[CVPR2024] 🥥COCONut: Crafting the Future of Segmentation Datasets with Exquisite Annotations in the Era of ✨Big Data✨
Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen
🔥 1st large-scale human verified dataset for segmentation, more info can be found at our website.
🔥 COCONut is now available at Kaggle and huggingface, welcome to download!
- 9/9: Relase a tutorial to prepare all dataset splits for training and evaluation.
- 6/24: Release COCONut-val and instance segmentation annotations.
- 5/6: Tutorial on semantic segmentation is out!
- 4/30: Tutorials on open-vocabulary segmentation and object detection are out!
- 4/28: COCONut is back to huggingface. relabeled COCO-Val, COCONut-S, and COCONut-B are available.
- 4/25: Tutorial on visualizing COCONut panoptic masks using detectron2. Turn the black mask image into overlayed colorful mask.
- 4/24: Collected FAQs are out, please check them before you leave any issue.
- 4/22: Tutorial on instance segmentation is out! More are coming!
- 4/19: Tutorial on panoptic segmentation is out!
- 4/16: COCONut is available at Kaggle! No need to merge COCONut-B from COCONut-S, update a version of ready-to-use.
- 4/15: COCONut is higlighted by AK's daily paper!
- 4/15: Huggingface download links are temporarily closed.
- Huggingface dataset preview on relabeled COCO-Val and COCONut-S
- Huggingface preview on COCONut-B
- Convert the annotation to semantic segmentation.
- Release COCONut-val and instance segmentation annotations (no need to convert from the panoptic masks).
- Release COCONut-L.
| Splits | #images | #masks | images | kaggle | huggingface |
|---|---|---|---|---|---|
| COCONut-S | 118K | 1.54M | download | download | preview |
| COCONut-B | 242K | 2.78M | download | download | preview |
| COCONut-L | 358K | 4.75M | download | downoad | download |
| relabeled-COCO-val | 5K | 67K | download | download | preview |
| COCONut-val | 25K | 437K | download | download | download |
Please refer to 🔗preparing datasets for exploring training and evaluation.
We only provide the annotation, for those who are interested to use our annotation will need to download the images from the links: COCONut-S images, COCONut-B images and relabeled COCO-val images.
We provide two methods to download the dataset annotations, details are as below。
You can use the web UI to download the dataset directly on Kaggle.
If you find our dataset useful, we really appreciate if you can upvote our dataset on Kaggle,
Directly download the data from huggingface or git clone the huggingface dataset repo will result in invalid data structure.
We recommend you to use our provided download script to download the dataset from huggingface.
pip install datasets tqdm
python download_coconut.py # default split: relabeled_coco_val
You can switch to download COCONut-S by adding "--split coconut_s" to the command.
python download_coconut.py --split coconut_s
The data will be saved at "./coconut_datasets" by default, you can change it to your preferred path by adding "--output_dir YOUR_DATA_PATH".
To use COCONut-Large, you need to download the panoptic masks from huggingface and copy the images by the image list from the objects365 image folder. Then add them on top of COCONut-B, to consist the full COCONut-Large dataset.
We summarize the common issues in FAQ.md, please check this out before you create any new issues.
- We follow the same license as COCO dataset for images. The dataset cannot be used for commercial purposes.
- This is not an official ByteDance product. The dataset is created for research purposes.
If you find our dataset useful, please cite:
@inproceedings{coconut2024cvpr,
author = {Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen},
title = {COCONut: Modernizing COCO Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2024},