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robot-grasp-detection · tnikolla

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using…

robot demonstrations
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robot-grasp-detection · tnikolla resource page screenshot
Resource screenshot · captured 2026-10-10 · visually reviewed

Overview

Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using…

Publisher purpose: Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.

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Project information

Maintainer
tnikolla
Primary language
Python
Repository code license
Apache-2.0
Model-weight license
Not recorded
Dataset license
Not recorded
Hardware design license
Not recorded
Maintenance
Not archived in the last recorded metadata
Last observed repository push
2018-01-26

Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.

Documentation sections

  • Detecting grasping positions with deep neural networks using RGB images
  • Problem description
  • How to train from scratch
  • Prepering Imagenet
  • Training on Imagenet
  • Prepering Cornell grasping dataset

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