felixleungsc/paperswithcode-data-evaluation-tables

task_path stringlengths 3 199 ⌀	dataset stringlengths 1 128 ⌀	model_name stringlengths 1 223 ⌀	paper_url stringlengths 21 601 ⌀	metric_name stringlengths 1 50 ⌀	metric_value stringlengths 1 9.22k ⌀
16k > Object Detection > 3D Object Detection From Monocular Images	KITTI-360	MonoDETR	https://arxiv.org/abs/2203.13310v5	AP25	27.13
16k > Object Detection > 3D Object Detection From Monocular Images	KITTI-360	GrooMeD-NMS	https://arxiv.org/abs/2103.17202v1	AP50	0.17
16k > Object Detection > 3D Object Detection From Monocular Images	KITTI-360	GrooMeD-NMS	https://arxiv.org/abs/2103.17202v1	AP25	16.12
16k > Object Detection > One-Shot Object Detection	PASCAL VOC 2012 val	QDTrack	https://arxiv.org/abs/2006.06664v4	MAP	22.1
16k > Object Detection > One-Shot Object Detection	COCO (Common Objects in Context)	OWL-ViT (R50+H/32)	https://arxiv.org/abs/2205.06230v2	AP 0.5	41.8
16k > Object Detection > One-Shot Object Detection	COCO (Common Objects in Context)	DE-ViT	https://arxiv.org/abs/2309.12969v4	AP 0.5	28.4
16k > Object Detection > One-Shot Object Detection	COCO (Common Objects in Context)	One-Shot Object Detection	https://arxiv.org/abs/1911.12529v1	AP 0.5	22.0
16k > Object Detection > One-Shot Object Detection	COCO (Common Objects in Context)	Siamese Mask R-CNN	https://arxiv.org/abs/1811.11507v2	AP 0.5	16.3
16k > Object Detection > Surgical tool detection	HeiChole Benchmark	MoCo V2 Surg SSL - FCN head	https://arxiv.org/abs/2207.00449v3	mAP	66.9
16k > Object Detection > Surgical tool detection	Cholec80	MoCo V2 Surg SSL - FCN head	https://arxiv.org/abs/2207.00449v3	mAP	93.5
16k > Object Detection > Surgical tool detection	Cholec80	ConvLSTM tracker	http://arxiv.org/abs/1812.01366v2	mAP	92.9
16k > Object Detection > Surgical tool detection	Cholec80	MTRCNet-CL	https://arxiv.org/abs/1907.06099v1	mAP	89.1
16k > Object Detection > Surgical tool detection	Cholec80	FCN	http://arxiv.org/abs/1806.05573v2	mAP	87.4
16k > Object Detection > Surgical tool detection	Cholec80	EndoNet	http://arxiv.org/abs/1602.03012v2	mAP	81.0
16k > Object Detection > Surgical tool detection	Cholec80	ToolNet	http://arxiv.org/abs/1602.03012v2	mAP	80.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	MM-Grounding-DINO	https://arxiv.org/abs/2401.02361v2	Intra-scenario FULL mAP	22.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	MM-Grounding-DINO	https://arxiv.org/abs/2401.02361v2	Intra-scenario PRES mAP	21.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	MM-Grounding-DINO	https://arxiv.org/abs/2401.02361v2	Intra-scenario ABS mAP	26.0
16k > Object Detection > Described Object Detection	Description Detection Dataset	FIBER-B	https://arxiv.org/abs/2206.07643v2	Intra-scenario FULL mAP	22.7
16k > Object Detection > Described Object Detection	Description Detection Dataset	FIBER-B	https://arxiv.org/abs/2206.07643v2	Intra-scenario PRES mAP	21.5
16k > Object Detection > Described Object Detection	Description Detection Dataset	FIBER-B	https://arxiv.org/abs/2206.07643v2	Intra-scenario ABS mAP	26.0
16k > Object Detection > Described Object Detection	Description Detection Dataset	OFA-DOD-base	https://arxiv.org/abs/2307.12813v2	Intra-scenario FULL mAP	21.6
16k > Object Detection > Described Object Detection	Description Detection Dataset	OFA-DOD-base	https://arxiv.org/abs/2307.12813v2	Intra-scenario PRES mAP	23.7
16k > Object Detection > Described Object Detection	Description Detection Dataset	OFA-DOD-base	https://arxiv.org/abs/2307.12813v2	Intra-scenario ABS mAP	15.4
16k > Object Detection > Described Object Detection	Description Detection Dataset	GLIP-T	https://arxiv.org/abs/2112.03857v2	Intra-scenario FULL mAP	19.1
16k > Object Detection > Described Object Detection	Description Detection Dataset	GLIP-T	https://arxiv.org/abs/2112.03857v2	Intra-scenario PRES mAP	18.3
16k > Object Detection > Described Object Detection	Description Detection Dataset	GLIP-T	https://arxiv.org/abs/2112.03857v2	Intra-scenario ABS mAP	21.5
16k > Object Detection > Described Object Detection	Description Detection Dataset	UNINEXT-large	https://arxiv.org/abs/2303.06674v2	Intra-scenario FULL mAP	17.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	UNINEXT-large	https://arxiv.org/abs/2303.06674v2	Intra-scenario PRES mAP	18.6
