---
dataset_info:
  features:
  - name: image_name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: overlaid_mask_1
    dtype: image
  - name: overlaid_mask_2
    dtype: image
  splits:
  - name: train
    num_bytes: 236064088
    num_examples: 1000
  - name: test
    num_bytes: 68819435
    num_examples: 250
  download_size: 287318302
  dataset_size: 304883523
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: cc-by-nc-4.0
pretty_name: CAMO
task_categories:
- image-segmentation
tags:
- camouflaged-object-detection
- camouflage
- segmentation
- background-removal
size_categories:
- 1K<n<10K
---

# CAMO (Camouflaged Object dataset)

Camouflaged object segmentation dataset, mirrored to the `nobg` org for convenience. Each example is an RGB image of a camouflaged object paired with its binary ground-truth mask.

- `image_name`: original file name
- `image`: RGB source image
- `mask`: binary camouflaged-object ground-truth mask
- `overlaid_mask_1`, `overlaid_mask_2`: visualizations of the mask overlaid on the image

Splits: `train` (CAMO-TR) and `test` (CAMO-TE).

## Source & credit

From the **CAMO** dataset introduced in **"Anabranch Network for Camouflaged Object Segmentation"** (Le et al., CVIU 2019). Project page: https://sites.google.com/view/ltnghia/research/camo. Redistributed here for research / non-commercial use.

## Citation

```bibtex
@article{le2019anabranch,
  title={Anabranch Network for Camouflaged Object Segmentation},
  author={Le, Trung-Nghia and Nguyen, Tam V. and Nie, Zhongliang and Tran, Minh-Triet and Sugimoto, Akihiro},
  journal={Computer Vision and Image Understanding},
  volume={184},
  pages={45--56},
  year={2019},
  publisher={Elsevier}
}
```