awesome-repositories.com
المدونة
MCP
awesome-repositories.com

اكتشف أفضل مستودعات المصادر المفتوحة باستخدام بحث مدعوم بالذكاء الاصطناعي.

استكشفعمليات بحث منسقةبدائل مفتوحة المصدربرمجيات ذاتية الاستضافةالمدونةخريطة الموقع
المشروعخادم MCPحولكيفية ترتيب النتائجالصحافة
قانونيالخصوصيةالشروط
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

3 مستودعات

Awesome GitHub RepositoriesRuntime Activation Visualizations

Tools for visualizing internal layer activations and decision-making processes during model execution.

Distinct from Model Inspection Tools: Distinct from Model Inspection Tools by focusing on runtime activation visualization rather than static metadata analysis

Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Runtime Activation Visualizations. Refine with filters or upvote what's useful.

Awesome Runtime Activation Visualizations GitHub Repositories

اعثر على أفضل المستودعات باستخدام الذكاء الاصطناعي.سنبحث عن أفضل المستودعات المطابقة باستخدام الذكاء الاصطناعي.
  • matterport/mask_rcnnالصورة الرمزية لـ matterport

    matterport/Mask_RCNN

    25,564عرض على GitHub↗

    This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for performing simultaneous object detection and instance segmentation, transforming raw images into segmented masks and bounding boxes for individual object identification. The toolset enables custom computer vision training through fine-tuning pre-trained weights and integrating user-provided datasets. It includes capabilities for distributed GPU training to accelerate the optimization of large vision models. The framework covers model evaluation using standard precision metrics an

    Includes diagnostics for internal model inspection through the visualization of layer activations.

    Pythoninstance-segmentationkerasmask-rcnn
    عرض على GitHub↗25,564
  • greena13/react-hotkeysالصورة الرمزية لـ greena13

    greena13/react-hotkeys

    2,158عرض على GitHub↗

    React hotkeys is a keyboard shortcut manager designed for declaring, scoping, and handling keyboard shortcuts and focus areas in React applications. It allows developers to define declarative shortcuts by binding key combinations or sequences to action names and handler functions, enabling components to respond predictably to user input. The library provides distinct mechanisms for managing how keyboard events interact with the document. It supports scoping hotkeys to specific component subtrees and DOM descendants to match natural browser focus behavior, alongside global key event handling t

    Retrieves active mappings and registered actions from mounted components to support help dialog generation.

    JavaScript
    عرض على GitHub↗2,158
  • mottosso/qt.pyالصورة الرمزية لـ mottosso

    mottosso/Qt.py

    993عرض على GitHub↗

    Qt.py is a compatibility layer and shim library that enables Python desktop applications to run across multiple graphical toolkit bindings without changing underlying code. It standardizes interface loading and normalizes divergent application programming interfaces across various versions of PySide and PyQt. The library bridges disparate graphical toolkit implementations by normalizing behavioral differences and unifying multiple binding options through a single compatibility layer. It provides standardized functions for loading interface definition files into new or existing widget instance

    Exposes metadata attributes indicating the currently active graphical toolkit, project version, and underlying component versions at runtime.

    Pythonpyqt4pyqt5pyside
    عرض على GitHub↗993
  1. Home
  2. Artificial Intelligence & ML
  3. Model Inspection Tools
  4. Runtime Activation Visualizations

استكشف الوسوم الفرعية

  • Active Binding InspectionsExposes metadata regarding active graphical toolkits and component versions at runtime. **Distinct from Runtime Activation Visualizations:** Distinct from Runtime Activation Visualizations: inspects active UI toolkit metadata and component versions rather than neural network layer activations.