Datasets › Segmentation in the Wild
Segmentation in the Wild
Recent advances in language-image pre-training has witnessed the emerging field of building transferable systems that can effortlessly adapt to a wide range of computer vision & multimodal tasks in the wild. This also poses a challenge to evaluate the transferability of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. "Segmentation in the Wild (SegInW)" Challenge is a part of X-Decoder, that proposed a new benchmark to evaluate the transfer ability of pre-trained vision models. This benchmark presents a diverse set of downstream segmentation datasets, measuring the ability of pre-training models on both the segmentation accuracy and their transfer efficiency in a new task, in terms of training examples and trainable parameters. This SegInW Challenge consists of 25 free public Segmentation datasets, crowd-sourced on roboflow.com. For more details about the challenge submission format, please refer to X-Decoder for SGinW.
Benchmarks archive 2025-07-28
All 1 leaderboard 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 | ||||
|---|---|---|---|---|---|---|
| Zero Shot Segmentation | Segmentation in the Wild | Grounded HQ-SAM Mean AP 49.6 | Segment Anything in High Quality | huggingface/transformers +3 | 12 | Compare |
Papers archive 2025-07-28
9 shown of 9 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 15. 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 | |||
|---|---|---|---|---|
| OpenSD: Unified Open-Vocabulary Segmentation and Detection | 0 | 1 | 10 Dec 2023 | not harvested |
| Hierarchical Open-vocabulary Universal Image Segmentation | 1 | 1 | 3 Jul 2023 | ran 2 of 4 samples (2 unverified) |
| Segment Anything in High Quality | 4 | 1 | 2 Jun 2023 | ran 3 of 17 samples (14 unverified; 3 pointer-only for licence) |
| A Simple Framework for Open-Vocabulary Segmentation and Detection | 2 | 1 | 14 Mar 2023 | not harvested |
| Universal Instance Perception as Object Discovery and Retrieval | 1 | 1 | 12 Mar 2023 | ran 3 of 4 samples (1 unverified) |
| Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection | 10 | 1 | 9 Mar 2023 | ran 2 of 5 samples (3 unverified) |
| Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | 1 | 1 | 8 Mar 2023 | not harvested |
| Side Adapter Network for Open-Vocabulary Semantic Segmentation | 3 | 1 | 23 Feb 2023 | ran 1 of 9 samples (8 unverified) |
| Generalized Decoding for Pixel, Image, and Language | 1 | 4 | 21 Dec 2022 | 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
- Segmentation in the Wild
1 variant name, as the archive lists them.
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