DIS5K: Highly Accurate Dichotomous Image Segmentation Dataset

DIS5K is a dataset for dichotomous image segmentation, ported from the DIS project. The dataset was created by Xuebin Qin and colleagues, with research presented at ECCV in 2022. Its specific scale and file formats are not detailed in the provided metadata.

Use Cases

• Training segmentation models based on the dichotomous image segmentation task.
• Benchmarking segmentation algorithm accuracy against the DIS5K standard.
• Researching fine-grained object boundary detection in images.

Strengths

• Dataset is associated with peer-reviewed research presented at ECCV 2022.
• The underlying DIS project is described as focusing on 'Highly Accurate' segmentation.

Limitations

• Row count, file formats, and column-level documentation are unknown.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Port from the DIS project (https://xuebinqin.github.io/dis/).

Freshness

Last updated 2025-06-04 19:47:04; freshness should be verified.