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SotA/Methodology/16k

16k

996 benchmarks146 papers

Benchmarks

16k on nuScenes

NDS mAP mATE mASE mAOE mAVE mAAE AP(l)AP(m)AP(s)AP50 AP75 AP85 AR AR(l)AR(m)AR(s)MAP

16k on COCO test-dev

box mAP AP50 AP75 APL APM APS Params (M)GFLOPs Hardware Burden Operations per network pass

16k on COCO minival

box AP AP50 AP75 APL APS APM Params (M)

16k on Set14 - 4x upscaling

PSNR SSIM MOS LPIPS DISTS

16k on COCO (Common Objects in Context)

box AP FPS (V100, b=1)MAP AP 0.5 mPC [AP]rPC [%]GFlops Average Recall

16k on BSD100 - 4x upscaling

PSNR SSIM MOS LPIPS DISTS

16k on Urban100 - 4x upscaling

PSNR SSIM LPIPS DISTS Perceptual Index LR-PSNR

16k on GoPro

PSNR SSIM Params (M)FID LPIPS

16k on Manga109 - 4x upscaling

PSNR SSIM DISTS LPIPS LR-PSNR

16k on PASCAL VOC 2007

MAP FPS AP50 mPC [AP50]rPC [%]Unknown Recall WI A-OSE mAP@50 mAP@50-95 box AP

16k on COCO-O

Average mAP Effective Robustness

16k on Set5 - 2x upscaling

16k on Set14 - 2x upscaling

16k on ImageNet VID

16k on MS-COCO (10-shot)

16k on ScanNetV2

mAP@0.25 mAP@0.5

16k on COCO 2017 val

AP AP50 AP75 APL APM APS Param.

16k on MSCOCO

AP 0.5 AP mAP @0.5:0.95 Average mAP mAP mAP@50

16k on PASCAL VOC 2012 test

MAP Bounding Box AP

16k on Set5 - 3x upscaling

16k on DUTS-TE

MAE max F-measure S-Measure mean F-Measure mean E-Measure Weighted F-Measure

16k on BSD100 - 2x upscaling

16k on SUN-RGBD val

mAP@0.25 mAP@0.5 Inference Speed (s)MAP

16k on Urban100 - 2x upscaling

16k on KITTI Cars Moderate

AP Medium AP75 AP

16k on LVIS v1.0

AP novel-LVIS base training AP novel-Unrestricted open-vocabulary training box AP

16k on NJU2K

Average MAE S-Measure max F-Measure max E-Measure

16k on CAMO-FS

16k on ISTD+

RMSE PSNR SSIM LPIPS

16k on MS-COCO (30-shot)

16k on SRD

RMSE PSNR SSIM LPIPS

16k on DIS-VD

max F-Measure S-Measure weighted F-measure MAE E-measure HCE

16k on Set14 - 3x upscaling

16k on DIS-TE1

max F-Measure S-Measure weighted F-measure MAE E-measure HCE

16k on DIS-TE2

max F-Measure S-Measure weighted F-measure MAE HCE E-measure

16k on DIS-TE3

max F-Measure weighted F-measure MAE S-Measure E-measure HCE

16k on DIS-TE4

max F-Measure weighted F-measure MAE E-measure S-Measure HCE

16k on Urban100 - 3x upscaling

16k on BSD100 - 3x upscaling

16k on DIV2K val - 4x upscaling

PSNR SSIM LPIPS LRPSNR DISTS NIQE

16k on Manga109 - 2x upscaling

16k on QVHighlights

16k on CrowdHuman (full body)

