cv_efficientsam-s_image-instance-segmentation_sa1b

iic/cv_efficientsam-s_image-instance-segmentation_sa1b

Publisheriic

Download access requirementsModelScope source

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything. 利用 SAM 的掩码图像预训练 (SAMI), 并用轻量级编码器训练掩码图像模型，从而从 SAM 的 ViT-H 而不是图像补丁重建特征，产生的通用 ViT 骨干可用于下游任务，如图像分类、物体检测和分割等。然后，研究者利用 SAM 解码器对预训练的轻量级编码器进行微调，以完成任何分割任务。通过 SAMI 预训练，可

Image Analysis

View all download instructions Mirror repository documentation and license ↗

Model details

模型介绍

中文整理

EfficientSAM

&nbspPaper&nbsp｜

&nbspGitHub &nbsp

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything.

模型描述

利用 SAM 的掩码图像预训练 (SAMI), 并用轻量级编码器训练掩码图像模型，从而从 SAM 的 ViT-H 而不是图像补丁重建特征，产生的通用 ViT 骨干可用于下游任务，如图像分类、物体检测和分割等。然后，研究者利用 SAM 解码器对预训练的轻量级编码器进行微调，以完成任何分割任务。通过 SAMI 预训练，可以在 ImageNet-1K 上训练 ViT-Tiny/-Small/-Base 等模型，并提高泛化性能。对于 ViT-Small 模型，研究者在 ImageNet-1K 上进行 100 次微调后，其 Top-1 准确率达到 82.7%，优于其他最先进的图像预训练基线。提出的 EfficientSAM 参数减少了 20 倍，但运行时间快了 20 倍，只与原始 SAM 模型的差距在 2 个百分点以内，大大优于 MobileSAM/FastSAM。 EfficientSAM 包含两个阶段：1）在 ImageNet 上对 SAMI 进行预训练（上）；2）在 SA-1B 上微调 SAM（下）。

期望模型使用方式以及适用范围

本模型适用范围较广，能对图片中包含的大部分感兴趣物体（COCO things 80类）根据提示(点、框、文本)进行分割。

如何使用

在ModelScope框架上，提供输入图片，即可通过简单的Pipeline调用来使用。

代码范例

范例的输入和输出:

模型局限性以及可能的偏差

部分感兴趣物体占比太小或遮挡严重可能会影响分割结果

训练数据介绍

分割任何10亿(SA-1B)是一个数据集，SA-1B由1100万张多样化、高分辨率、隐私保护图像和使用数据引擎收集的1.1B高质量分割掩码组成。

数据评估及结果

Clone with HTTP

Quote

如果该模型对你有所帮助，请引用相关的论文：

优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。

你将获得什么

完整仓库方式包含该固定版本中的权重、配置、分词器及说明文件；指定版本方式只下载所选文件或整组分片。多模态模型的投影文件、配置等依赖，请按作者说明配齐。

完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。

使用前须知

运行环境

运行模型需要的设备、工具与依赖，以原作者说明为准。

授权范围

是否允许商用、修改和分发，请查看模型许可证。

闲社提供资料与镜像下载方法，不代表已运行模型或完成安全审计。下载的代码应先检查，再执行。

Problem with the information or download?

Pinned version · Multiple download options

cv_efficientsam-s_image-instance-segmentation_sa1b model download

Download the full repository or choose a version or file.

Download tools · More than GGUF

Use the Hugging Face download tool to obtain iic/cv_efficientsam-s_image-instance-segmentation_sa1b. Download Safetensors, GGUF, ONNX, configuration and other repository files. Downloading does not mean the tool can run this model.

Pinned-version file information is being completed. Existing downloads remain available, or load the file manifest below.

Choose a mirror or official source, your operating system and download scope below, then copy the command. Install the download tool first if needed.

Pinned version: 0b9debb25c33810da26a8592f4a2c354c02093fd. Downloads files only; does not start the model.

Choose a download source

Pinned ModelScope version: 0b9debb25c33810da26a8592f4a2c354c02093fd. Version identifiers can differ across sites. The alternative source was provided after checking weights and runtime configuration; this is not proof of a successful model run.

A checked, pinned ModelScope source is available for this model. Use the ModelScope download tool.

Choose what to download

Need just one GGUF precision? Load the file manifest and choose a version. Direct file links do not require Python; the command below can download multiple shards together.

Quantization / file

This reads the pinned version's file manifest; no model weights are downloaded.

Choose your operating system

For command-line downloads, follow these steps to get the full repository or selected files. Direct file links do not need these steps.

Open a terminal window

Create and open a folder for the model. In the folder's address bar, enter powershell and press Enter.

If Python is not installed, get it from the official Python website. In the terminal, enter python --version; continue when a version number appears.

First time? Install the download tool

Copy this command, paste it into the terminal, press Enter and wait for installation to finish.

python -m pip install --upgrade huggingface_hub

Already installed? Continue to step 3.

Run the download command

Select the button below, paste into the same terminal and press Enter. The tool downloads all files included in the current command.

Copying does not start a download. Paste and run the command on your device.

Where are the downloaded files?

Open the folder you chose. Look for the models folder. When progress stops and the terminal returns to its prompt without errors, check that all files are present. 'Copied' does not mean 'downloaded'.

No response or an error?

python not found: Install Python and reopen the terminal. On Windows, follow the installer's instructions to enable command-line access.

hf not found: Confirm that step 2 succeeded, then reopen the terminal. If needed, add Python's Scripts folder to your user PATH. Do not repeatedly download scripts.

Network interrupted: Stay in the same folder and run the same command again. The tool checks existing files.

Permission or disk error: Choose a writable folder with enough free space. Do not disable system security protection.

View the full download command

modelscope download --model "iic/cv_efficientsam-s_image-instance-segmentation_sa1b" --revision "0b9debb25c33810da26a8592f4a2c354c02093fd" --local_dir "./models/iic--cv_efficientsam-s_image-instance-segmentation_sa1b--ms-0b9debb25c33"

Files transfer directly from the source you choose, not through Xianshe servers.

The version is pinned. Repeating the same command checks existing files. Change the source manually if it is slow or unavailable.

Public models do not require submitting keys to Xianshe. Restricted models still require the author's authorization. On Linux / macOS, use python3 instead of python when appropriate.