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-# Image Segmentation Using Text and Image Prompts
-This repository contains the code used in the paper ["Image Segmentation Using Text and Image Prompts"](https://arxiv.org/abs/2112.10003).
-
-**The Paper has been accepted to CVPR 2022!**
-
-<img src="overview.png" alt="drawing" height="200em"/>
-
-The systems allows to create segmentation models without training based on:
-- An arbitrary text query
-- Or an image with a mask highlighting stuff or an object.
-
-### Quick Start
-
-In the `Quickstart.ipynb` notebook we provide the code for using a pre-trained CLIPSeg model. If you run the notebook locally, make sure you downloaded the `rd64-uni.pth` weights, either manually or via git lfs extension.
-It can also be used interactively using [MyBinder](https://mybinder.org/v2/gh/timojl/clipseg/HEAD?labpath=Quickstart.ipynb)
-(please note that the VM does not use a GPU, thus inference takes a few seconds).
-
-
-### Dependencies
-This code base depends on pytorch, torchvision and clip (`pip install git+https://github.com/openai/CLIP.git`).
-Additional dependencies are hidden for double blind review.
-
-
-### Datasets
-
-* `PhraseCut` and `PhraseCutPlus`: Referring expression dataset
-* `PFEPascalWrapper`: Wrapper class for PFENet's Pascal-5i implementation
-* `PascalZeroShot`: Wrapper class for PascalZeroShot
-* `COCOWrapper`: Wrapper class for COCO.
-
-### Models
-
-* `CLIPDensePredT`: CLIPSeg model with transformer-based decoder.
-* `ViTDensePredT`: CLIPSeg model with transformer-based decoder.
-
-### Third Party Dependencies
-For some of the datasets third party dependencies are required. Run the following commands in the `third_party` folder.  
-```bash
-git clone https://github.com/cvlab-yonsei/JoEm
-git clone https://github.com/Jia-Research-Lab/PFENet.git
-git clone https://github.com/ChenyunWu/PhraseCutDataset.git
-git clone https://github.com/juhongm999/hsnet.git
-```
-
-### Weights
-
-The MIT license does not apply to these weights. 
-
-We provide two model weights, for D=64 (4.1MB) and D=16 (1.1MB).
-```
-wget https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download -O weights.zip
-unzip -d weights -j weights.zip
-```
-
-
-### Training and Evaluation
-
-To train use the `training.py` script with experiment file and experiment id parameters. E.g. `python training.py phrasecut.yaml 0` will train the first phrasecut experiment which is defined by the `configuration` and first `individual_configurations` parameters. Model weights will be written in `logs/`.
-
-For evaluation use `score.py`. E.g. `python score.py phrasecut.yaml 0 0` will train the first phrasecut experiment of `test_configuration` and the first configuration in `individual_configurations`.
-
-
-### Usage of PFENet Wrappers
-
-In order to use the dataset and model wrappers for PFENet, the PFENet repository needs to be cloned to the root folder.
-`git clone https://github.com/Jia-Research-Lab/PFENet.git `
-
-
-### License
-
-The source code files in this repository (excluding model weights) are released under MIT license.
-
-### Citation
-```
-@InProceedings{lueddecke22_cvpr,
-    author    = {L\"uddecke, Timo and Ecker, Alexander},
-    title     = {Image Segmentation Using Text and Image Prompts},
-    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-    month     = {June},
-    year      = {2022},
-    pages     = {7086-7096}
-}
-
-```