16k on nuScenes Camera Only

NDS Future Frame

16k on DUT-OMRON

MAE S-Measure F-measure mean F-Measure mean E-Measure Weighted F-Measure

16k on CAMO

MAE Weighted F-Measure S-Measure E_{\phi}S_{\alpha}F_{\beta}

16k on Manga109 - 3x upscaling

16k on Artaxor

16k on CPPE-5

box AP AP50 AP75 APS APM APL

16k on FBMS-59

S-Measure MAX F-MEASURE AVERAGE MAE MAX E-MEASURE

16k on LVIS v1.0 val

AP box AP APr APc APf box APr AP50 AP75

16k on PCOD_1200

16k on SIP

Average MAE S-Measure max E-Measure max F-Measure

16k on UODD

16k on COCO 2017

mAP AP AP50 AP75 APM APM50 APM75 Mean mAP

16k on DIOR

16k on Manga109-s 15test

16k on Waymo 2D detection all_ns f0val

16k on ECSSD

MAE S-Measure F-measure mean F-Measure mean E-Measure Weighted F-Measure F-Score

16k on GRAZPEDWRI-DX

AP50 F1-score mAP Fracture Sensitivity

16k on HKU-IS

MAE S-Measure F-measure mean F-Measure mean E-Measure Weighted F-Measure F-Score

16k on HRSOD

S-Measure max F-Measure MAE mBA

16k on NLPR

S-Measure Average MAE max F-Measure max E-Measure

16k on PKU-DDD17-Car

16k on STERE

S-Measure Average MAE max F-Measure max E-Measure

16k on Cityscapes

16k on DES

S-Measure Average MAE max F-Measure max E-Measure

16k on PASCAL-S

MAE S-Measure F-measure mean F-Measure mean E-Measure Weighted F-Measure F-Score

16k on USB (Standard USB 1.0 protocol)

16k on Watercolor2k

16k on COD

S-Measure Weighted F-Measure MAE

16k on DAVIS-S

S-measure F-measure MAE mBA

16k on DSEC

16k on DWD

16k on SFCHD

mAP@0.5:0.95 mAP@0.50

16k on Set5 - 4x upscaling

16k on UHRSD

S-Measure max F-Measure MAE mBA

16k on DAVIS-2016

S-Measure AVERAGE MAE MAX E-MEASURE MAX F-MEASURE

16k on FFHQ 256 x 256 - 4x upscaling

PSNR SSIM FID MS-SSIM

16k on GEN1 Detection

16k on KITTI Cars Easy val

16k on KITTI Cars Moderate val

16k on KITTI-360

AP50 AP25 mAP@0.3

16k on LVIS v1.0 minival

16k on View-of-Delft (val)

16k on nuscenes Camera-Radar

16k on Clipark1k

16k on CoCA

S-measure max F-measure mean E-measure Mean F-measure max E-measure MAE

16k on CoSOD3k

max E-measure S-measure max F-measure MAE mean E-measure mean F-measure

16k on CoSal2015

max E-measure S-measure max F-measure MAE mean E-measure mean F-measure

16k on DeepFish

16k on DeepLesion

16k on Flickr1024 - 2x upscaling

16k on ISTD

MAE Balanced Error Rate

16k on KITTI Cars Hard val

16k on Middlebury - 2x upscaling

16k on Middlebury - 4x upscaling

16k on NEU-DET

16k on ViSal

S-Measure max E-measure Average MAE

16k on Wildtrack

MODA MODP Recall

16k on BurstSR

PSNR SSIM LPIPS

16k on DAIR-V2X-I

AP|R40(moderate)AP|R40(easy)AP|R40(hard)

