HaS (Hide and Seek) is an on-device image privacy model that performs pixel-level detection and segmentation of sensitive regions in images.
Modellquelle
Quellenauszug
HaS (Hide and Seek) is an on-device image privacy model that performs pixel-level detection and segmentation of sensitive regions in images.
Quellen
1 QuelleVerifiziert 27. Sept.
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge--- license: mit language: - zh - en tags: - privacy - anonymization - image-segmentation - pii - hide-and-seek - on-device - yolo11 library_name: ultralytics pipeline_tag: image-segmentation --- # 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. | Capability | Description | |------------|-------------| | **21 Privacy Categories** | Covers biometrics (face, fingerprint, palmprint), ID documents (ID card, passport, permit), financial (bank card), screens (mobile, monitor), and more | | **Pixel-Level Segmentation** | Instance segmentation, not just bounding boxes — masks follow the exact contour of each sensitive region | | **Multiple Redaction Methods** | Supports mosaic, Gaussian blur, and solid fill — configurable strength and style per use case | | **Batch Processing** | Process entire directories of images in one command | | **Category Filtering** | Selectively redact only specific categories (e.g., faces only, or faces + license plates) | | **High Speed** | ~20ms per image, suitable for real-time and batch workflows | ## 2. Supported Privacy Categories 6 groups, 21 categories: | Group | ID | Category | Description | |-------|----|----------|-------------| | **Biometrics** | 0 | `face` | Human faces | | | 1 | `fingerprint` | Fingerprints | | | 2 | `palmprint` | Palm prints | | **ID Documents** | 3 | `id_card` | National ID cards | | | 4 | `hk_macau_permit` | HK/Macau travel permits | | | 5 | `passport` | Passports | | | 6 | `employee_badge` | Employee badges | | **Transportation** | 7 | `license_plate` | Vehicle license plates | | **Financial** | 8 | `bank_card` | Bank/credit cards | | **Security** | 9 | `physical_key` | Physical keys | | **Documents** | 10 | `re...
Source context: 203 downloads · 4 likes · Pipeline image-segmentation · Library ultralytics · Repo xuanwulab/HaS_Image_0209_FP32