COCO dataset format upload

Community Help
1

Hi I’m trying to upload a Coco dataset, if I understand correctly there seem to be two ways to do this.
One way is to create a json file for each image file like yolo, and the other way is to assign an id to each image and write annotations for all images in one json file.
Which method should I upload the coco json file to roboflow?
And another thing is, is it necessary to use up to 2 decimal places for the segmentation variable in the json file?

Thanks for your help!

2

To upload a COCO dataset to Roboflow, you would typically use a single JSON file that contains annotations for all images. Each image is assigned an ID, and the annotations for all images are written in this one JSON file. This is the standard format for COCO datasets.

As for the precision of the segmentation variable in the JSON file, the extracts do not provide specific information on this. However, in general, the precision you choose to use could depend on the level of accuracy you need for your specific use case.

To upload your dataset to Roboflow, you can use the Web User Interface, the API, or by forking/downloading a publicly available image dataset from Roboflow Universe. Here’s a Python SDK example of how to upload a dataset to a project that already exists within Roboflow:

import roboflow
rf = roboflow.Roboflow(api_key=YOUR_API_KEY_HERE)
project = rf.workspace().project("PROJECT_ID")
project.upload_dataset( dataset_path="./dataset/", num_workers=10, dataset_format="yolov8", project_license="MIT", project_type="object-detection")

In the upload_dataset method, you would replace "yolov8" with "coco" for the dataset_format parameter.

For more details, you can refer to the Roboflow documentation on uploading a dataset.

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