16k on DAVSOD-easy35

S-Measure Average MAE max E-Measure max F-Measure

16k on DIOR-R

16k on FFHQ 1024 x 1024 - 4x upscaling

FID MS-SSIM PSNR SSIM

16k on HRSC2016

16k on IXI

PSNR 2x T2w PSNR 4x T2w SSIM 4x T2w SSIM for 2x T2w

16k on ImageNet

FID PSNR SSIM MAP

16k on KITTI2012 - 4x upscaling

16k on KITTI2015 - 2x upscaling

16k on KITTI2015 - 4x upscaling

16k on MS-COCO

16k on MultiviewX

MODA MODP Recall

16k on SeaDronesSee

mAP@0.5 mAP@0.50

16k on UA-DETRAC

16k on VOS-T

S-Measure max E-measure Average MAE

16k on CelebA

16k on Clipart1k

16k on Comic2k

16k on DAVSOD-Difficult20

S-Measure max E-measure Average MAE

16k on DAVSOD-Normal25

S-Measure max E-measure Average MAE

16k on Description Detection Dataset

Intra-scenario FULL mAP Intra-scenario PRES mAP Intra-scenario ABS mAP

16k on FFHQ 512 x 512 - 4x upscaling

PSNR SSIM MS-SSIM LLE FED FID LPIPS NIQE

16k on Flickr1024 - 4x upscaling

16k on MCL

S-Measure MAX E-MEASURE AVERAGE MAE MAX F-MEASURE

16k on ODinW Full-Shot 13 Tasks

16k on PROBA-V

Normalized cPSNR

16k on Rope3D

16k on SUN-RGBD

mAP@0.25 mAP@0.5 Inference Speed (s)

16k on SegTrack v2

S-Measure AVERAGE MAE max E-measure MAX F-MEASURE

16k on Set5 - 8x upscaling

16k on SyntheticBurst

PSNR SSIM LPIPS

16k on UAVDT

16k on UVSD

S-Measure max E-measure Average MAE

16k on Waymo Open Dataset

mAPH/L2 AP/L2 Latency, ms 3D mAPH Vehicle (Front Camera Only)AP

16k on KITTI Cars Hard

16k on LFSD

S-Measure Average MAE max E-Measure max F-Measure

16k on MS-COCO (1-shot)

16k on MS-COCO-2014

16k on NAO

mAP mAP w/o OOD mAR

16k on OVAD-Box benchmark

mean average precision

16k on PASCAL VOC 2012

16k on PASCAL VOC'07

16k on S3DIS

mAP@0.5 mAP@0.25

16k on SBU / SBU-Refine

Balanced Error Rate

16k on SOC

Average MAE S-Measure mean E-Measure

16k on SUN RGB-D

AP 0.5 AP@0.15 (10 / NYU-37)AP@0.15 (NYU-37)AP@0.15 (10 / PNet-30)

16k on Set14 - 8x upscaling

16k on TBBR

Average Recall@IoU:0.5-0.95

16k on TvSum

16k on UCF

Balanced Error Rate

16k on VggFace2 - 8x upscaling

16k on WebFace - 8x upscaling

16k on YouTube Highlights

16k on nuScenes LiDAR only

NDS mAP NDS (val)mAP (val)

16k on waymo cyclist

16k on waymo pedestrian

16k on AI-TOD

AP AP50 AP75 APvt APt APs APm mAP50 mAP@50-95

16k on Argoverse

16k on BSD100 - 8x upscaling

PSNR SSIM DISTS LPIPS LRPSNR NIQE

16k on BigDetection val

16k on CHAMELEON

S-measure weighted F-measure MAE

16k on Charades

16k on Cholec80

16k on EventPed

16k on InOutDoor

16k on KITTI Pedestrians Moderate

16k on NC4K

S-measure weighted F-measure MAE

16k on PeopleArt

mAP@0.5 mAP mAP@0.75 MAP

16k on STCrowd

16k on V2XSet

AP0.5 (Perfect)AP0.7 (Perfect)AP0.5 (Noisy)AP0.7 (Noisy)

16k on waymo vehicle

APH/L2 L1 mAP AP

16k on KITTI2012 - 2x upscaling

16k on CVCS

MODA (1m)MODP (1m)Precision (1m)Recall (1m)F1_score (1m)MODA (0.5m)F1_score (0.5m)

16k on CityStreet

MODA (2m)MODP (2m)Precision (2m)Recall (2m)F1_score (2m)

