#Coco 2017 download how to#
Check out the next section to see how to easily load it back into Python. The idea is to load each image and associated labels as a FiftyOne Sample and add them to a FiftyOne Dataset:Īnd there you have it! /path/to/coco-detection-dataset now contains your images and labels in COCO format. If your data is not stored in a supported format, then it is still easy to load it into FiftyOne using Python and export it in COCO format. # Convert a COCO detection dataset to CVAT image format fiftyone convert \ -input-dir /path/to/cvat-image-dataset \ -input-type \ -output-dir /path/to/coco-detection-dataset \ -output-type If your dataset happens to follow a different common format that is supported by FiftyOne, like CVAT, YOLO, KITTI, Pascal VOC, TF Object detection, or others, then you can load and convert it to COCO format in a single command. In this case, you already have a dataset with images and annotations but want to convert it to the COCO format. This section will outline how to take your raw or annotated dataset and convert it to the COCO format depending on what data you currently have and the format it is in. This also stores bounding box area and iscrowd indicating a large bounding box surrounding multiple objects of the same category which is used for evaluation. This is where you will store the bounding box information in our case or segmentation/keypoint/other label information for other tasks.
#Coco 2017 download download#
If you were to download the COCO dataset from their website, this would be the instances_train2017.json and instances_val2017.json files. If you have multiple splits of data, they would be stored in different directories with different json files. The dataset is stored in a directory containing your raw image data and a single json file that contains all of the annotations, metadata, categories, and other information that you could possibly want to store about your dataset. The folder structure of a COCO dataset looks like this: / data/. This section will explain what the file and folder structure of a COCO formatted object detection dataset actually looks like.Īt a high level, the COCO format defines exactly how your annotations (bounding boxes, object classes, etc) and image metadata (like height, width, image sources, etc) are stored on disk. If you are new to the object detection space and are tasked with creating a new object detection dataset, then following the COCO format is a good choice due to its relative simplicity and widespread usage. See this post or this documentation for more details! COCO file format
#Coco 2017 download install#
You can easily install FiftyOne through pip: pip install fiftyone It’s designed to let researchers and engineers easily work with and visualize image and video datasets with annotations and model predictions stored in various formats. In order to do all of this, I’ll be using the open-source machine learning developer tool, FiftyOne, that I have been working on.
Many blog posts exist that describe the basic format of COCO, but they often lack detailed examples of loading and working with your COCO formatted data. The “ COCO format” is a specific JSON structure dictating how labels and metadata are saved for an image dataset. For now, we will focus only on object detection data. While the COCO dataset also supports annotations for other tasks like segmentation, I will leave that to a future blog post. It is widely used to benchmark the performance of computer vision methods.ĭue to the popularity of the dataset, the format that COCO uses to store annotations is often the go-to format when creating a new custom object detection dataset. Microsoft's Common Objects in Context dataset ( COCO) is the most popular object detection dataset at the moment.
Image 001298.jpg from the COCO dataset visualized in FiftyOne (Image by author)