TEED/README.md

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# Tiny and Efficient Model for the Edge Detection Generalization (Paper)

## Overview

<div style="text-align:center"><img src='imgs/teedBanner.png' width=800>
</div>



Tiny and Efficient Edge Detector (TEED) is a light convolutional neural
network with only $58K$ parameters, less than $0.2$% of the 
state-of-the-art models. Training on the [BIPED](https://www.kaggle.com/datasets/xavysp/biped)
dataset takes *less than 30 minutes*, with each epoch requiring 
*less than 5 minutes*. Our proposed model is easy to train
and it quickly converges within very first few epochs, while the 
predicted edge-maps are crisp and of high quality, see image above.
[This paper has been accepted by ICCV 2023-Workshop RCV](https://arxiv.org/abs/2308.06468).

... In construction

    git clone https://github.com/xavysp/TEED.git

    cd TEED


Then,

## Testing with TEED

Copy and paste your images into data/ folder, and:

    python main.py --choose_test_data=-1


## Training with TEED

Set the following lines in main.py:

    25: is_testing =False

    # training with BIPED

    223: TRAIN_DATA = DATASET_NAMES[0] 


then run
    

    python main.py


Check the configurations of the datasets in dataset.py


## UDED dataset

Here the [link](https://github.com/xavysp/UDED) to access the UDED dataset for edge detection

## Citation

If you like TEED, why not starring the project on GitHub!

[![GitHub stars](https://img.shields.io/github/stars/xavysp/TEED.svg?style=social&label=Star&maxAge=3600)](https://GitHub.com/xavysp/TEED/stargazers/)

Please cite our Dataset if you find helpful in your academic/scientific publication,
```

@InProceedings{Soria_2023teed,

    author    = {Soria, Xavier and Li, Yachuan and Rouhani, Mohammad and Sappa, Angel D.},

    title     = {Tiny and Efficient Model for the Edge Detection Generalization},

    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},

    month     = {October},

    year      = {2023},

    pages     = {1364-1373}

}