Omkar2118/datasets huggingface dataset:
COCO-WAN (Medium noise)
Full name: Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters

The COCO-WAN benchmark is designed to assess the impact of weakly annotations (combined with auto-annotation tools) noise on instance segmentation models. This benchmark is built upon the COCO dataset and incorporates noise generated through weak annotations, simulating real-world scenarios where annotations might be imperfect due to semi-automated tools. It includes various levels of noise to challenge the robustness and generalization capabilities of segmentation models.

Accurately labeling instance segmentation datasets is a complex and error-prone task, often leading to noisy labels. The COCO-WAN benchmark aims to provide a realistic testing ground for models to handle such noisy annotations. By utilizing foundation models and weak annotations, COCO-WAN simulates semi-automated annotation tools, helping researchers understand how well their models can perform under less-than-ideal labeling conditions. This benchmark includes multiple noise levels (easy, medium, and hard) to reflect varying degrees of annotation imperfections.

Potential Use Cases of the Dataset:

Model Robustness Testing: Researchers can use COCO-WAN to evaluate how different instance segmentation models cope with noisy annotations, allowing for the development of more resilient algorithms.
Annotation Tool Improvement: By analyzing model performance on COCO-WAN, developers of annotation tools can identify common pitfalls and work on reducing noise in their outputs.

Semi-Automated Annotation Systems: The benchmark provides insights into how models trained with semi-automated annotations perform, guiding improvements in such systems for better accuracy and efficiency in labeling tasks.
The COCO-WAN benchmark offers a crucial resource for advancing the field of instance segmentation by highlighting the challenges posed by noisy labels and fostering the creation of more robust and reliable models.

Model Robustness Testing: Researchers can use COCO-WAN to evaluate how different instance segmentation models cope with spatial, real noisy annotations, allowing for the development of more resilient algorithms.

Semi-Automated Annotation Systems: The benchmark provides insights into how models trained with semi-automated annotations perform, guiding improvements in such systems for better accuracy and efficiency in labeling tasks.

The COCO-WAN benchmark offers a crucial resource for advancing the field of instance segmentation by highlighting the challenges posed by noisy labels and fostering the creation of more robust and reliable models.

Introducing paper: Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters