Robotic Grasping on Cornell Grasp Dataset

Metric: 5 fold cross validation (higher is better)

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#	Model↕	5 fold cross validation▼	Extra Data	Paper	Date↕	Code
1	grasp_det_seg_cnn (rgb only, IW split)	98.2	No	End-to-end Trainable Deep Neural Network for Rob...	2021-07-12	Code
2	GR-ConvNet	97.7	No	Antipodal Robotic Grasping using Generative Resi...	2019-09-11	Code
3	ResNet50 multi-grasp predictor	96	No	-	-	Code
4	Multi-Modal Grasp Predictor	89.21	No	Robotic Grasp Detection using Deep Convolutional...	2016-11-24	-
5	AlexNet, MultiGrasp	88	No	Real-Time Grasp Detection Using Convolutional Ne...	2014-12-09	Code
6	GGCNN	73	No	-	-	Code
7	Fast Search	60.5	No	Deep Learning for Detecting Robotic Grasps	2013-01-16	-

#1grasp_det_seg_cnn (rgb only, IW split)SOTA
98.2
5 fold cross validation· 2021-07-12
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB Code
#2GR-ConvNetSOTA
97.7
5 fold cross validation· 2019-09-11
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network Code
#3ResNet50 multi-grasp predictor
96
5 fold cross validation
No paperCode
#4Multi-Modal Grasp PredictorSOTA
89.21
5 fold cross validation· 2016-11-24
Robotic Grasp Detection using Deep Convolutional Neural Networks
#5AlexNet, MultiGraspSOTA
88
5 fold cross validation· 2014-12-09
Real-Time Grasp Detection Using Convolutional Neural Networks Code
#6GGCNN
73
5 fold cross validation
No paperCode
#7Fast SearchSOTA
60.5
5 fold cross validation· 2013-01-16
Deep Learning for Detecting Robotic Grasps