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viper

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computer-vision
instance-segmentation
dataset
sim2real
viper
noisy-labels
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1
viper
File size: 3,113 Bytes
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---
license: cc-by-nc-4.0
language: en
tags:
- computer-vision
- instance-segmentation
- dataset
- sim2real
- viper
- noisy-labels
---

# 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
```python
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`
```python
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:

```python
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
```bibtex
@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.