# foolwood/DaSiamRPN

[ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking

Repository: https://github.com/foolwood/DaSiamRPN
Canonical: https://ross.abutalabs.com/products/dasiamrpn
Language: Python
License: MIT
License Family: permissive
Last push: 2020-01-08T09:29:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2942, "days_push": 2429, "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 1299, forks 363 (observed 2026-08-28T04:04:17.466416+00:00)

## What it is
PyTorch implementation of DaSiamRPN, an ECCV 2018 distractor-aware Siamese network for visual object tracking, winner of the VOT-18 real-time challenge. It includes pretrained models and evaluation code for benchmarks like VOT and OTB.

## Use cases
- visual object tracking in video
- reproduce VOT2018 benchmark results
- run a real-time single-object tracker
- research on Siamese network trackers
- long-term tracking with distractor rejection

## When to choose
- you need a proven real-time visual tracker with pretrained weights
- you're reproducing or comparing against ECCV2018 tracking baselines
- you're researching distractor-aware tracking

## When to avoid
- you need a maintained library with modern PyTorch support (requires pytorch 0.3.1, python 2.7)
- you need multi-object tracking
- you want production-ready tracking in a modern pipeline

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, deep-learning
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: object-tracking, siamese-networks, pytorch, eccv2018, research-code, vot-benchmark, linux, gpu

## Member repositories
- foolwood/DaSiamRPN (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:17.466416+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 2026-08-30T04:53:25.982076+00:00, confidence not recorded.
  - readme: https://github.com/foolwood/DaSiamRPN (fetched 2026-08-28T04:04:17.466416+00:00, sha 5e1fb6a3dbbb)
- Data as of 2026-08-30T08:39:29.467469+00:00.