16k > Object Detection > Described Object Detection	Description Detection Dataset	UNINEXT-large	https://arxiv.org/abs/2303.06674v2	Intra-scenario ABS mAP	15.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	SPHINX-7B	https://arxiv.org/abs/2311.07575v1	Intra-scenario FULL mAP	10.6
16k > Object Detection > Described Object Detection	Description Detection Dataset	SPHINX-7B	https://arxiv.org/abs/2311.07575v1	Intra-scenario PRES mAP	11.4
16k > Object Detection > Described Object Detection	Description Detection Dataset	SPHINX-7B	https://arxiv.org/abs/2311.07575v1	Intra-scenario ABS mAP	7.9
16k > Object Detection > Described Object Detection	Description Detection Dataset	OWL-ViT-base	https://arxiv.org/abs/2205.06230v2	Intra-scenario FULL mAP	8.6
16k > Object Detection > Described Object Detection	Description Detection Dataset	OWL-ViT-base	https://arxiv.org/abs/2205.06230v2	Intra-scenario PRES mAP	8.5
16k > Object Detection > Described Object Detection	Description Detection Dataset	OWL-ViT-base	https://arxiv.org/abs/2205.06230v2	Intra-scenario ABS mAP	8.8
16k > Object Detection > Described Object Detection	Description Detection Dataset	CORA-R50	https://arxiv.org/abs/2303.13076v1	Intra-scenario FULL mAP	6.2
16k > Object Detection > Described Object Detection	Description Detection Dataset	CORA-R50	https://arxiv.org/abs/2303.13076v1	Intra-scenario PRES mAP	6.7
16k > Object Detection > Described Object Detection	Description Detection Dataset	CORA-R50	https://arxiv.org/abs/2303.13076v1	Intra-scenario ABS mAP	5.0
16k > Object Detection > Body Detection	Manga109	DASS-Detector (YOLOX XL)	https://arxiv.org/abs/2211.10641v2	Average Precision	87.98
16k > Object Detection > Body Detection	DCM	DASS-Detector (YOLOX XL)	https://arxiv.org/abs/2211.10641v2	Average Precision	86.14
16k > Object Detection > Body Detection	DCM	DASS-Detector (YOLOX Tiny)	https://arxiv.org/abs/2211.10641v2	Average Precision	87.06
16k > Object Detection > Body Detection	Clipart1k	DASS-Detector (YOLOX XL)	https://arxiv.org/abs/2211.10641v2	MAP	83.59
16k > Object Detection > Body Detection	Watercolor2k	DASS-Detector (YOLOX XL)	https://arxiv.org/abs/2211.10641v2	MAP	89.81
16k > Object Detection > Body Detection	Comic2k	DASS-Detector (YOLOX XL)	https://arxiv.org/abs/2211.10641v2	MAP	73.65
16k > Object Detection > Pupil Detection	INI-30	CNN	https://arxiv.org/abs/2312.00425v2	Euclidean Distance	0.5
16k > Object Detection > Pupil Detection	INI-30	TinyissimoV8	https://arxiv.org/abs/2312.00425v2	Euclidean Distance	1.75
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	YONOD + CPPM (RGB + Depth)	https://arxiv.org/abs/2207.01071v2	AP 0.5	58.1
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	Frustum Pointnet (RGB)	http://arxiv.org/abs/1711.08488v2	AP 0.5	56.8
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	simCrossTrans with Swin Transformer (Point Cloud only)	https://arxiv.org/abs/2203.10456v1	AP 0.5	55.8
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	2D - Driven (RGB)	http://openaccess.thecvf.com/content_iccv_2017/html/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.html	AP 0.5	49.7
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	Frustum VoxNet (RGB)	https://arxiv.org/abs/1910.05483v3	AP 0.5	47.9
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	RGB-D RCNN (RGB + Depth)	http://arxiv.org/abs/1407.5736v1	AP 0.5	44.2
16k > Object Detection > Object Detection In Indoor Scenes	SUN RGB-D	Frustum VoxNet (Depth only)	https://arxiv.org/abs/1910.05483v3	AP 0.5	42.8
16k > Object Detection > Weakly Supervised 3D Detection	KITTI-360	VSRD-MonoDETR	https://arxiv.org/abs/2404.00149v1	mAP@0.3	58.40
16k > Object Detection > Weakly Supervised 3D Detection	KITTI-360	Auto-Labels	https://arxiv.org/abs/1911.11288v2	mAP@0.3	48.16
16k > Object Detection > Weakly Supervised 3D Detection	KITTI-360	WeakM3D	https://arxiv.org/abs/2203.08332v1	mAP@0.3	29.89
16k > Object Detection > Object Skeleton Detection	SK-LARGE	DeepFlux	http://arxiv.org/abs/1811.12608v1	F-Measure	0.732
16k > Object Detection > Object Skeleton Detection	SK-LARGE	Hi-Fi	http://arxiv.org/abs/1801.01849v4	F-Measure	0.724
16k > Object Detection > Semantic Part Detection	PASCAL Part 2010 - Animals	Attention-based Joint Detection of Object and Semantic Part	https://arxiv.org/abs/2007.02419v1	mAP@0.5	52.0
