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3 Repos

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

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • paddlepaddle/paddledetectionAvatar von PaddlePaddle

    PaddlePaddle/PaddleDetection

    14,243Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗14,243
  • dusty-nv/jetson-inferenceAvatar von dusty-nv

    dusty-nv/jetson-inference

    8,734Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗8,734
  • rlinf/rlinfAvatar von RLinf

    RLinf/RLinf

    2,502Auf GitHub ansehen↗

    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
    Auf GitHub ansehen↗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

Unter-Tags erkunden

  • Throughput Optimizations1 Sub-TagTechniques 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.