16k on DTTD-Mobile

ADD AUC ADD-S AUC

16k on GoogleEarth

Depth Error KID Camera Error FID

16k on KITTI Cars Easy

16k on KITTI Cyclists Moderate

16k on KITTI2012 - 2x upscaling

16k on LVIS v1.0 test-dev

AP AP50 AP75 APr APc APf

16k on Manga109 - 8x upscaling

16k on ODinW

16k on OPV2V

AP@0.7@Default AP@0.7@CulverCity AP50

16k on OVAD benchmark

mean average precision

16k on RGBD135

Average MAE S-Measure max F-Measure max E-Measure

16k on SKU-110K

16k on SOD4SB Private Test

16k on SOD4SB Public Test

16k on SimBEV

SDS mAP mATE mAOE mASE mAVE

16k on Urban100 - 8x upscaling

16k on V2X-SIM

mAP mATE mASE mAOE

16k on VisDrone-DET2019

AP50 AP APvt APt APs AP75 FPS APm

16k on iSAID

Average Precision

16k on ARKitScenes

mAP@0.25 mAP@0.5

16k on Aria Everyday Objects

16k on Aria Synthetic Environments

16k on CelebA-HQ 128x128

PSNR SSIM Consistency

16k on HICO-DET

16k on HIDE

16k on India Driving Dataset

16k on KITTI Cyclist Easy val

16k on KITTI Cyclist Hard val

16k on KITTI Cyclist Moderate val

16k on KITTI Pedestrian Easy val

16k on KITTI Pedestrian Hard

AP Hard Average Precision

16k on KITTI Pedestrian Hard val

16k on KITTI Pedestrian Moderate val

16k on MP-IDB

16k on MoCA-Mask

S-measure weighted F-measure MAE mDice mIoU

16k on TruckScenes

16k on Visual Genome

16k on WaterScenes

16k on 3D Object Detection on Argoverse2 Camera Only

16k on 3RScan

mAP@0.25 mAP@0.5

16k on AVD

16k on Argoverse-HD (Detection-Only, Test)

16k on Argoverse-HD (Full-Stack, Test)

16k on COD10K

E_{\phi}MAE S_{\alpha}F_{\beta}

16k on DOTA

16k on FAIR1M-2.0

16k on FlickrLogos-32

16k on General100 - 4x upscaling

LPIPS DISTS PSNR SSIM LRPSNR NIQE LR-PSNR

16k on Google Objectron

Average Precision at 0.5 3D IoU MPE AP at 15' Azimuth error AP at 10' Elevation error

16k on HIDE (trained on GOPRO)

16k on KITTI Pedestrian Easy

AP Easy Average Precision

16k on KITTI Pedestrian Moderate

AP Medium Average Precision

16k on LOL-Blur

Average PSNR SSIM LPIPS

16k on Manga109

Average Precision

16k on MultiScan

mAP@0.25 mAP@0.5

16k on ODinW-13

16k on ODinW-35

16k on OoDIS

16k on PASCAL VOC 2012, 60 proposals per image

16k on PIRM-test

16k on Pascal VOC to Clipart1K

16k on Replica

16k on SOD

16k on ScanNet++

mAP@0.25 mAP@0.5

16k on VEDAI

16k on VizDoom

16k on WiderPerson

16k on aiMotive Dataset

BEV AP@0.3 Highway BEV AP@0.3 Night BEV AP@0.3 Rain BEV AP@0.3 Urban

16k on xView

16k on 2x upscaling

#params (K)FLOPs(G)

16k on 3x upscaling

#params (K)FLOPs(G)

16k on 4x upscaling

#params (K)FLOPs(G)

16k on Argoverse-HD (Detection-Only, Val)

16k on Argoverse-HD (Full-Stack, Val)

16k on Argoverse2

16k on B100 - 2x upscaling

16k on B100 - 3x upscaling

16k on B100 - 4x upscaling

16k on BSDS100 - 2x upscaling

16k on COCO 2017 (Electronic, Indoor, Kitchen, Furniture)

16k on COCO 2017 (Outdoor, Accessories, Appliance, Truck)

Unknown Recall MAP WI A-OSE

16k on COCO 2017 (Sports, Food)

