List your company

Instance Segmentation on LVIS 0.5 (val)

22.1APrDe-confound-TDE
1.7167.00812.317.592Mar 11, 2020May 14, 2020Jul 17, 2020Sep 19, 2020Nov 22, 2020Jan 25, 2021Mar 30, 2021

Evaluation results

MethodLinks
De-confound-TDEFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.0922.128.44330.62930.3————————
Capsule-TDEFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.0921.128.442.130.829.729.6————————
Cosine-TDEFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.0920.828.142.630.228.730.3————————
Forest R-CNNBackbone=ResNet-101-FPN, multi-scale training=true2020.0820.228.244.329.729.629.6213642—————
Forest R-CNNBackbone=ResNet-101-FPN2020.0820.126.942.228.427.928.323.643.551.8—————
Forest R-CNNBackbone=ResNet-50-FPN, multi-scale training=true2020.0819.726.74228.827.528.520.334.839.7—————
BALMS2020.0719.6———28.927.5—27——————
Forest R-CNNBackbone=ResNet-50-FPN2020.0818.325.640.327.126.427.618.532.741.1—————
GroupSoftmaxPre-Train=COCO, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.031826.3——26.928.7————————
SimCalPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.0316.423.4——22.527.2————————
SOLOv2backbone=Res-101-FPN2020.0316.326.8——27.630.116.835.847—————
SOLOv2backbone=Res-101 FPN2020.0316.326.8——27.630.116.835.847—————
SOLOv2Backbone=ResNet-101-FPN, multi-scale training=true, image resampling=true2020.0816.326.8——27.630.116.835.847—————
Balanced Softmaxvariant=multiple binary logistic regression2020.0716.2———27.328.8—26.3——————
DisAlignPre-Train=COCO, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Cosine2021.0316.227.9——29.330.8————————
DisAlignPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Cosine2021.0314.926.3——27.629.2————————
LDAM2020.0714.6———25.326.3—24.1——————
Equalization Loss2020.0714.6———27.326.6—25.2——————
EQLBackbone=ResNet-101-FPN2020.0814.624.838.426.826.726.4————————
RFSPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.0314.624.3——2428.5————————
Mask-RCNNbackbone=Res-50-FPN2020.0314.524.4——21.328.4————————
Mask RCNNbackbone=Res-50 FPN2020.0314.524.4——24.328.4————————
LWS2020.0714.4———24.426.8—23.8——————
Softmax2020.0713.6———2427.3—23.7——————
SOLOv2backbone=Res-50-FPN2020.0313.425.5——26.628.915.934.644.9—————
SOLOv2backbone=Res-50 FPN2020.0313.425.5——26.628.915.934.644.9—————
Repeat Factor SamplingBackbone=ResNet-50-FPN2020.0813.423.2——23.227.1————————
SOLOv2Backbone=ResNet-50-FPN, multi-scale training=true, image resampling=true2020.0813.425.5——26.628.915.934.644.9—————
Focal Loss2020.0712.8———23.527.5—23.4——————
Sigmoid2020.0712.7———2427.3—23.6——————
Repeat Factor SamplingBackbone=ResNet-50, Architecture=Mask R-CNN2020.0312.221.334.92221.524.715.227.136.1—————
Mask-RCNN*backbone=Res-50-FPN, training schedule=3x, re-implementation=true2020.0312.124.6——25.828.118.731.238.2—————
Mask RCNN*-3xbackbone=Res-50 FPN2020.0312.124.6——25.828.118.731.238.2—————
BaselinePre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Cosine2021.0311.523.8——23.728.9————————
Class Balanced Loss2020.0711.4———23.827.3—23.3——————
De-confoundFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.0911.425.738.527.826.130.9————————
EQLBackbone=ResNet-50, Architecture=Mask R-CNN2020.0311.322.83624.424.725.116.329.738.2—————
EQLBackbone=ResNet-50-FPN2020.0811.322.83624.424.725.116.329.738.2—————
EQLPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.0311.322.8——24.725.1————————
BaselinePre-Train=COCO, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Cosine2021.031125.3——25.530.7————————
EQLFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.0910.324.937.926.727.327.8————————
DisAlignPre-Train=COCO, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Linear2021.039.425.7——27.629.7————————
Focal LossBackbone=ResNet-50, Architecture=Mask R-CNN2020.039.32134.222.12125.815.627.835.4—————
CosineFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.099.32537.72725.530.8————————
Focal LossBackbone=ResNet-50-FPN2020.089.32134.222.12125.815.627.835.4—————
Focal LossPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.039.321——2125.8————————
CapsuleFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.098.525.437.827.426.431————————
DisAlignPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Linear2021.038.524.3——26.328.1————————
Class-balanced LossBackbone=ResNet-50, Architecture=Mask R-CNN2020.038.220.933.822.221.225.715.628.135.3—————
Class-balanced LossBackbone=ResNet-50-FPN2020.088.220.933.822.221.225.715.628.135.3—————
Class-aware SamplingBackbone=ResNet-50, Architecture=Mask R-CNN2020.037.318.531.118.919.321.913.324.330.5—————
Class-aware SamplingBackbone=ResNet-50-FPN2020.087.318.531.118.919.321.913.324.330.5—————
Sigmoid LossBackbone=ResNet-50, Architecture=Mask R-CNN2020.037.220.132.721.219.925.41527.134.6—————
Softmax LossBackbone=ResNet-50, Architecture=Mask R-CNN2020.034.520.232.621.320.825.615.527.734.4—————
BaselinePre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Linear2021.033.721.2——21.628.4————————
Focal LossFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.093.221.132.122.621.128.3————————
BaselinePre-Train=COCO, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.0001, Classifier=Linear2021.032.823.9——23.430.5————————
BaselineFramework=Cascade Mask R-CNN, Backbone=R101-FPN2020.092.522.633.524.42330.2————————
BlendMaskBackbone=R-101+dcni3, resolution=56 × 562020.01—44.1————————————
BMask R-CNNTraining dataset=COCO, Inputs=Ground truth boxes2021.03—————————57.846.150.856.340.7
LSTBackbone=ResNet-50-FPN2020.08—2336.724.8——————————
LSTPre-Train=ImageNet, Backbone=ResNet-50, Framework=Mask-R-CNN-FPN, Score threshold=0.00012021.03—23————————————
Mask R-CNNBackbone=X101-FPN, resolution=28 × 282020.01—40.7————————————
Mask R-CNNTraining dataset=COCO, Inputs=Ground truth boxes2021.03—————————51.538.345.448.729.2
PointRendBackbone=X101-FPN, resolution=224 x 2242020.01—43.4————————————
PointRendTraining dataset=COCO, Inputs=Ground truth boxes2021.03—————————56.845.949.65642.2