COCONut Dataset
Collection
This is a collection of COCONut datasets accepted at CVPR2024
•
3 items
•
Updated
•
2
Datasets:
Tags:
License:
mask
imagewidth (px) 59
640
| segments_info
dict | image_info
dict |
|---|---|---|
{
"file_name": "000000000009.png",
"image_id": 9,
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{
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{
"category_id": 51,
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]
} | {
"coco_url": "http://images.cocodataset.org/train2017/000000000009.jpg",
"date_captured": "2013-11-14 16:03:19",
"file_name": "000000000009.jpg",
"height": 480,
"id": 9,
"license": 3,
"width": 640
} |
|
{
"file_name": "000000000025.png",
"image_id": 25,
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} | {
"coco_url": "http://images.cocodataset.org/train2017/000000000025.jpg",
"date_captured": "2013-11-14 16:03:19",
"file_name": "000000000025.jpg",
"height": 426,
"id": 25,
"license": 3,
"width": 640
} |
|
{
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{
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} | {
"coco_url": "http://images.cocodataset.org/train2017/000000000030.jpg",
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"file_name": "000000000030.jpg",
"height": 428,
"id": 30,
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} |
|
{
"file_name": "000000000034.png",
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} | {
"coco_url": "http://images.cocodataset.org/train2017/000000000034.jpg",
"date_captured": "2013-11-14 16:03:19",
"file_name": "000000000034.jpg",
"height": 425,
"id": 34,
"license": 3,
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} |
|
{
"file_name": "000000000036.png",
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} | {
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"file_name": "000000000036.jpg",
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"id": 36,
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|
{
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"id": 42,
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} |
|
{
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"file_name": "000000000049.jpg",
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"id": 49,
"license": 3,
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|
{
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} |
|
{
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|
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|
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|
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|
{
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} | {
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{
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End of preview. Expand
in Dataset Viewer.
How to download
- Set up environment
pip install datasets tqdm
wget https://raw.githubusercontent.com/bytedance/coconut_cvpr2024/main/download_coconut.py
- Use the download script to download the COCONut dataset splits.
python download_coconut.py --split coconut_s # default split: relabeled_coco_val, need to switch to coconut_s
The above command should print your download status, if you download it successfully you can see the results below:
- 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_b" NOTE: multiple splits download is not yet supported.
python download_coconut.py --split relabeled_coco_val --output_dir relabeled_coco_val
- The mask images are nearly black as we use continuous segment ids for each image, you can use github visualization tutorial to create colorful masks for viewing.
Please go to our offical github repo for detailed usage instruction: https://github.com/bytedance/coconut_cvpr2024
- Downloads last month
- 25
Size of downloaded dataset files:
817 MB
Size of the auto-converted Parquet files:
817 MB
Number of rows:
118,200