# Tiny and Efficient Model for the Edge Detection Generalization arXiv: https://arxiv.org/abs/2308.06468 ยท HTML: https://mlsanity.com/paper/2308.06468/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 56 references that are in the map. - First posted on arXiv 12 Aug 2023. ## Builds on (most cited first, 25 of 25) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016) - [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016) - [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021) - [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021) - [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015) - [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021) - [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018) - [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016) - [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019) - [Learning to predict crisp boundaries](https://mlsanity.com/paper/1807.10097/) (2018) - [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020) - [Dense Extreme Inception Network for Edge Detection](https://mlsanity.com/paper/2112.02250/) (2021) - [PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report](https://mlsanity.com/paper/1810.01641/) (2018) - [Depthwise Convolution is All You Need for Learning Multiple Visual Domains](https://mlsanity.com/paper/1902.00927/) (2019) - [EDTER: Edge Detection with Transformer](https://mlsanity.com/paper/2203.08566/) (2022) - [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021) - [Unmixing Convolutional Features for Crisp Edge Detection](https://mlsanity.com/paper/2011.09808/) (2020) - [Traditional Method Inspired Deep Neural Network for Edge Detection](https://mlsanity.com/paper/2005.13862/) (2020) - [Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review](https://mlsanity.com/paper/1801.00454/) (2018) ## Built on by (most cited first, 28 of 28) - [PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions](https://mlsanity.com/paper/2409.15278/) (2024) - [MagicQuill: An Intelligent Interactive Image Editing System](https://mlsanity.com/paper/2411.09703/) (2024) - [KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data](https://mlsanity.com/paper/2409.14066/) (2024) - [Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances](https://mlsanity.com/paper/2502.04268/) (2025) - [ToonComposer: Streamlining Cartoon Production with Generative Post-Keyframing](https://mlsanity.com/paper/2508.10881/) (2025) - [MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues](https://mlsanity.com/paper/2512.03046/) (2025) - [MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention](https://mlsanity.com/paper/2501.00816/) (2025) - [Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm](https://mlsanity.com/paper/2501.02534/) (2025) - [MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network](https://mlsanity.com/paper/2507.11333/) (2025) - [SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection](https://mlsanity.com/paper/2401.02313/) (2024) - [A Comprehensive Study for Edge Detection: Ideal-Prior Guidance Learning, High-Precision Architecture, and Better Distinguishability Evaluation Benchmark](https://mlsanity.com/paper/2407.19992/) (2024) - [Symmetrization Weighted Binary Cross-Entropy: Modeling Perceptual Asymmetry for Human-Consistent Neural Edge Detection](https://mlsanity.com/paper/2501.13365/) (2025) - [DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets](https://mlsanity.com/paper/2505.23980/) (2025) - [Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection](https://mlsanity.com/paper/2504.15770/) (2025) - [GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans](https://mlsanity.com/paper/2505.05376/) (2025) - [Binarization-Aware Adjuster for Discrete Decision Learning with an Application to Edge Detection](https://mlsanity.com/paper/2506.12460/) (2025) - [Structure-Guided Diffusion Models for High-Fidelity Portrait Shadow Removal](https://mlsanity.com/paper/2507.04692/) (2025) - [Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generation](https://mlsanity.com/paper/2510.16319/) (2025) - [MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision](https://mlsanity.com/paper/2602.20689/) (2026) - [Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding](https://mlsanity.com/paper/2407.04859/) (2024) - [Edge-Boundary-Texture Loss: A Tri-Class Generalization of Weighted Binary Cross-Entropy for Enhanced Edge Detection](https://mlsanity.com/paper/2507.06569/) (2025) - [MooneyMaker: A Python package to create ambiguous two-tone images](https://mlsanity.com/paper/2601.14077/) (2026) - [Sketch It Out: Exploring Label-Free Structural Cues for Multimodal Gait Recognition](https://mlsanity.com/paper/2603.05537/) (2026) - [Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels](https://mlsanity.com/paper/2605.00744/) (2026) - [Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models](https://mlsanity.com/paper/2607.01535/) (2026) - [Neighbor-Aware View Synthesis for Restoring Missing Views in Light-Field Camera Arrays](https://mlsanity.com/paper/2608.23175/) (2026) - [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026) - [OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation](https://mlsanity.com/paper/2609.22069/) (2026) ## Abstract (arXiv) 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.