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    1. Home
    2. Themed Directories
    3. Awesome Deep Vision

    Awesome Deep Vision

    A GitHub-hosted curated “awesome” list collecting deep learning resources specifically for computer vision, including papers, code, and tutorials. It is part of the broader Awesome ecosystem of topic-focused resource directories.

    Awesome Deep Vision

    Website: https://github.com/kjw0612/awesome-deep-vision
    Category: Themed Directories
    Tags: computer-vision, deep-learning, ai
    Maintainer/Brand: kjw0612 (with collaborators)
    Status: Not actively maintained

    Overview

    Awesome Deep Vision is a GitHub-hosted, curated “awesome” list that aggregates deep learning resources focused on computer vision. It collects links to research papers, code, and related materials, and is part of the broader Awesome ecosystem of topic-specific resource lists.

    Features

    • Curated deep learning resources for computer vision
      • Focused on computer vision tasks using deep learning methods
      • Includes research papers and often associated code or implementation links
    • Organized by task areas (as visible from the table of contents and headings)
      • ImageNet Classification
      • Object Detection
      • Video Classification
      • Object Tracking
      • Low-Level Vision
        • Super-Resolution
        • Other Applications
      • Edge Detection
    • Reference figures and examples
      • ImageNet classification example figure (from Krizhevsky et al., 2012, NIPS)
      • Object detection example figure (from Ren et al., Faster R-CNN, arXiv:1506.01497)
      • Edge detection example illustration
    • Research-oriented content
      • Primarily links to academic papers in deep learning and computer vision
      • Highlights influential and foundational works in each sub-area
    • Community contributions
      • Open to contributions via GitHub pull requests for adding new papers or resources
    • Open collaboration channels
      • Public Gitter chat room for discussion around the list and its content

    Contribution

    • Contributions are accepted via GitHub pull requests to add or update papers and resources.
    • Public discussion and coordination available through the linked Gitter chat.

    Pricing

    • Free to access and use (open GitHub repository / awesome list).
    Surveys

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    Information

    Websitegithub.com
    PublishedDec 25, 2025

    Categories

    1 Item
    Themed Directories

    Tags

    3 Items
    #computer-vision#deep-learning#ai

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