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2 dépôts

Awesome GitHub RepositoriesConvolutional Channel Pruning

Reducing the number of input channels in convolutional layers to decrease model complexity.

Distinct from Model Pruning: Specializes in channel-level structural pruning rather than general parameter removal.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Convolutional Channel Pruning. Refine with filters or upvote what's useful.

Awesome Convolutional Channel Pruning GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • vainf/torch-pruningAvatar de VainF

    VainF/Torch-Pruning

    3,319Voir sur GitHub↗

    Torch-Pruning is a deep learning model pruning tool and neural network optimization toolkit designed for PyTorch. The library analyzes neural network graphs to remove redundant parameters and channels, reducing model size and computational costs. The framework traces computational graphs dynamically using sample inputs to map relational dependencies and identify coupled layers that require simultaneous pruning. It evaluates and ranks layer parameters across the entire network using global importance ranking and applies structural constraints to optimize overall outcomes. Additional capabili

    Reducing the size of deep learning models by removing unnecessary parameters and channels to lower memory and compute costs.

    Pythonefficient-deep-learningllmmodel-compression
    Voir sur GitHub↗3,319
  • tencent/pocketflowAvatar de Tencent

    Tencent/PocketFlow

    2,914Voir sur GitHub↗

    PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format optimization. It provides a system for reducing the size and complexity of neural networks to improve inference efficiency, featuring a dedicated engine for knowledge distillation and a mobile model optimizer. The framework differentiates itself through an automated hyperparameter tuning system that uses reinforcement learning and statistical models to determine optimal compression ratios and layer-wise bit allocation. It also includes a distributed training system that utilizes mu

    Reduces input channels in convolutional layers to decrease model size while minimizing reconstruction loss.

    Pythonautomlcomputer-visiondeep-learning
    Voir sur GitHub↗2,914
  1. Home
  2. Artificial Intelligence & ML
  3. Model Optimization
  4. Compression Techniques
  5. Model Pruning
  6. Convolutional Channel Pruning

Explorer les sous-tags

  • Channel Scaling ManagementTechniques for adjusting input and output channel dimensions through expansion factors and convolutions. **Distinct from Convolutional Channel Pruning:** Focuses on constructive scaling and shape matching rather than removing channels for pruning.
  • Structured Neural Network PruningReducing the size of deep learning models by removing unnecessary parameters and channels to lower memory and compute costs. **Distinct from Convolutional Channel Pruning:** Distinct from Convolutional Channel Pruning: covers broader structural pruning of neural network parameters and channels beyond strictly convolutional layers.