• cvpods_playground
  • ImageNet Classification
  • Self-supervised Learning
  • Object Detection
    • COCO
      • Faster R-CNN
      • RetinaNet
      • FCOS
      • ATSS
      • FreeAnchor
      • TridentNet
      • RepPoints
      • CenterNet
      • EfficientDet
      • YOLO
      • SSD
      • DETR
      • Sparse R-CNN
    • PASCAL VOC
    • WIDER FACE
    • CityPersons
    • CrowdHuman
  • Instance Segmentation
    • COCO
      • Mask R-CNN
      • TensorMask
      • CascadeRCNN
      • PointRend
      • SOLO
    • LVIS
    • CITYSCAPES
  • Semantic Segmentation
    • COCO
      • SemanticFPN
    • CITYSCAPES
      • PointRend
      • DynamicRouting
      • FCN
  • Panoptic Segmentation
    • COCO
      • PanopticFPN
  • Key-Points
    • COCO_PERSON
      • Keypoint-RCNN
  • 3D

Model Zoo

All experiments are conducted on servers with 8 NVIDIA V100 / 2080Ti GPUs (PCIE). The software in use were PyTorch 1.3, CUDA 10.1, cuDNN 7.6.3.

ImageNet Classification

Comming Soon.

Self-supervised Learning

Comming Soon.

Object Detection

COCO

Faster R-CNN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
FasterRCNN-R50-FPN	640-800	90k	0.225(2080ti)	2.82	38.1	LINK
FasterRCNN-R50-FPN-SyncBN	640-800	180k	0.546	5.23	39.9	LINK
FasterRCNN-ResNeSt50-FPN	800	90k	0.416	3.53	39.9	LINK
FasterRCNN-ResNeSt50-FPN-SyncBN	640-800	90k	0.661	5.35	42.5	LINK
FasterRCNN-MOBILENET-FPN	640-800	90k	0.279(2080ti)	3.47	29.27	LINK
FasterRCNN-MOBILENET-FPN-NoP2	640-800	90k	0.227(2080ti)	2.49	29.57	LINK

RetinaNet

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
RetinaNet-R50	800	90k	0.3593	3.85	35.9	LINK
RetinaNet-R50	640-800	90k	0.244	3.84	36.5	LINK
RetinaNet-R50	640-800	90k	0.344(2080ti)	3.96	37.2	LINK
RetinaNet-R50-DRLoss	800	90k	0.357(2080ti)	3.72	37.4	LINK
RetinaNet-MOBILENET	640-800	90k	0.266	4.46	28.5	LINK

FCOS

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
FCOS-R50-FPN	800	90k	0.334(2080ti)	3.09	38.8	LINK

ATSS

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
ATSS-R50-FPN	800	90k	0.340(2080ti)	3.09	39.3	LINK

FreeAnchor

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
FreeAnchor-R50-FPN	800	90k	0.353(2080ti)	4.08	38.3	LINK

TridentNet

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
TridentNet-R50-C4	800	90k	0.754(2080ti)	4.65	37.7	LINK

RepPoints

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
RepPoints-R50-FPN	800	90k	0.415(2080ti)	2.85	38.2	LINK

CenterNet

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
CenterNet-R18	512	126k	TBD	TBD	29.8	TBD )
CenterNet-R50	512	126k	TBD	TBD	34.9	TBD )
CenterNet-R101	512	126k	TBD	TBD	36.8	TBD )

EfficientDet

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
EffDet0-Effnet0-BiFPN	512	562k	0.540(2080ti)	5.77	32.6	LINK
EffDet0-Effnet0-BiFPN-SyncBN	512	562k	0.760(2080ti)	5.77	33.2	LINK
EffDet1-Effnet1-BiFPN	640	562k	0.782(v100)	23.18	38.1	LINK
EffDet1-Effnet1-BiFPN-SyncBN	640	562k	1.182(v100)	23.18	38.0	LINK

YOLO

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
YOLOv3-Darknet53-SyncBN	320-608	470k	0.729	7.45	37.5	LINK

SSD

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
SSD-VGG16	300	200k	0.442	1.93	23.6	LINK
SSD-VGG16-Expand	300	200k	0.448	1.93	24.9	LINK
SSD-VGG16	512	200k	0.487	4.37	26.7	LINK
SSD-VGG16-Expand	512	200k	0.491	4.37	29.0	LINK

DETR

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
DETR-R50-C5	480-800	150e	0.270(v100)	3.62	38.7	LINK

Sparse R-CNN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
SparseRCNN-R50-FPN	480-800	270k	0.627(2080ti)	4.11	43.2	LINK

PASCAL VOC

Name	input size	lr sched	train time (s/iter)	train mem (GB)	AP	AP50	AP75	Trained Model
FasterRCNN-R50-FPN	480-800	18k	0.377	2.82	54.2	82.1	59.3	LINK

