GR-ConvNet-grasping

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

GR-ConvNet-grasping

★ 20 Open GitHub ↗

Deep Robotic Grasping using Generative Residual Convolutional Neural Networks that can generate robust antipodal grasps from RGB-D input images at real-time

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Deep Robotic Grasping using Generative Residual Convolutional Neural Network

A CNN (GR-ConvNet) model that can generate robust antipodal grasps from RGB-D input images at real-time
![alt text](https://raw.githubusercontent.com/Loahit5101/GR-ConvNet-grasping/main/results/Figure_15.png "Loss Curve")

## Setup
- Clone this repository and install required libraries
```
git clone https://github.com/Loahit5101/GR-ConvNet-grasping.git
cd robotic-grasping
pip install -r requirements.txt
```
- Install [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) and [Torch-TensorRT](https://github.com/pytorch/TensorRT)
## Architecture
![alt text](https://raw.githubusercontent.com/Loahit5101/GR-ConvNet-grasping/main/results/Architecture.png "Loss Curve")
## Dataset

Download and extract the Cornell Grasping dataset and run the following command: [Cornell Grasping Dataset](https://www.kaggle.com/oneoneliu/cornell-grasp)
```
python -m utils.dataset_processing.generate_cornell_depth <Path To Dataset>

````
## Pretrained models
Trained models are available [here](https://github.com/Loahit5101/GR-ConvNet-grasping/tree/main/trained%20_Models)
## Training
```
python train.py
````
## Testing
```
python test.py
````
## Model Optimization using TensorRT
- Post-training quantization
```
python ptq.py
````
- Benchmarking grasp inference time of optimized and unoptimized models
```
python trt_benchmark.py
````
## Results

![alt text](https://raw.githubusercontent.com/Loahit5101/GR-ConvNet-grasping/main/figures/Figure_loss.png "Loss Curve")

Average Grasp Inference Time
|     Model     |   Time (ms)   | Accuracy | 
| ------------- |:-------------:|:--------:| 
| Baseline      |     4.59        |     95.5     |
| FP-32         |     3.71           |   94.2       | 
| FP-16          |    1.45      |       93.16   |

Post-processing time = 14 ms on average

## References
1. [Antipodal Robotic Grasping using GR-ConvNet](https://github.com/skumra/robotic-grasping)
2. [Pytorch-TensorRT Tutorials](https://github.com/pytorch/TensorRT/tree/master/examples)

GR-ConvNet-grasping

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

GR-ConvNet-grasping

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README