| ---
|
| dataset_info:
|
| config_name: detection
|
| features:
|
| - name: id
|
| dtype: string
|
| - name: image
|
| dtype: image
|
| - name: width
|
| dtype: int32
|
| - name: height
|
| dtype: int32
|
| - name: license
|
| dtype: string
|
| - name: license_url
|
| dtype: string
|
| - name: flickr_url
|
| dtype: string
|
| - name: coco_url
|
| dtype: string
|
| - name: objects
|
| struct:
|
| - name: annotation_id
|
| list: int64
|
| - name: category
|
| list:
|
| class_label:
|
| names:
|
| '0': person
|
| '1': bicycle
|
| '2': car
|
| '3': motorcycle
|
| '4': airplane
|
| '5': bus
|
| '6': train
|
| '7': truck
|
| '8': boat
|
| '9': traffic light
|
| '10': fire hydrant
|
| '11': stop sign
|
| '12': parking meter
|
| '13': bench
|
| '14': bird
|
| '15': cat
|
| '16': dog
|
| '17': horse
|
| '18': sheep
|
| '19': cow
|
| '20': elephant
|
| '21': bear
|
| '22': zebra
|
| '23': giraffe
|
| '24': backpack
|
| '25': umbrella
|
| '26': handbag
|
| '27': tie
|
| '28': suitcase
|
| '29': frisbee
|
| '30': skis
|
| '31': snowboard
|
| '32': sports ball
|
| '33': kite
|
| '34': baseball bat
|
| '35': baseball glove
|
| '36': skateboard
|
| '37': surfboard
|
| '38': tennis racket
|
| '39': bottle
|
| '40': wine glass
|
| '41': cup
|
| '42': fork
|
| '43': knife
|
| '44': spoon
|
| '45': bowl
|
| '46': banana
|
| '47': apple
|
| '48': sandwich
|
| '49': orange
|
| '50': broccoli
|
| '51': carrot
|
| '52': hot dog
|
| '53': pizza
|
| '54': donut
|
| '55': cake
|
| '56': chair
|
| '57': couch
|
| '58': potted plant
|
| '59': bed
|
| '60': dining table
|
| '61': toilet
|
| '62': tv
|
| '63': laptop
|
| '64': mouse
|
| '65': remote
|
| '66': keyboard
|
| '67': cell phone
|
| '68': microwave
|
| '69': oven
|
| '70': toaster
|
| '71': sink
|
| '72': refrigerator
|
| '73': book
|
| '74': clock
|
| '75': vase
|
| '76': scissors
|
| '77': teddy bear
|
| '78': hair drier
|
| '79': toothbrush
|
| - name: category_id
|
| list: int32
|
| - name: bbox
|
| list:
|
| list: float64
|
| length: 4
|
| - name: area
|
| list: float64
|
| - name: iscrowd
|
| list: int8
|
| - name: segmentation
|
| list: string
|
| splits:
|
| - name: train
|
| num_bytes: 20208266269
|
| num_examples: 118287
|
| - name: validation
|
| num_bytes: 828791908
|
| num_examples: 5000
|
| download_size: 20532028806
|
| dataset_size: 21037058177
|
| configs:
|
| - config_name: detection
|
| data_files:
|
| - split: train
|
| path: detection/train-*
|
| - split: validation
|
| path: detection/validation-*
|
| default: true
|
| size_categories:
|
| - 100K<n<1M
|
| license_link: https://cocodataset.org/#termsofuse
|
| extra_gated_fields:
|
| I will check the license of each image before using it: checkbox
|
| I will not use images whose license forbids commercial use for commercial purposes: checkbox
|
| I will cite COCO in publications that use this dataset: checkbox
|
| pretty_name: COCO 2017
|
| tags:
|
| - coco
|
| - object-detection
|
| - instance-segmentation
|
| - ms-coco
|
| license_name: coco
|
| license: other
|
| task_categories:
|
| - object-detection
|
| extra_gated_prompt: 'COCO 2017 is distributed by the COCO Consortium. The annotations
|
| are licensed under CC BY 4.0. The COCO Consortium does not own the copyright of
|
| the images: the images come from Flickr and remain under their respective Creative
|
| Commons licenses, and about 70% of them are non-commercial. Check the license of
|
| each image (license/license_url) before using it, and cite the original work.'
