Papers › Tiny and Efficient Model for the Edge Detection Generalization

Tiny and Efficient Model for the Edge Detection Generalization

12 Aug 2023arXiv:2308.06468archive 2025-07-28

Xavier Soria, Yachuan Li, Mohammad Rouhani, Angel D. Sappa

Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only $58K$ parameters, less than $0.2$% of the state-of-the-art models. Training on the 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. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.

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xavysp/teed officialmentioned in papermentioned on GitHubpytorch report

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Boundary DetectionContour DetectionEdge DetectionImage SegmentationSemantic Segmentation

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UDED

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Edge Detection UDED TEED ODS 0.828 #2 of 5 Archive leaderboard report

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