16k > Object Detection > Malaria Falciparum Detection	M5-Malaria Dataset	YOLO Para	https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor	AP	71.0
16k > Object Detection > Malaria Falciparum Detection	MP-IDB	YOLO Para	https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor	AP	86.5
16k > Object Detection > Malaria Vivax Detection	MP-IDB	YOLO Para	https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor	AP	88.3
16k > Object Detection > Malaria Malariae Detection	MP-IDB	YOLO Para	https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor	AP	94.9
16k > Object Detection > Malaria Ovale Detection	MP-IDB	YOLO Para	https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor	AP	95.1
16k > Image Super-Resolution	Chikusei Dataset	DIP-HyperKite (ours)	https://arxiv.org/abs/2107.02630v1	PSNR	43.53
16k > Image Super-Resolution	Urban100 - 8x upscaling	DRLN+	https://arxiv.org/abs/1906.12021v2	PSNR	23.24
16k > Image Super-Resolution	Urban100 - 8x upscaling	DRLN+	https://arxiv.org/abs/1906.12021v2	SSIM	0.6523
16k > Image Super-Resolution	Urban100 - 8x upscaling	DBPN-RES-MR64-3	https://arxiv.org/abs/1904.05677v2	PSNR	23.2
16k > Image Super-Resolution	Urban100 - 8x upscaling	DBPN-RES-MR64-3	https://arxiv.org/abs/1904.05677v2	SSIM	0.652
16k > Image Super-Resolution	Urban100 - 8x upscaling	HAN+	https://arxiv.org/abs/2008.08767v1	PSNR	23.20
16k > Image Super-Resolution	Urban100 - 8x upscaling	HAN+	https://arxiv.org/abs/2008.08767v1	SSIM	0.6518
16k > Image Super-Resolution	Urban100 - 8x upscaling	HBPN	https://arxiv.org/abs/1906.06874v2	PSNR	23.04
16k > Image Super-Resolution	Urban100 - 8x upscaling	HBPN	https://arxiv.org/abs/1906.06874v2	SSIM	0.647
16k > Image Super-Resolution	Urban100 - 8x upscaling	ABPN	https://arxiv.org/abs/1910.04476v1	PSNR	23.04
16k > Image Super-Resolution	Urban100 - 8x upscaling	ABPN	https://arxiv.org/abs/1910.04476v1	SSIM	0.641
16k > Image Super-Resolution	Set5 - 6x upscaling	HyperRes	https://arxiv.org/abs/2206.05970v3	PSNR	24.92
16k > Image Super-Resolution	Set5 - 6x upscaling	HyperRes	https://arxiv.org/abs/2206.05970v3	SSIM	0.71
16k > Image Super-Resolution	BSD200 - 2x upscaling	CSRCNN	https://arxiv.org/abs/2008.10329v2	PSNR	32.92
16k > Image Super-Resolution	BSD200 - 2x upscaling	CSRCNN	https://arxiv.org/abs/2008.10329v2	SSIM	0.9122
16k > Image Super-Resolution	Middlebury - 4x upscaling	PASSRnet	http://arxiv.org/abs/1903.05784v3	PSNR	28.63
16k > Image Super-Resolution	Manga109 - 16x upscaling	ABPN	https://arxiv.org/abs/1910.04476v1	PSNR	21.25
16k > Image Super-Resolution	Manga109 - 16x upscaling	ABPN	https://arxiv.org/abs/1910.04476v1	SSIM	0.673
16k > Image Super-Resolution	BSD100 - 2x upscaling	WaveMixSR-V2	https://arxiv.org/abs/2409.10582v3	PSNR	33.12
16k > Image Super-Resolution	BSD100 - 2x upscaling	WaveMixSR-V2	https://arxiv.org/abs/2409.10582v3	SSIM	0.9326
16k > Image Super-Resolution	BSD100 - 2x upscaling	WaveMixSR	https://arxiv.org/abs/2307.00430v1	PSNR	33.08
16k > Image Super-Resolution	BSD100 - 2x upscaling	WaveMixSR	https://arxiv.org/abs/2307.00430v1	SSIM	0.9322
16k > Image Super-Resolution	BSD100 - 2x upscaling	DRCT-L	https://arxiv.org/abs/2404.00722v5	PSNR	32.90
16k > Image Super-Resolution	BSD100 - 2x upscaling	DRCT-L	https://arxiv.org/abs/2404.00722v5	SSIM	0.9078
16k > Image Super-Resolution	BSD100 - 2x upscaling	HMA†	https://arxiv.org/abs/2405.05001v1	PSNR	32.79
16k > Image Super-Resolution	BSD100 - 2x upscaling	HMA†	https://arxiv.org/abs/2405.05001v1	SSIM	0.9071
16k > Image Super-Resolution	BSD100 - 2x upscaling	Hi-IR-L	https://arxiv.org/abs/2411.18588v1	PSNR	32.77
16k > Image Super-Resolution	BSD100 - 2x upscaling	Hi-IR-L	https://arxiv.org/abs/2411.18588v1	SSIM	0.9092
16k > Image Super-Resolution	BSD100 - 2x upscaling	DRCT	https://arxiv.org/abs/2404.00722v5	PSNR	32.75
16k > Image Super-Resolution	BSD100 - 2x upscaling	DRCT	https://arxiv.org/abs/2404.00722v5	SSIM	0.9071
16k > Image Super-Resolution	BSD100 - 2x upscaling	HAT-L	https://arxiv.org/abs/2205.04437v3	PSNR	32.74
16k > Image Super-Resolution	BSD100 - 2x upscaling	HAT-L	https://arxiv.org/abs/2205.04437v3	SSIM	0.9066
16k > Image Super-Resolution	BSD100 - 2x upscaling	HAT_FIR	https://arxiv.org/abs/2208.11247v3	PSNR	32.71
16k > Image Super-Resolution	BSD100 - 2x upscaling	HAT	https://arxiv.org/abs/2205.04437v3	PSNR	32.69
16k > Image Super-Resolution	BSD100 - 2x upscaling	HAT	https://arxiv.org/abs/2205.04437v3	SSIM	0.9060