|
| language:
|
| - en
|
| ---
|
| # COCO 2017
|
|
|
| COCO 2017 instance detection, packed once with the official annotations kept whole.
|
|
|
| ## What is here
|
|
|
| - `train`: 118,287 images (`train2017`)
|
| - `validation`: 5,000 images (`val2017`)
|
| - 80 categories with COCO's official ids, 1 to 90 (12, 26, 29, ... are unused)
|
| - Every official annotation column: annotation id, category id, bbox, area, iscrowd, segmentation
|
| - Every image's official license and source URLs, because the images come from Flickr and are not all under the same license
|
| - All 5,000 validation images are kept, including the 48 that have no annotations; their `objects` lists are empty
|
|
|
| `test2017` is not included: the official test annotations are not public (the test server only), so there is nothing to pack.
|
|
|
| ## Schema
|
|
|
| | column | type | meaning |
|
| | --- | --- | --- |
|
| | `id` | string | image id, the zero-padded file name stem (e.g. `000000000139`) |
|
| | `image` | Image | the original JPEG bytes |
|
| | `width`, `height` | int32 | image size |
|
| | `license`, `license_url` | string | the image's official Flickr license name and URL |
|
| | `flickr_url`, `coco_url` | string | the image's original source URLs |
|
| | `objects.annotation_id` | int64 | official annotation id |
|
| | `objects.category` | ClassLabel | the 80 names in official id order; the index is 0 to 79 |
|
| | `objects.category_id` | int32 | COCO's official category id, 1 to 90 with gaps |
|
| | `objects.bbox` | list of 4 float64 | official `[x, y, w, h]`, absolute pixels, top-left origin |
|
| | `objects.area` | float64 | official area in pixels |
|
| | `objects.iscrowd` | int8 | 1 for a crowd region the official evaluator ignores |
|
| | `objects.segmentation` | string | official segmentation as JSON: polygon lists, or the RLE dict for crowd boxes |
|
|
|
| `category` is the readable class name; `category_id` keeps the official number so a consumer can
|
| map straight back to the COCO category ids. No annotation is filtered, merged or renumbered.
|
|
|
| ## Usage
|
|
|
| ```python
|
| from datasets import load_dataset
|
|
|
| ds = load_dataset("<user>/coco2017", "detection")
|
| ```
|
|
|
| Replace `<user>` with the repo owner.
|
|
|
| ## Source and license
|
|
|
| Official COCO 2017: <https://cocodataset.org/>.
|
|
|
| > The annotations in this dataset are licensed under a Creative Commons Attribution 4.0 License.
|
| > The COCO Consortium does not own the copyright of the images. Use of the images must abide by
|
| > the Flickr Terms of Use. The users of the images accept full responsibility for the use of the
|
| > dataset, including but not limited to the use of any copies of copyrighted images that they may
|
| > create from the dataset.
|
|
|
| Each image's license is in `license`/`license_url`. The images come from Flickr under different
|
| Creative Commons licenses, and about 70% of train and validation images are non-commercial
|
| (`BY-NC`, `BY-NC-SA`, `BY-NC-ND`): check the per-image license before using an image, especially
|
| for commercial work. This packaging adds no rights; use it under the COCO
|
| [terms of use](https://cocodataset.org/#termsofuse) and cite the original work.
|
|
|
| ```bibtex
|
| @inproceedings{lin2014microsoft,
|
| title={Microsoft COCO: Common Objects in Context},
|
| author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence},
|
| booktitle={European Conference on Computer Vision (ECCV)},
|
| pages={740--755},
|
| year={2014},
|
| organization={Springer}
|
| }
|
| ```
|
|
|
| ## Reproducing this dataset
|
|
|
| `create_dataset.py` in this repo builds the parquet from an unpacked official COCO 2017 folder:
|
|
|
| ```bash
|
| python create_dataset.py --dry-run --coco-root /path/to/coco --limit 20
|
| python create_dataset.py --repo <user>/coco2017 --coco-root /path/to/coco
|
| ```
|
|
|
| It checks the packed box counts, a random sample of records and the image bytes against the
|
| official `instances_*.json` files before uploading.
|
|
|
