Add COCO datasets
## Adding a Dataset
- **Name:** COCO
- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.
- **Paper + website:** https://cocodataset.org/#home
- **Data:** https://cocodataset.org/#download
- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.
@NielsRogge  I started adding COCO, will be done tomorrow EOD
my work so far https://github.com/merveenoyan/datasets (my fork) Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo? Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.  > Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.

It's really nice of you!! I see you've commited another version just now @yixuanren we're working on it, will be available soon, thanks a lot for your patience Hi @NielsRogge and @merveenoyan, did you find a way to load a dataset with COCO annotations to HF's hub?
I have a panoptic segmentation dataset in COCO format and would like to share it with the community.
Thanks in advance :) The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) > The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)

Hi @lhoestq , thank you for your quick reply.
I've correctly created a metadata.jsonl file for a dataset with instance segmentation annotations [here](https://huggingface.co/datasets/lombardata/data_2017)
but do not understand how I can integrate panoptic annotations with the metadata format of ImageFolder datasets. The "problem" with panoptic annotations is that we have a folder with images, a json file with annotations and another folder with png annotations.

I checked between all the datasets already published on HuggingFace and, the only one who has uploaded a correct panoptic dataset is @NielsRogge [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo). Indeed he accomplished to have three fields : 
1.image (image)
2.label (image)
3.segments_info (list)
but I not find the corresponding code that allows to upload a panoptic dataset from this 3 sources.
Can you please share an example code?
Thanks ! Both were uploaded using `ds.push_to_hub()` :)

You can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column("image", Image())`.

```python
from datasets import Dataset, Image

ds = Dataset.from_dict(...)
ds = ds.cast_column("image", Image())
ds = ds.cast_column("label", Image())
ds.push_to_hub(...)
``` > Both were uploaded using `ds.push_to_hub()` :)
> 
> You can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column("image", Image())`.
> 
> ```python
> from datasets import Dataset, Image
> 
> ds = Dataset.from_dict(...)
> ds = ds.cast_column("image", Image())
> ds = ds.cast_column("label", Image())
> ds.push_to_hub(...)
> ```

Thank you very much @lhoestq , I succesfully created a hf dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_21) with the two fields :
1.image (image)
2.label (image)
following your suggestions. Now still remain the problem of uploading **segments_info** information to the dataset.
There is a function that easily imports the _panoptic_coco_annotation.json_ file to a segment_info field?
I think we must define a **list_of_segment**, i.e. a list of lists  of this type : 
```python
[ { "area": 214858, "bbox": [ 0, 0, 511, 760 ], "category_id": 0, "id": 7895160, "iscrowd": 0 }, { "area": 73067, "bbox": [ 98, 719, 413, 253 ], "category_id": 3, "id": 3289680, "iscrowd": 0 }, { "area": 832, "bbox": [ 53, 0, 101, 16 ], "category_id": 5, "id": 5273720, "iscrowd": 0 }, { "area": 70668, "bbox": [ 318, 60, 191, 392 ], "category_id": 8, "id": 15132390, "iscrowd": 0 }, { "area": 32696, "bbox": [ 0, 100, 78, 872 ], "category_id": 18, "id": 472063, "iscrowd": 0 }, { "area": 76045, "bbox": [ 42, 48, 264, 924 ], "category_id": 37, "id": 16713830, "iscrowd": 0 }, { "area": 27103, "bbox": [ 288, 482, 216, 306 ], "category_id": 47, "id": 16753408, "iscrowd": 0 } ]
```
and then apply again the **cast_column** function [here](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L2060) but with a list as a second argument, like : 
```python
from datasets import Dataset, Image
ds = ds.cast_column("image", Image())
ds = ds.cast_column("label", Image())
ds = ds.cast_column("segments_info", list)
```
but I do not see how to transfer the information of the _panoptic_coco_annotation.json_ to a list of lists of this type : 
```python
[ { "area": 214858, "bbox": [ 0, 0, 511, 760 ], "category_id": 0, "id": 7895160, "iscrowd": 0 }, { "area": 73067, "bbox": [ 98, 719, 413, 253 ], "category_id": 3, "id": 3289680, "iscrowd": 0 }, { "area": 832, "bbox": [ 53, 0, 101, 16 ], "category_id": 5, "id": 5273720, "iscrowd": 0 }, { "area": 70668, "bbox": [ 318, 60, 191, 392 ], "category_id": 8, "id": 15132390, "iscrowd": 0 }, { "area": 32696, "bbox": [ 0, 100, 78, 872 ], "category_id": 18, "id": 472063, "iscrowd": 0 }, { "area": 76045, "bbox": [ 42, 48, 264, 924 ], "category_id": 37, "id": 16713830, "iscrowd": 0 }, { "area": 27103, "bbox": [ 288, 482, 216, 306 ], "category_id": 47, "id": 16753408, "iscrowd": 0 } ]
```
like @NielsRogge has done  [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo).
Thank you again for your help and have a good day ! You can pass this data in .from_dict() - no need to cast anything for this column

```python
ds = Dataset.from_dict({
    "image": [...],
    "label": [...],
    "segments_info": [...],
)}
```

where `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts. > You can pass this data in .from_dict() - no need to cast anything for this column
> 
> ```python
> ds = Dataset.from_dict({
>     "image": [...],
>     "label": [...],
>     "segments_info": [...],
> )}
> ```
> 
> where `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.

Thank you for the quick reply @lhoestq , but then how to generate the `segments_info` list of lists of dicts starting from a _panoptic_coco_annotation.json_ file ?


 You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere > You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere

Perfect, I've done it and succesfully uploaded a new dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_22), but I've (I hope) a last problem.
The dataset has currently 302 images and, when I upload it to the hub, only the first page of images is correctly uploaded.
When I try to see the second/third/fourth page of items of my dataset, I can see that the fields **segments_info** and **image_name** are correctly uploaded, while the images are not (the "null" string is shown everywhere).

I've checked the path of images that are not uploaded and they exists, is there a problem with the size of the dataset ?
How can I upload the whole dataset to the hub ?
Thank you again @lhoestq and have a good day ! Awesome ! Your dataset looks all good 🤗 

The `null` in the viewer is a bug on our side, let me investigate