metadata

size_categories:
  - 1K<N<10K
source_datasets:
  - original
task_categories:
  - image-segmentation
task_ids:
  - instance-segmentation
pretty_name: XAMI-dataset
tags:
  - COCO format
  - Astronomy
  - XMM-Newton
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: valid
        path: data/valid-*
dataset_info:
  features:
    - name: observation id
      dtype: string
    - name: segmentation
      dtype: image
    - name: bbox
      dtype: image
    - name: label
      dtype: string
    - name: area
      dtype: string
    - name: image shape
      dtype: string
  splits:
    - name: train
      num_bytes: 154137131
      num_examples: 272
    - name: valid
      num_bytes: 210925170
      num_examples: 360
  download_size: 365017887
  dataset_size: 365062301

XAMI-dataset

The Git repository for this dataset can be found here.

The XAMI dataset contains 1000 annotated images of observations from diverse sky regions of the XMM-Newton Optical Monitor (XMM-OM) image catalog. An additional 50 images with no annotations are included to help decrease the amount of False Positives or Negatives that may be caused by complex objects (e.g., large galaxies, clusters, nebulae).

Artefacts

A particularity of the dataset compared to every-day images are the locations where artefacts usually appear. Examples of an image with multiple artefacts.

Here are some examples of common artefacts in the dataset:

Examples of common artefacts in the OM observations.

Annotation platforms

The images have been annotated using the following platform:

Zooniverse, where the resulted annotations are not externally visible.
Roboflow, which allows for more interactive and visual annotation tools.

The dataset format

The dataset is splited into train and validation categories and contains annotated artefacts in COCO format for Instance Segmentation. We use multilabel Stratified K-fold (k=4) to balance class distributions across splits. We choose to work with a single dataset splits version (out of 4) but also provide means to work with all 4 versions.

Please check Dataset Structure for a more detailed structure of our dataset in COCO-IS and YOLOv8-Seg format.

Downloading the dataset

(Option 1) Downloading the dataset archive from HuggingFace

using a python script:

import os
import json
import pandas as pd
from huggingface_hub import hf_hub_download

dataset_name = 'xami_dataset' # the dataset name of Huggingface
images_dir = '.' # the output directory of the dataset images

hf_hub_download(
    repo_id="iulia-elisa/XAMI-dataset", # the Huggingface repo ID
    repo_type='dataset', 
    filename=dataset_name+'.zip', 
    local_dir=images_dir
);

# Unzip file
!unzip -q "xami_dataset.zip" 

# Read the train json annotations file
annotations_path = os.path.join(images_dir, dataset_name, 'train/', '_annotations.coco.json')

with open(annotations_path) as f:
    data_in = json.load(f)

data_in['images'][0]

using a CLI command:

huggingface-cli download iulia-elisa/XAMI-dataset xami_dataset.zip --repo-type dataset --local-dir '/path/to/local/dataset/dir'

(Option 2) Cloning the repository for more visualization tools

# Github
git clone https://github.com/ESA-Datalabs/XAMI-dataset.git
cd XAMI-dataset

Licence

...