Datasets › WildDash
WildDash
WildDash is a benchmark evaluation method is presented that uses the meta-information to calculate the robustness of a given algorithm with respect to the individual hazards.
Source: WildDash - Creating Hazard-Aware Benchmarks Image Source: https://wilddash.cc/
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Domain Generalization | WildDash | ITEN Mean IoU 31.2 | Exploiting Image Translations via Ensemble... | — | 1 | Compare |
| Semantic Segmentation | WildDash | SIW Mean IoU 69.7 | Scaling up Multi-domain Semantic Segmentation with... | — | 1 | Compare |
Papers archive 2025-07-28
2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 47. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings | 0 | 1 | 4 Feb 2022 | not harvested |
| Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation | 0 | 1 | 13 Jul 2021 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- WildDash
1 variant name, as the archive lists them.
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