PaddleSeg

This page documents the current PaddleSeg integration status for RankSEG.

Current status

The PaddleSeg integration already exists, but it is currently maintained outside the main rankseg branch.

At this stage, the main repository provides an entry point to that work rather than duplicating or re-implementing the full PaddleSeg integration locally.

How it fits the RankSEG integration pattern

The insertion point is the same as the first-party integrations:

PaddleSeg model -> probability map -> convert to PyTorch tensor
-> RankSEG -> prediction mask

The difference is maintenance scope. RankSEG’s public predictor currently expects a PyTorch tensor, so Paddle outputs need an explicit conversion step before calling RankSEG.predict.

import torch
from rankseg import RankSEG

# probs_paddle has shape (batch_size, num_classes, height, width)
probs = torch.from_numpy(probs_paddle.numpy())

rankseg = RankSEG(metric="dice", solver="RMA", output_mode="multiclass")
preds = rankseg.predict(probs)

Who should use this path

This path is useful if:

  • you already deploy segmentation pipelines in PaddleSeg;

  • you want to evaluate RankSEG as an inference-time post-processing module in the Paddle ecosystem;

  • you are comfortable using an externally maintained integration branch.

Available entry points

Scope and maintenance

This integration is currently treated as an external/community-maintained path.

That means:

  • the main RankSEG repository links to it;

  • the main RankSEG repository does not yet treat it as a first-party official integration path;

  • once the integration assets become stable enough, it can be promoted into a first-party maintained path later.

Relationship to official integrations

If you are new to RankSEG, the recommended first entry point remains:

That path is maintained directly in this repository and is the current default official integration route.