README.md

---
license: cc
dataset_info:
  features:
  - name: mask
    dtype: image
  - name: segments_info
    struct:
    - name: file_name
      dtype: string
    - name: image_id
      dtype: int64
    - name: segments_info
      list:
      - name: area
        dtype: float64
      - name: category_id
        dtype: int64
      - name: id
        dtype: int64
      - name: iscrowd
        dtype: int64
      - name: isthing
        dtype: int64
  - name: image_info
    struct:
    - name: coco_url
      dtype: string
    - name: date_captured
      dtype: string
    - name: file_name
      dtype: string
    - name: height
      dtype: int64
    - name: id
      dtype: int64
    - name: license
      dtype: int64
    - name: width
      dtype: int64
  splits:
  - name: train
    num_bytes: 1799961313.342
    num_examples: 241602
  download_size: 1641153199
  dataset_size: 1799961313.342
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

## How to download
1. Set up environment
```
pip install datasets tqdm
wget https://raw.githubusercontent.com/bytedance/coconut_cvpr2024/main/download_coconut.py
```

2. Use the download script to download the COCONut dataset splits.
```
python download_coconut.py --split coconut_b # default split: relabeled_coco_val, need to switch to coconut_b
```


3. Download other COCONut dataset splits.

If you want to download the other splits, you can replace the split name to "relabeled_coco_val" or "coconut_s"
NOTE: multiple splits download is not yet supported.

```
python download_coconut.py --split relabeled_coco_val --output_dir relabeled_coco_val
```

4. The mask images are nearly black as we use continuous segment ids for each image, you can use [github visualization tutorial](https://github.com/bytedance/coconut_cvpr2024/blob/main/tutorials/visualization/demo.ipynb) to create colorful masks for viewing.

Please go to our offical github repo for detailed usage instruction: https://github.com/bytedance/coconut_cvpr2024