foolwood/DaSiamRPN
[ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2942
- days_rel: n/a
- days_push: 2429
- n_releases_24m: 0
Adoption not part of the score
1299 stars · 363 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
library · maturity maintenance
computer-vision machine-learning deep-learning computer-vision deep-learning machine-learning python object-tracking siamese-networks pytorch eccv2018 research-code vot-benchmark linux gpu
1 source
- readme: https://github.com/foolwood/DaSiamRPN · fetched 2026-08-28 · 5e1fb6a3dbbb
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| foolwood/DaSiamRPN | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="foolwood/DaSiamRPN")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem