HaS Image Model (FP32)

HaS (Hide and Seek) is an on-device image privacy model that performs pixel-level detection and segmentation of sensitive regions in images.

• 📦 YOLO11-based instance segmentation, FP32, 119 MB
• 🔒 Data never leaves device — local inference, no network required
• 🎯 21 privacy categories covering biometrics, ID documents, financial cards, screens, and more
• ⚡ ~20ms per image inference speed on modern hardware

1. Core Capabilities

Traditional image redaction relies on face-only detectors or manual masking. HaS Image is an on-device privacy segmentation model — it recognizes 21 categories of sensitive content with pixel-level masks, enabling precise and automated redaction.

2. Supported Privacy Categories

6 groups, 21 categories:

Category filtering supports English names, Chinese names, or numeric IDs (comma-separated).

3. Quick Start

Installation

pip install ultralytics

Python Usage

from ultralytics import YOLO

model = YOLO("sensitive_seg_best.pt")

# Detect all privacy regions
results = model.predict("photo.jpg", conf=0.25)

# Process results
for result in results:
    for box, mask in zip(result.boxes, result.masks):
        category_id = int(box.cls)
        confidence = float(box.conf)
        print(f"Category: {category_id}, Confidence: {confidence:.2f}")

CLI Usage (via HaS)

HaS ships with a CLI tool has-image that wraps the model into ready-to-use commands:

# Scan — detect privacy regions without modifying the image
has-image scan --image photo.jpg

# Hide — detect and redact with mosaic (default)
has-image hide --image photo.jpg

# Hide — redact faces only, with Gaussian blur
has-image hide --image photo.jpg --types face --method blur --strength 25

# Hide — batch process a directory
has-image hide --dir ./photos/ --output-dir ./redacted/

4. Redaction Methods

5. Usage Scenarios

6. Redaction Example

Figure 1. Before and after privacy redaction with HaS Image model

7. Model Specifications

8. Related Models

HaS Text handles text anonymization (named entities, PII); HaS Image handles image anonymization (visual privacy regions). Together they provide a complete on-device privacy solution for both text and images.

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中文版

HaS Image Model (FP32)

HaS（Hide and Seek） 是一个端侧部署的图像隐私模型，对图片中的隐私区域进行像素级识别和分割。

• 📦 基于 YOLO11 实例分割，FP32 精度，119 MB
• 🔒 数据不出设备，本地推理，无需联网
• 🎯 21 类隐私类别，覆盖生物特征、证件、金融卡、屏幕等
• ⚡ 单张图片 ~20ms 推理速度

一、核心能力

传统图像脱敏依赖人脸检测器或手动遮挡。HaS Image 是一个端侧隐私分割模型——识别 21 类敏感内容，提供像素级掩码，实现精准自动化脱敏。

二、支持的隐私类别

6 大分组，21 个类别：

类别过滤支持英文名、中文名或数字 ID，逗号分隔。

三、快速开始

安装

pip install ultralytics

Python 使用

from ultralytics import YOLO

model = YOLO("sensitive_seg_best.pt")

# 检测所有隐私区域
results = model.predict("photo.jpg", conf=0.25)

# 处理结果
for result in results:
    for box, mask in zip(result.boxes, result.masks):
        category_id = int(box.cls)
        confidence = float(box.conf)
        print(f"类别: {category_id}, 置信度: {confidence:.2f}")

CLI 使用（通过 HaS）

HaS 配套了 CLI 工具 has-image，将模型封装为开箱即用的命令：

# 扫描——仅检测隐私区域，不修改图片
has-image scan --image photo.jpg

# 脱敏——检测并遮挡（默认马赛克）
has-image hide --image photo.jpg

# 脱敏——仅遮挡人脸，使用高斯模糊
has-image hide --image photo.jpg --types face --method blur --strength 25

# 批量处理整个目录
has-image hide --dir ./photos/ --output-dir ./redacted/

四、遮挡方式

五、使用场景

六、脱敏示例

图 1. HaS Image 模型脱敏前后对比

七、模型规格

八、相关模型

HaS Text 处理文本脱敏（命名实体、个人信息）；HaS Image 处理图像脱敏（视觉隐私区域）。二者结合，提供完整的端侧文本+图像隐私保护方案。