README.md

---
license: cc-by-nc-4.0
language: en
tags:
- computer-vision
- instance-segmentation
- dataset
- benchmark
- noisy-labels
- sim2real
- viper
- coco
---

# VIPER-N — Noisy-label benchmark for **instance segmentation** (COCO-format annotations)

**VIPER-N** provides *noisy* COCO **instance segmentation** annotations for the VIPER dataset, as introduced in:

- Paper: **Noisy Annotations in Semantic Segmentation** (Kimhi et al., 2025)
  - arXiv: https://arxiv.org/abs/2406.10891
- Code/tools to generate/apply noise: https://github.com/mkimhi/noisy_labels

This repo is **annotations-only** (no images). Pair it with **`kimhi/viper`** (VIPER images + clean annotations).

Collection (all related datasets):
- https://huggingface.co/collections/Kimhi/noisy-labels-for-instance-segmentation-coco-format

## What’s included
- COCO instances JSON (same schema as COCO 2017):
  - `benchmark/annotations/instances_train2017.json`
  - `benchmark/annotations/instances_val2017.json`

### Intended use
VIPER-N is meant for **robust instance segmentation under label noise**:
- train/eval with the noisy annotations, or
- compare clean vs noisy, or
- evaluate noise-robust learning methods.

## How to use (apply VIPER-N on top of VIPER)
You need the VIPER images and (optionally) clean labels from **`kimhi/viper`**.

### Option A — keep a COCO-like folder layout
Assume you have:
- VIPER images at: `.../viper/images/...`
- VIPER clean labels at: `.../viper/coco/annotations/instances_{train,val}2017.json`

To evaluate/train with VIPER-N, simply point your dataloader to the JSONs in this repo:
- `.../viper-n/benchmark/annotations/instances_train2017.json`
- `.../viper-n/benchmark/annotations/instances_val2017.json`

### Option B — overwrite the annotation files (quick & dirty)
Replace the clean VIPER annotation files with the VIPER-N ones **while keeping filenames**:
- overwrite `instances_train2017.json`
- overwrite `instances_val2017.json`

## Loading code snippets

### 1) Download with `huggingface_hub`
```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"  # contains train/val images
ann_train = f"{viper_n_root}/benchmark/annotations/instances_train2017.json"
ann_val   = f"{viper_n_root}/benchmark/annotations/instances_val2017.json"

print(images_root)
print(ann_train)
```

### 2) Read COCO annotations with `pycocotools`
```python
from pycocotools.coco import COCO

coco = COCO(ann_val)
img_ids = coco.getImgIds()[:5]
imgs = coco.loadImgs(img_ids)
print(imgs[0])

ann_ids = coco.getAnnIds(imgIds=img_ids[0])
anns = coco.loadAnns(ann_ids)
print(len(anns), anns[0].keys())
```

## Applying the same noise recipe to *other* datasets
See the paper repo for scripts and recipes to generate/apply noisy labels to other COCO-format instance segmentation datasets:
- https://github.com/mkimhi/noisy_labels

(High-level idea: convert dataset → COCO instances JSON → apply noise model → export new `instances_*.json`.)

## Dataset viewer
Hugging Face’s built-in dataset viewer does not currently render COCO instance-segmentation JSONs directly.
Use the snippets above (or your training pipeline) 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.