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Cascadepsp

CascadePSP is a deep-learning model and PyTorch implementation that refines segmentation masks for high-resolution images using global and local refinement. Its repository provides a pretrained model, training and testing functionality, an annotated UHD dataset called BIG, and a Python package for processing an image and input mask locally.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Cascadepsp — OpenViST project profile in Image & Video AI.
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Understand the project

Practical overview

When it fits

Use CascadePSP when an existing segmentation mask needs refinement for a high-resolution image, or when working with training and testing workflows for segmentation refinement.

Who it is for

  • Computer-vision developers adding mask refinement to an image-processing pipeline.
  • Researchers training or evaluating high-resolution segmentation refinement.
  • Practitioners who already have an image and segmentation mask to refine.

Documented uses

  • Refine an existing segmentation mask using its source image.
  • Integrate mask refinement into a Python computer-vision workflow.
  • Train or test the model using the repository’s documented resources.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. The quick-start example supports CPU or CUDA initialization; no account or pricing information is stated.

  • The package expects both an input image and a corresponding mask; it is a refinement stage, not a documented image-only segmentation workflow.
  • The README describes global and local refinement and offers a fast mode that uses only the global step.
  • The quick start references PyTorch 1.0 as tested and says higher versions would likely work for inference, without guaranteeing compatibility.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Refine a mask with the Python package

Run the documented package example to produce a refined mask from an image and an input mask.

Before you start

  • Python environment with the segmentation-refinement package installed.
  • An image and corresponding grayscale segmentation mask.
  • CUDA device or CPU; the example initializes the refiner on either.

Steps

  1. Install the package with the command below.

    Documented command
    pip install segmentation-refinement

  2. Load the image and its grayscale mask in Python, following the README example.

  3. Initialize Refiner with device='cuda:0' or device='cpu'.

  4. Call refiner.refine(image, mask, fast=False, L=900) and save the returned mask with cv2.imwrite.

Expected outcome

A refined mask is written to an image file.

Guide limitations

  • The example says model loading takes some time.
  • In fast mode, a smaller L uses less memory and is faster; the README does not provide a broader performance guarantee.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
hkchengrex/CascadePSP · Official homepage title · retained in its source language
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