clipseg rd64 refined

ProviderCIDAS
Categoryimage-segmentation
Licenseapache-2.0
Downloads203
Stars0

Overview

ClipSeg RD64 Refined is a specialized image segmentation model designed for zero-shot performance, leveraging the combined strengths of CLIP's visual-language embeddings. Unlike traditional segmentation models that require predefined class labels, this model allows developers to isolate image regions using arbitrary natural language prompts. It is particularly effective for dynamic pipelines where target objects change frequently, eliminating the need for constant retraining. For integration, it fits into standard PyTorch-based workflows and is ideal for building intelligent cropping tools, automated image tagging, or interactive visual search interfaces where precise spatial masks are required based on text descriptions.

Highlights

  • Zero-shot segmentation using natural language prompts
  • Eliminates need for task-specific training datasets
  • Apache-2.0 license for flexible commercial deployment
  • High precision spatial masking for diverse objects

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("CIDAS/clipseg-rd64-refined")
tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download CIDAS/clipseg-rd64-refined

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download CIDAS/clipseg-rd64-refined config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('CIDAS/clipseg-rd64-refined')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/CIDAS/clipseg-rd64-refined

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/CIDAS/clipseg-rd64-refined

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('CIDAS/clipseg-rd64-refined')
tokenizer = AutoTokenizer.from_pretrained('CIDAS/clipseg-rd64-refined')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model CIDAS/clipseg-rd64-refined

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model CIDAS/clipseg-rd64-refined README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('CIDAS/clipseg-rd64-refined')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/CIDAS/clipseg-rd64-refined.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/CIDAS/clipseg-rd64-refined.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'CIDAS/clipseg-rd64-refined')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
tags:

  • vision

  • image-segmentation

inference: false
---

CLIPSeg model

CLIPSeg model with reduce dimension 64, refined (using a more complex convolution). It was introduced in the paper Image Segmentation Using Text and Image Prompts by Lüddecke et al. and first released in this repository.

Intended use cases

This model is intended for zero-shot and one-shot image segmentation.

Usage

Refer to the documentation.

View official source
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