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chongzhou96 avatar

chongzhou96/EdgeSAM

0
View on GitHub↗
1,157 stars·60 forks·Jupyter Notebook·9 viewsmmlab-ntu.com/project/edgesam↗

EdgeSAM

Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"

Features

  • Image segmentation - Listed in the “Image segmentation” section of the Ailia Models awesome list.

Star history

Star history chart for chongzhou96/edgesamStar history chart for chongzhou96/edgesam

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with EdgeSAM

These projects share indexed features with EdgeSAM. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • chaoningzhang/mobilesamChaoningZhang avatar

    ChaoningZhang/MobileSAM

    5,795View on GitHub↗

    MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on resource-constrained hardware. It functions as an automatic image masking tool capable of detecting and isolating distinct objects across an entire image without manual input. The system enables prompt-based object masking using coordinate points or bounding boxes to generate precise masks. It also supports all-object image segmentation through object-aware prompt sampling to identify every distinct object in a scene. To facilitate mobile and edge deployment, the model is compatible w

    Jupyter Notebook
    View on GitHub↗5,795
  • davidtvs/pytorch-enetD

    davidtvs/PyTorch-ENet

    0View on GitHub↗
    View on GitHub↗0
  • facebookresearch/segment-anythingfacebookresearch avatar

    facebookresearch/segment-anything

    54,353View on GitHub↗

    This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring server-side processing. The system distinguishes itself by utilizing hardware-accelerated execution and parallel processing to achieve real-time segmentation speeds. It supports prompt-based mask decoding, allowing users to generate spatial masks by providing specific points or boxes

    Jupyter Notebook
    View on GitHub↗54,353
  • casia-iva-lab/fastsamC

    CASIA-IVA-Lab/FastSAM

    0View on GitHub↗
    View on GitHub↗0
Compare all 23 related projects→

Frequently asked questions

What does chongzhou96/edgesam do?

Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"

What are the main features of chongzhou96/edgesam?

The main features of chongzhou96/edgesam are: Image segmentation.

Which projects share features with chongzhou96/edgesam?

Projects with overlapping indexed features include: chaoningzhang/mobilesam — MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on… davidtvs/pytorch-enet. facebookresearch/segment-anything — This project provides a deep learning architecture designed to identify and isolate distinct objects within images by… facebookresearch/segment-anything-2. hrnet/hrnet-semantic-segmentation — This is the implementation for PyTroch 0.4.1. - The HRNet + OCR version ia available here. - The PyTroch 1.1 version… casia-iva-lab/fastsam.