Datasets:
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---
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
- CC BY-NC 3.0 IGO
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: 154137131.0
num_examples: 272
- name: valid
num_bytes: 210925170.0
num_examples: 360
download_size: 365017887
dataset_size: 365062301.0
---
# XAMI (**X**MM-Newton Optical **A**rtefact **M**apping for Astronomical **I**nstance Segmentation)
The **Git** repository for this dataset can be found **[here](https://github.com/ESA-Datalabs/XAMI-dataset)**.
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).
# Annotation platforms
The images have been annotated using the following platform:
- [Zooniverse](https://www.zooniverse.org/projects/ori-j/ai-for-artefacts-in-sky-images), where the resulted annotations are not externally visible.
- [Roboflow](https://universe.roboflow.com/iuliaelisa/xmm_om_artefacts_512/), 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](Datasets-Structure.md) for a more detailed structure of our dataset in COCO-IS and YOLOv8-Seg format.
# Downloading the dataset
Please check [load_and_visualise_dataset.pynb](https://github.com/ESA-Datalabs/XAMI-dataset/blob/main/load_and_visualise_dataset.ipynb)) formore visualization tools.
```python
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" -d 'path/to/dest'
```
Or you can simply download only the dataset zip file from HuggingFace using a CLI command:
```bash
DEST_DIR='/path/to/local/dataset/dir'
huggingface-cli download iulia-elisa/XAMI-dataset xami_dataset.zip --repo-type dataset --local-dir "$DEST_DIR" && unzip "$DEST_DIR/xami_dataset.zip" -d "$DEST_DIR" && rm "$DEST_DIR/xami_dataset.zip"
```
## Licence
**[CC BY-NC 3.0 IGO](https://creativecommons.org/licenses/by-nc/3.0/igo/deed.en).**
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