# xuebinqin/BASNet

Code for CVPR 2019 paper. BASNet: Boundary-Aware Salient Object Detection

Repository: https://github.com/xuebinqin/BASNet
Canonical: https://ross.abutalabs.com/products/basnet
Language: Python
License: MIT
License Family: permissive
Last push: 2023-09-10T11:28:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2714, "days_push": 1088, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1436, forks 249 (observed 2026-08-28T04:04:43.537137+00:00)

## What it is
BASNet is the official PyTorch implementation of the CVPR 2019 paper 'BASNet: Boundary-Aware Salient Object Detection', a deep learning model for accurately segmenting the most prominent objects in images. It also covers the extended boundary-aware segmentation network for salient objects in clutter (SOC) and camouflaged object detection (COD), with pre-trained models and training/inference scripts.

## Use cases
- segment the main object in a photo with precise boundaries
- run salient object detection on images with a pretrained model
- train a boundary-aware segmentation network on my own dataset
- detect camouflaged objects in images
- generate saliency maps for benchmark datasets like DUTS or ECSSD
- extract foreground objects for image editing or background removal

## When to choose
- you need research-grade salient object detection with strong boundary accuracy
- you want a PyTorch model with pretrained weights for SOD, SOC, or COD
- you are reproducing or building on the CVPR 2019 BASNet paper

## When to avoid
- you need a production-ready, actively maintained segmentation library
- you want a simple API or packaged install rather than running scripts
- you need real-time inference on mobile or web (consider the newer U^2-Net instead)

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: computer-vision, image-processing, deep-learning, machine-learning
- platform: python, cross-platform
- tags: salient-object-detection, segmentation, pytorch, cvpr-2019, boundary-aware, camouflaged-object-detection, research-code

## Member repositories
- xuebinqin/BASNet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.537137+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:36:49.831117+00:00, confidence not recorded.
  - readme: https://github.com/xuebinqin/BASNet (fetched 2026-08-28T04:04:43.537137+00:00, sha 00f64f5f75f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.