VIPER (clean) — images + clean COCO-format instance segmentation annotations

This dataset repo packages the VIPER images together with clean COCO instance segmentation annotations, as used in:

• Paper: Noisy Annotations in Semantic Segmentation (Kimhi et al., 2025)
  • arXiv: https://arxiv.org/abs/2406.10891
• Code/tools for noisy-label benchmarks: https://github.com/mkimhi/noisy_labels

If you are looking for the noisy benchmark labels (annotations-only), see:

• VIPER-N: https://huggingface.co/datasets/kimhi/viper-n

All datasets are grouped in this collection:

• Noisy Labels for Instance Segmentation (COCO-format): https://huggingface.co/collections/Kimhi/noisy-labels-for-instance-segmentation-coco-format

What’s inside

Images

• images/train/... (VIPER train images)
• images/val/... (VIPER val images)

Clean annotations (COCO instances)

• coco/annotations/instances_train2017.json
• coco/annotations/instances_val2017.json

Qualitative gallery (optional)

• reports/gallery/*/index.html

Loading code snippets

1) Download VIPER from the Hub

from huggingface_hub import snapshot_download

viper_root = snapshot_download("kimhi/viper", repo_type="dataset")

images_root = f"{viper_root}/images"
ann_train = f"{viper_root}/coco/annotations/instances_train2017.json"
ann_val   = f"{viper_root}/coco/annotations/instances_val2017.json"

print(images_root)
print(ann_val)

2) Read COCO annotations with pycocotools

from pycocotools.coco import COCO

coco = COCO(ann_val)
img_id = coco.getImgIds()[0]
img = coco.loadImgs([img_id])[0]
print(img)

ann_ids = coco.getAnnIds(imgIds=[img_id])
anns = coco.loadAnns(ann_ids)
print("#instances in image:", len(anns))

Using VIPER with VIPER-N (noisy labels)

Download both repos and swap the annotation JSONs:

from huggingface_hub import snapshot_download

viper_root   = snapshot_download("kimhi/viper", repo_type="dataset")
viper_n_root = snapshot_download("kimhi/viper-n", repo_type="dataset")

images_root = f"{viper_root}/images"
ann_val_noisy = f"{viper_n_root}/benchmark/annotations/instances_val2017.json"

Applying the noise recipe to other datasets

See the paper repo for scripts/recipes to generate/apply noisy labels to other COCO-format instance segmentation datasets:

• https://github.com/mkimhi/noisy_labels

Dataset viewer

Hugging Face’s built-in dataset viewer does not currently render COCO instance-segmentation JSONs directly. You can still browse images in the Files tab, and use pycocotools/Detectron2/MMDetection to visualize masks.

Citation

@misc{kimhi2025noisyannotationssemanticsegmentation,
  title={Noisy Annotations in Semantic Segmentation},
  author={Moshe Kimhi and Omer Kerem and Eden Grad and Ehud Rivlin and Chaim Baskin},
  year={2025},
  eprint={2406.10891},
}

License

CC BY-NC 4.0 — Attribution–NonCommercial 4.0 International.