16k > Object Detection > 3D Object Detection From Monocular Images

KITTI-360

MonoDETR

https://arxiv.org/abs/2203.13310v5

AP25

27.13

16k > Object Detection > 3D Object Detection From Monocular Images

KITTI-360

GrooMeD-NMS

https://arxiv.org/abs/2103.17202v1

AP50

0.17

16k > Object Detection > 3D Object Detection From Monocular Images

KITTI-360

GrooMeD-NMS

https://arxiv.org/abs/2103.17202v1

AP25

16.12

16k > Object Detection > One-Shot Object Detection

PASCAL VOC 2012 val

QDTrack

https://arxiv.org/abs/2006.06664v4

MAP

22.1

16k > Object Detection > One-Shot Object Detection

COCO (Common Objects in Context)

OWL-ViT (R50+H/32)

https://arxiv.org/abs/2205.06230v2

AP 0.5

41.8

16k > Object Detection > One-Shot Object Detection

COCO (Common Objects in Context)

DE-ViT

https://arxiv.org/abs/2309.12969v4

AP 0.5

28.4

16k > Object Detection > One-Shot Object Detection

COCO (Common Objects in Context)

One-Shot Object Detection

https://arxiv.org/abs/1911.12529v1

AP 0.5

22.0

16k > Object Detection > One-Shot Object Detection

COCO (Common Objects in Context)

Siamese Mask R-CNN

https://arxiv.org/abs/1811.11507v2

AP 0.5

16.3

16k > Object Detection > Surgical tool detection

HeiChole Benchmark

MoCo V2 Surg SSL - FCN head

https://arxiv.org/abs/2207.00449v3

mAP

66.9

16k > Object Detection > Surgical tool detection

Cholec80

MoCo V2 Surg SSL - FCN head

https://arxiv.org/abs/2207.00449v3

mAP

93.5

16k > Object Detection > Surgical tool detection

Cholec80

ConvLSTM tracker

http://arxiv.org/abs/1812.01366v2

mAP

92.9

16k > Object Detection > Surgical tool detection

Cholec80

MTRCNet-CL

https://arxiv.org/abs/1907.06099v1

mAP

89.1

16k > Object Detection > Surgical tool detection

Cholec80

FCN

http://arxiv.org/abs/1806.05573v2

mAP

87.4

16k > Object Detection > Surgical tool detection

Cholec80

EndoNet

http://arxiv.org/abs/1602.03012v2

mAP

81.0

16k > Object Detection > Surgical tool detection

Cholec80

ToolNet

http://arxiv.org/abs/1602.03012v2

mAP

80.9