segformer-b0-finetuned-segments-construction-1

Key Features & Capabilities Zero‑shot segmentation: No task‑specific training required – simply describe the target object in plain English. One‑shot segmentation: Provide a reference mask to guide the model for fine‑grained control.

Model ID: briaai/RMBG-1.4 – a state‑of‑the‑art background‑removal model built on the IS‑Net architecture.

BiRefNet (Bilateral Reference for High‑Resolution Dichotomous Image Segmentation) is a deep‑learning model designed to produce precise binary masks for a wide range of image‑segmentation tasks.

What is this model? This is a lightweight, vision‑transformer‑based image‑segmentation model that has been fine‑tuned on the ADE20K dataset. The model is packaged as ONNX weights so it can be run directly in browsers or Node.js via Transformers.js .