TEEDTEEDTEEDTEED

[](https://paperswithcode.com/sota/edge-detection-on-uded?p=tiny-and-efficient-model-for-the-edge) # Tiny and Efficient Model for the Edge Detection Generalization (Paper) ## Overview 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! [](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} }