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3 repositorios

Awesome GitHub RepositoriesInference Performance Optimizers

Tools for model compression and quantization to enhance speed in resource-constrained environments.

Distinct from Edge Object Detection: Distinct from edge detection: focuses on the optimization process rather than the detection model itself.

Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Inference Performance Optimizers. Refine with filters or upvote what's useful.

Awesome Inference Performance Optimizers GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • paddlepaddle/paddledetectionAvatar de PaddlePaddle

    PaddlePaddle/PaddleDetection

    14,243Ver en GitHub↗

    PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

    Enhances inference speed and precision through model compression and quantization for resource-constrained environments.

    Pythonblazefacedeepsortdetr
    Ver en GitHub↗14,243
  • dusty-nv/jetson-inferenceAvatar de dusty-nv

    dusty-nv/jetson-inference

    8,734Ver en GitHub↗

    jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

    Increases inference throughput using custom attention kernels, in-flight batching, and paged KV caching.

    C++caffecomputer-visiondeep-learning
    Ver en GitHub↗8,734
  • rlinf/rlinfAvatar de RLinf

    RLinf/RLinf

    2,502Ver en GitHub↗

    RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface

    Maximizes simulator throughput by overlapping model forward passes with environment stepping across vectorized slices.

    Pythonagentic-aiembodied-aireinforcement-learning
    Ver en GitHub↗2,502
  1. Home
  2. Artificial Intelligence & ML
  3. Computer Vision Systems
  4. Computer Vision
  5. Object Detection and Tracking
  6. Edge Object Detection
  7. Inference Performance Optimizers

Explorar subetiquetas

  • Throughput Optimizations1 sub-etiquetaTechniques specifically targeting the increase of inference throughput using attention kernels and batching. **Distinct from Inference Performance Optimizers:** Focuses on throughput-increasing mechanisms like paged KV caching and in-flight batching, rather than general compression/quantization.