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- # tsbir
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Image Retrieval with Text and Sketch
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+ This code is for our 2022 ECCV paper [[A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch]](https://patsorn.me/projects/tsbir/)
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+
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+ <img src="https://patsorn.me/projects/tsbir/img/teaser_web_mini.jpg" width="900px"/>
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+
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+ ---------------------
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+ folder structure
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+ ---------------------
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+ |---model/ : Contain the trained model*
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+ |---sketches/ : Contain example query sketch
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+ |---images/ : Contain 100 randomly sampled images from COCO TBIR benchmark
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+ |---notebooks/ : Contain the demo ipynb notebook (can run via Colab)
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+ |---code/
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+ |---training/model_configs/ : Contain model config file for the network
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+ |---clip/ : Contain source code for running the notebook
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+
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+ *model can be downloaded from https://patsorn.me/projects/tsbir/data/tsbir_model_final.pt
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+
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+ This repo is based on open_clip implementation from https://github.com/mlfoundations/open_clip
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+
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+ ## Prerequisites
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+ - Pytorch
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+
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+ ## Getting Started
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+
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+ Simply run notebooks/Retrieval_Demo.ipynb, you can use your own set of images and sketches by modifying the images/ and sketches/ folder accordingly.
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+
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+ ## Download Models
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+ Pre-trained models
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+ - <a href='https://patsorn.me/projects/tsbir/data/tsbir_model_final.pt' > Pre-trained models </a>
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+
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+ ## Citation
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+ If you find it this code useful for your research, please cite:
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+
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+ "A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch"
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+
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+ [Patsorn Sangkloy](https://patsorn.me), [Wittawat Jitkrittum](http://wittawat.com/), Diyi Yang, James Hays in ECCV, 2022.
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+ ```
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+ @article{
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+ tsbir2022,
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+ author = {Patsorn Sangkloy and Wittawat Jitkrittum and Diyi Yang and James Hays},
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+ title = {A Sketch is Worth a Thousand Words: Image Retrieval with Text and Sketch},
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+ journal = {European Conference on Computer Vision, ECCV},
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+ year = {2022},
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+ }
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+ ```
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+