WIDER FACE

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	Trained Model
RetinaNet-R50	600	45k	0.342	4.76	49.4	LINK
FCOS-R50-FPN	600	45k	0.382	5.75	50.8	LINK

CityPersons

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	MR	Trained Model
FasterRCNN-R50-FPN	640	9K	0.401	3.38	36.1	0.37	LINK
RetinaNet-R50	640	18k	0.349	2.97	33.6	0.42	LINK
FCOS-R50-FPN	640	9K	0.375	3.55	35.7	0.40	LINK

CrowdHuman

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	MR	Trained Model
FasterRCNN-R50-FPN	800	2.8K	0.856	4.80	84.1	0.481	LINK

Instance Segmentation

COCO

Mask R-CNN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
MaskRCNN-R50-C4	640-800	90k	0.609	5.04	36.8	32.2	LINK
MaskRCNN-R50-C4-SyncBN-ExtraNorm	640-800	90k	0.852	9.82	37.9	33.1	LINK
MaskRCNN-R50-C4-SyncBN	640-800	180k	0.837	9.82	39.9	34.5	LINK)
MaskRCNN-R50-C4-SyncBN-ExtraNorm	640-800	180k	0.853	9.82	40.1	34.7	LINK
MaskRCNN-R50-FPN	640-800	90k	0.297(2080ti)	3.36	38.5	35.2	LINK

TensorMask

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
TensorMask-R50-FPN	800	90k	0.788(2080ti)	7.83	37.5	32.3	LINK

CascadeRCNN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
CascadeRCNN-R50-FPN	800	90k	0.546	3.91	41.7	36.1	LINK

PointRend

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
PointRend-R50-FPN	640-800	90k	0.439	4.88	38.4	36.2	LINK
PointRend-R50-FPN	640-800	270k	0.416	4.88	41.1	38.2	LINK

SOLO

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
SOLO-R50-FPN	800	90k	0.970	6.99	33.1	32.7	LINK
SOLO-R50-FPN	640-800	270k	0.950	6.99	35.6	35.2	LINK
DecoupledSOLO-R50-FPN	800	90k	1.097	6.68	34.0	33.7	LINK
DecoupledSOLO-R50-FPN	640-800	270k	0.922	6.47	35.9	35.6	LINK

LVIS

Name	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	mask AP	Trained Model
MaskRCNN-R50-FPN	800	90k	0.486	5.26	20.3	21.0	LINK
MaskRCNN-R50-FPN-DataResampling	800	90k	0.500	5.26	23.0	23.1	LINK
MaskRCNN-R50-FPN-DataResampling	640-800	90k	0.485	5.25	24.1	24.7	LINK

CITYSCAPES

Name	input size	lr sched	train time (s/iter)	train mem (GB)	mask AP
MaskRCNN-R50-FPN	640-800	90k	0.737	5.21	37.4	LINK
PointRend-R50-FPN	800-1024	240k	0.746	8.21	36.0	LINK

Semantic Segmentation

COCO

SemanticFPN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	mIoU	Trained Model
SemanticFPN-R50-FPN	640-800	90k	0.285	6.16	40.3	LINK

CITYSCAPES

PointRend

Name	input size	lr sched	train time (s/iter)	train mem (GB)	mIoU	Trained Model
PointRend-R101-FPN	512-2048	65k	1.900	3.88	78.2	LINK

DynamicRouting

Name	input size	lr sched	train time (s/iter)	train mem (GB)	mIoU	Trained Model
Dynamic-A	512-2048	190k	0.736	8.74	75.7	LINK
Dynamic-B	512-2048	190k	0.706	8.74	75.3	LINK
Dynamic-C	512-2048	190k	0.717	8.74	76.2	LINK
Dynamic-Raw	512-2048	190k	0.757	8.73	76.5	LINK

FCN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	mIoU	Trained Model
FCN-Res101-s32	512-2048	65k	0.605	3.44	71.9	LINK
FCN-Res101-s16	512-2048	65k	0.593	3.41	73.5	LINK
FCN-Res101-s8	512-2048	65k	0.541	3.41	74.0	LINK

Panoptic Segmentation

COCO

PanopticFPN

Name	input size	lr sched	train time (s/iter)	train mem (GB)	PG	Trained Model
PanopticFPN-R50-FPN-800	800	90k	0.4842	4.74	39.4	LINK
PanopticFPN-R50-FPN-MS	640-800	90k	0.4657	4.74	39.5	LINK

Key-Points

COCO_PERSON

Keypoint-RCNN

Named	input size	lr sched	train time (s/iter)	train mem (GB)	box AP	kp AP
RCNN_R50_FPN	480-800	90k	0.4r0(2080Ti)	4.47	53.7	64.2

3D

Comming Soon.