https://github.com/fidler-lab/polyrnn-pp

Inference Code for Polygon-RNN++ (CVPR 2018)
https://github.com/fidler-lab/polyrnn-pp

annotation cvpr2018 deep-learning instance-annotation instance-segmentation labelling polygon-rnn polyrnn tensorflow

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Inference Code for Polygon-RNN++ (CVPR 2018)

• Host: GitHub
• URL: https://github.com/fidler-lab/polyrnn-pp
• Owner: fidler-lab
• License: gpl-3.0
• Created: 2018-03-16T23:04:27.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-08-29T16:30:36.000Z (over 6 years ago)
• Last Synced: 2024-08-08T23:18:50.308Z (6 months ago)
• Topics: annotation, cvpr2018, deep-learning, instance-annotation, instance-segmentation, labelling, polygon-rnn, polyrnn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.47 MB
• Stars: 732
• Watchers: 47
• Forks: 161
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PolygonRNN++

This is the official inference code for Polygon-RNN++ (CVPR-2018). For technical details, please refer to:  

**An official pytorch reimplementation with training/tool code is available [here](https://github.com/fidler-lab/polyrnn-pp-pytorch)**

**Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++**  

[David Acuna](http://www.cs.toronto.edu/~davidj/)\*, [Huan Ling](http:///www.cs.toronto.edu/~linghuan/)\*, [Amlan Kar](http://www.cs.toronto.edu/~amlan/)\*, [Sanja Fidler](http://www.cs.toronto.edu/~fidler/) (\* denotes equal contribution)   

CVPR 2018  

**[[Paper](https://arxiv.org/abs/1803.09693)] [[Video](https://www.youtube.com/watch?v=evGqMnL4P3E)] [[Project Page](http://www.cs.toronto.edu/polyrnn/)] [[Demo](http://www.cs.toronto.edu/~amlan/demo/)] [[Training/Tool Code](https://github.com/fidler-lab/polyrnn-pp-pytorch)]**

![Model](readme/model.png)

### Usage

   1. Clone the repository 

   ```

   git clone https://github.com/davidjesusacu/polyrnn && cd polyrnn

   ```

   2. Install dependencies   

   (Note: Using a GPU (and tensorflow-gpu) is recommended. The model will run on a CPU, albeit slowly.)

   ```

   virtualenv env

   source env/bin/activate

   pip install -r requirements.txt

   ```

   3. Download the pre-trained models and graphs (448 MB)  

   (These models were trained on the Cityscapes Dataset)

   ``` 

   ./models/download_and_unpack.sh 

   ```

   4. Run demo\_inference.sh

   ```

   ./src/demo_inference.sh 

   ```

   This should produce results in the output/ folder that look like

   ![ex2](readme/frankfurt_000000_000294_42.png)

   ![ex1](readme/medical_00_5_20.png) 

### Walkthrough

   Checkout the ipython [notebook](src/demo_polyrnn.ipynb) that provides a simple walkthrough demonstrating how

   to run our model on sample input image crops

If you use this code, please cite:

    @inproceedings{AcunaCVPR18,

    title={Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++},

    author={David Acuna and Huan Ling and Amlan Kar and Sanja Fidler},

    booktitle={CVPR},

    year={2018}

    }

    @inproceedings{CastrejonCVPR17,

    title = {Annotating Object Instances with a Polygon-RNN},

    author = {Lluis Castrejon and Kaustav Kundu and Raquel Urtasun and Sanja Fidler},

    booktitle = {CVPR},

    year = {2017}

    }