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etos-deepcut

A tool for object segmentation from extreme points.

About Deep Extreme Cut (DEXTR)

Visit project page for accessing the paper, and the pre-computed results.

DEXTR

This is based the implementation of Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

Installation

  1. Clone the repo:

    git clone https://github.com/etosworld/etos-deepcut
cd etos-deepcut

  2. Install dependencies:

    pip install -r requirements.txt

  3. Download the model by running the script inside models/:

    cd models/
chmod +x download_dextr_model.sh
./download_dextr_model.sh
cd ..

    The default model is trained on PASCAL VOC Segmentation train + SBD (10582 images). To download models trained on PASCAL VOC Segmentation train or COCO, please visit project page, or keep scrolling till the end of this README.

  4. To try the demo version of etos-deepcut, please run:

    python demo.py

If installed correctly, the result should look like this:

image2txt

we have implemented the image2txt function , for each segmented object, an image and text file would be saved.enjoy!

Training

To train and evaluate etos-deepcut on PASCAL (or PASCAL + SBD), please follow these additional steps:

  1. Install tensorboard (integrated with PyTorch).

    pip install tensorboard tensorboardx

  2. Download the pre-trained PSPNet model for semantic segmentation, taken from this repository.

    cd models/
chmod +x download_pretrained_psp_model.sh
./download_pretrained_psp_model.sh
cd ..

  3. Set the paths in mypath.py, so that they point to the location of PASCAL/SBD dataset.

  4. Run python train_pascal.py, after changing the default parameters, if necessary (eg. gpu_id).

Enjoy!!

Pre-trained models

you can download the following DEXTR models, pre-trained on:

  • PASCAL + SBD, trained on PASCAL VOC Segmentation train + SBD (10582 images). Achieves mIoU of 91.5% on PASCAL VOC Segmentation val.
  • PASCAL, trained on PASCAL VOC Segmentation train (1464 images). Achieves mIoU of 90.5% on PASCAL VOC Segmentation val.
  • COCO, trained on COCO train 2014 (82783 images). Achieves mIoU of 87.8% on PASCAL VOC Segmentation val.

TODO

  • to support deep extreme video cut

Citation

@Inproceedings{Man+18,
  Title          = {Deep Extreme Cut: From Extreme Points to Object Segmentation},
  Author         = {K.K. Maninis and S. Caelles and J. Pont-Tuset and L. {Van Gool}},
  Booktitle      = {Computer Vision and Pattern Recognition (CVPR)},
  Year           = {2018}
}

@InProceedings{Pap+17,
  Title          = {Extreme clicking for efficient object annotation},
  Author         = {D.P. Papadopoulos and J. Uijlings and F. Keller and V. Ferrari},
  Booktitle      = {ICCV},
  Year           = {2017}
}