Unknown Recall MAP WI A-OSE

16k on COCO VOC to non-VOC

16k on CUFED5 - 4x upscaling

16k on Camouflaged Animal Dataset

S-measure weighted F-measure MAE mDice mIoU

16k on Chameleon

E_{\phi}F_{\beta}MAE S_{\alpha}

16k on Cityscapes test

mPC [AP]rPC [%]

16k on Cityscapes-to-Foggy Cityscapes

16k on DAIR-V2X

16k on DCM

Average Precision

16k on DIV2K val - 8x upscaling

LPIPS PSNR SSIM DISTS LRPSNR NIQE

16k on DIV8K val - 16x upscaling

LPIPS PSNR SSIM

16k on Drone vs Bird

AP50 AP50l AP50m AP50s

16k on GMVD

16k on General100 - 8x upscaling

LPIPS DISTS LRPSNR NIQE PSNR SSIM

16k on INI-30

Euclidean Distance

16k on IconArt

16k on IndustReal

16k on KITTI Cyclist Easy

16k on KITTI Cyclist Hard

16k on KITTI Cyclist Moderate

16k on ODinW Full-shot 35 Tasks

16k on ONCE

16k on OSM

Average FID KID

16k on Objects365

16k on OpenImages-v4

mask AP50 AP 0.5

16k on OpenImages-v6

16k on PASCAL Part 2010 - Animals

16k on PASCAL VOC 10%

16k on PASCAL VOC to Comic2k

16k on PASCAL VOC to Watercolor2k

16k on SA-Det-100k

AP AP50 AP75 APS APM APL

16k on SIXray

16k on SK-LARGE

16k on Sen2venus - 2x upscaling

16k on SpaceNet 2

F1 Score (Avg. over Cities)

16k on Spiideo SoccerNet SynLoc

mAP-LocSim FrameAccuracy F1

16k on Sun80 - 4x upscaling

16k on iCoSeg

max E-measure S-measure max F-measure MAE

16k on waymo all_ns

16k on A Dataset of Multispectral Potato Plants Images

Average IOU Dice Score

16k on A2D

16k on AODRaw

16k on AquaTrash

mean average precision

16k on BDD100K

16k on BDD100K val

16k on BSD100 - 16x upscaling

16k on BSD200 - 2x upscaling

16k on BSDS100 - 4x upscaling

16k on BSDS100 - 8x upscaling

16k on Bee4Exp Honeybee Detection

16k on C2A: Human Detection in Disaster Scenarios

16k on CASPAPaintings

16k on CISOL - Track A - TD-TSR

mAP@0.5:0.95:0.05

16k on CISOL - Track B - TSR-only

mAP@0.5:0.95:0.05

16k on COCO

boxAP boxAP50 boxAP75 box AP

16k on COCO val2017

Bounding Box AP

16k on COCO+

16k on COCO-Mix

unknown F1 score unknown-AP

16k on COCO-OOD

unknown F1 score unknown-AP

16k on Celeb-HQ 4x upscaling

16k on Chikusei Dataset

16k on CityPersons

16k on Cityscapes 3D

16k on Cityscapes to Foggy Cityscapes

16k on Clear Weather

mod. Car AP@.7IoU

16k on CoPerception-UAVs

16k on ConceptNet

16k on CrowdHuman

16k on DIV2K val - 16x upscaling

16k on DIV8K test - 16x upscaling

16k on DVSMOTION20

16k on DeepTrash

16k on Dense Fog

mod. Car AP@.5IoU mod. Cyclist AP@.25IoU mod. Pedestrian AP@.25IoU mod. mAP

16k on Drinking Waste Classification

16k on ELEVATER

16k on EPFL NIR-VIS

16k on EPIC KITCHENS-seen splits

16k on EPIC KITCHENS-unseen splits

16k on EPIC-KITCHENS-55

16k on EVD4UAV

Detection: Full (mAP@0.5)

