mmdetection-master/configs/detr/metafile.yml | Collections:
- Name: DETR
Metadata:
Training Data: COCO
Training Techniques:
- AdamW
- Multi Scale Train
- Gradient Clip
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
- Transformer
Paper:
URL: https://arxiv.org/abs/2005.12872
... | 971 | 27.588235 | 140 | yml |
mmdetection | mmdetection-master/configs/double_heads/README.md | # Double Heads
> [Rethinking Classification and Localization for Object Detection](https://arxiv.org/abs/1904.06493)
<!-- [ALGORITHM] -->
## Abstract
Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However... | 3,394 | 101.878788 | 1,223 | md |
mmdetection | mmdetection-master/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... | 845 | 34.25 | 77 | py |
mmdetection | mmdetection-master/configs/double_heads/metafile.yml | Collections:
- Name: Rethinking Classification and Localization for Object Detection
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- RPN
- ResNet
- R... | 1,359 | 31.380952 | 157 | yml |
mmdetection | mmdetection-master/configs/dyhead/README.md | # DyHead
> [Dynamic Head: Unifying Object Detection Heads with Attentions](https://arxiv.org/abs/2106.08322)
<!-- [ALGORITHM] -->
## Abstract
The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the... | 6,655 | 124.584906 | 1,098 | md |
mmdetection | mmdetection-master/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... | 3,608 | 30.938053 | 73 | py |
mmdetection | mmdetection-master/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... | 2,043 | 29.969697 | 79 | py |
mmdetection | mmdetection-master/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py | _base_ = '../_base_/default_runtime.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
type='ATSS',
backbone=dict(
type='SwinTransformer',
pretrain_img_size=384,
embed_dims=192,
dept... | 5,122 | 30.048485 | 129 | py |
mmdetection | mmdetection-master/configs/dyhead/metafile.yml | Collections:
- Name: DyHead
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 4x T4 GPUs
Architecture:
- ATSS
- DyHead
- FPN
- ResNet
- Deformable Convolution
- Pyramid C... | 2,473 | 31.12987 | 190 | yml |
mmdetection | mmdetection-master/configs/dynamic_rcnn/README.md | # Dynamic R-CNN
> [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://arxiv.org/abs/2004.06002)
<!-- [ALGORITHM] -->
## Abstract
Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from cr... | 3,034 | 96.903226 | 1,003 | md |
mmdetection | mmdetection-master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DynamicRoIHead',
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_... | 1,051 | 35.275862 | 77 | py |
mmdetection | mmdetection-master/configs/dynamic_rcnn/metafile.yml | Collections:
- Name: Dynamic R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Dynamic R-CNN
- FPN
- RPN
- ResNet
- RoIAlign
Paper:
U... | 1,083 | 29.111111 | 134 | yml |
mmdetection | mmdetection-master/configs/efficientnet/README.md | # EfficientNet
> [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v5)
<!-- [BACKBONE] -->
## Introduction
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are... | 3,341 | 106.806452 | 608 | md |
mmdetection | mmdetection-master/configs/efficientnet/metafile.yml | Models:
- Name: retinanet_effb3_fpn_crop896_8x4_1x_coco
In Collection: RetinaNet
Config: configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.5
Weights: https:/... | 814 | 39.75 | 182 | yml |
mmdetection | mmdetection-master/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k... | 3,016 | 30.757895 | 147 | py |
mmdetection | mmdetection-master/configs/empirical_attention/README.md | # Empirical Attention
> [An Empirical Study of Spatial Attention Mechanisms in Deep Networks](https://arxiv.org/abs/1904.05873)
<!-- [ALGORITHM] -->
## Abstract
Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors an... | 5,489 | 160.470588 | 1,128 | md |
mmdetection | mmdetection-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
mmdetection | mmdetection-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
... | 575 | 32.882353 | 72 | py |
mmdetection | mmdetection-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... | 403 | 27.857143 | 56 | py |
mmdetection | mmdetection-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... | 575 | 32.882353 | 72 | py |
mmdetection | mmdetection-master/configs/empirical_attention/metafile.yml | Collections:
- Name: Empirical Attention
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Deformable Convolution
- FPN
- RPN
- ResNet
- RoIAlign
... | 3,593 | 33.557692 | 196 | yml |
mmdetection | mmdetection-master/configs/fast_rcnn/README.md | # Fast R-CNN
> [Fast R-CNN](https://arxiv.org/abs/1504.08083)
<!-- [ALGORITHM] -->
## Abstract
This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compar... | 2,400 | 31.445946 | 598 | md |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
... | 1,710 | 33.918367 | 78 | py |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/fast_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rg... | 1,944 | 35.698113 | 78 | py |
mmdetection | mmdetection-master/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 147 | 23.666667 | 53 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/README.md | # Faster R-CNN
> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
<!-- [ALGORITHM] -->
## Abstract
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN ha... | 26,948 | 301.797753 | 1,311 | md |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 224 | 27.125 | 67 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
# u... | 1,526 | 29.54 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 24.875 | 61 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,388 | 33.725 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py | _base_ = './faster_rcnn_r50_caffe_c4_1x_coco.py'
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=... | 1,314 | 32.717949 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,304 | 33.342105 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | 1,448 | 32.697674 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 162 | 31.6 | 57 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... | 1,410 | 32.595238 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py | _base_ = 'faster_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=1000... | 372 | 22.3125 | 69 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
classes = ('person', 'bicycle', 'car')
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetectio... | 476 | 46.7 | 209 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
classes = ('person', )
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rc... | 460 | 45.1 | 209 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... | 1,554 | 32.085106 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 162 | 31.6 | 57 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img... | 1,505 | 30.375 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py | _base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(inter... | 380 | 22.8125 | 69 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 177 | 28.666667 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 177 | 28.666667 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))
| 207 | 28.714286 | 70 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
| 201 | 27.857143 | 64 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| 89 | 21.5 | 43 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))
| 201 | 27.857143 | 64 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
| 200 | 27.714286 | 63 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
| 91 | 22 | 77 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
| 118 | 38.666667 | 73 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_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(
test_cfg=dict(
rcnn=dict(
score_thr=0.05,
nms=dict(type='soft_nms', iou_threshold=0.5),
... | 347 | 25.769231 | 72 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | 561 | 30.222222 | 75 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN'... | 468 | 26.588235 | 77 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN'... | 1,923 | 29.539683 | 77 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py | _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | 421 | 27.133333 | 76 | py |
mmdetection | mmdetection-master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN'... | 468 | 26.588235 | 77 | py |