16k on Extended TACO-1

16k on Extended TACO-7

16k on Extragalactic Planetary Nebulae

Number of sources

16k on FLIR

16k on GMOT-40

16k on GQA

16k on General-100 - 4x upscaling

DISTS LPIPS PSNR SSIM

16k on Heavy Snowfall

mod. Car AP@.7IoU

16k on HeiChole Benchmark

16k on INS Dataset

Average PSNR (dB)

16k on IRV2V

16k on ImageNet Detection

16k on KITTI

16k on KITTI 2012 - 2x upscaling

16k on KITTI 2012 - 4x upscaling

16k on KITTI 2015 - 2x upscaling

16k on KITTI 2015 - 4x upscaling

16k on KITTI Cyclists Easy

16k on KITTI Cyclists Hard

16k on KITTI Cyclists Moderate val

16k on KITTI Pedestrian

16k on KITTI Pedestrians Easy

16k on KITTI Pedestrians Hard

16k on KITTI Pedestrians Moderate val

16k on KITTI2012 - 2x scaling

16k on LDD

16k on LLVIP

16k on LeukemiaAttri

16k on Light Snowfall

mod. Car AP@.7IoU

16k on M5-Malaria Dataset

16k on MJU-Waste

16k on MS-COCO (5-shot)

16k on MS-COCO-2017

16k on MUSES: MUlti-SEnsor Semantic perception dataset

16k on Manga109 - 16x upscaling

16k on MoNuSeg 2018

16k on Multispectral Dataset

16k on NII-CU MAPD

mAP@0.5:0.95 AP@0.5 AP@0.75

16k on NJUD

16k on NYU Depth v2

16k on OLI2MSI - 3x upscaling

16k on PASCAL VOC

16k on PASCAL VOC 2007 (15+5)

16k on PASCAL VOC 2012 val

16k on PKU-DDD17-Car

16k on Real-world Dataset

16k on RealBlur-J

16k on RealBlur-R

16k on RealBlur-R(trained on GoPro)

16k on Rice Grain Disease Dataset

16k on SHEL5K

16k on SODA-D

16k on STARE

16k on Sen2venus - 4x upscaling

16k on Set14

16k on Set5 - 5x upscaling

16k on Set5 - 6x upscaling

16k on ShipSpotting

Frechet Inception Distance

16k on Songdo Vision

Precision Recall mAP@50 mAP@50-95

16k on SpaceNet 1

16k on TexBiG 2022 test

mAP@0.5:0.95:0.05

16k on TexBiG 2023 test

mAP@0.5:0.95:0.05

16k on TextZoom

Average Accuracy ASTER Overall Accuracy MORAN Overall Accuracy CRNN Overall Accuracy

16k on UAVVaste

16k on USC-GRAD-STDdb

16k on USR-248 - 4x upscaling

16k on Urban100 - 16x upscaling

16k on VME & CDSI

16k on Virtual KITTI 2

mAP@0.3 mAP@0.5

16k on VisDrone - 10% labeled data

16k on VisDrone - 5% labeled data

16k on VisDrone- 1% labeled data

16k on WLFW

16k on WSRD+

LPIPS PSNR SSIM

16k on Waymo 2D detection all_ns test

16k on YT-BB

16k on nuScenes Cars

AP 2.0m AP 0.5m AP 1.0m AP 4.0m ATE ASE AOE

16k on nuScenes-C

mean Corruption Error (mCE)

16k on nuScenes-F

AP AP50 AP75 AR ARI ARm ARs

16k on nuScenes-FB

AP AP50 AP75 AR ARI ARm ARs

16k on 01/01/19679682867

0S 10 Images, 1*1 Stitching, Exact Accuracy

16k on 10,000 People - Human Pose Recognition Data

16k on 100 sleep nights of 8 caregivers

16k on Barrett’s Esophagus

16k on Common Voice Estonian

16k on DOTA 1.0

16k on ExDark

16k on KITTI-C

mean Corruption Error (mCE)

16k on Rebar Head

16k on SAR-AIRcraft-1.0

16k on STN PLAD

16k on ^(#$!@#$)(()))******

16k on arabiska