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--- |
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size_categories: |
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- 1K<N<10K |
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source_datasets: |
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- original |
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task_categories: |
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- image-segmentation |
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task_ids: |
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- instance-segmentation |
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pretty_name: XAMI-dataset |
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tags: |
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- COCO format |
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- Astronomy |
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- XMM-Newton |
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- CC BY-NC 3.0 IGO |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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dataset_info: |
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features: |
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- name: observation id |
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dtype: string |
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- name: segmentation |
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dtype: image |
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- name: bbox |
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dtype: image |
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- name: label |
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dtype: string |
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- name: area |
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dtype: string |
|
- name: image shape |
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dtype: string |
|
splits: |
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- name: train |
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num_bytes: 154137131.0 |
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num_examples: 272 |
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- name: valid |
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num_bytes: 210925170.0 |
|
num_examples: 360 |
|
download_size: 365017887 |
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dataset_size: 365062301.0 |
|
--- |
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|
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# XAMI (**X**MM-Newton Optical **A**rtefact **M**apping for Astronomical **I**nstance Segmentation) |
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The **Git** repository for this dataset can be found **[here](https://github.com/ESA-Datalabs/XAMI-dataset)**. |
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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). |
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# Annotation platforms |
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The images have been annotated using the following platform: |
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- [Zooniverse](https://www.zooniverse.org/projects/ori-j/ai-for-artefacts-in-sky-images), where the resulted annotations are not externally visible. |
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- [Roboflow](https://universe.roboflow.com/iuliaelisa/xmm_om_artefacts_512/), which allows for more interactive and visual annotation tools. |
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# The dataset format |
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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. |
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Please check [Dataset Structure](Datasets-Structure.md) for a more detailed structure of our dataset in COCO-IS and YOLOv8-Seg format. |
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# Downloading the dataset |
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- using a python script |
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```python |
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from huggingface_hub import hf_hub_download |
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dataset_name = 'xami_dataset' # the dataset name of Huggingface |
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images_dir = '.' # the output directory of the dataset images |
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hf_hub_download( |
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repo_id="iulia-elisa/XAMI-dataset", # the Huggingface repo ID |
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repo_type='dataset', |
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filename=dataset_name+'.zip', |
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local_dir=images_dir |
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); |
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# Unzip file |
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!unzip -q "xami_dataset.zip" -d 'path/to/dest' |
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``` |
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Or you can simply download only the dataset zip file from HuggingFace using a CLI command: |
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```bash |
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DEST_DIR='/path/to/local/dataset/dir' |
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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" |
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``` |
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## Licence |
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**[CC BY-NC 3.0 IGO](https://creativecommons.org/licenses/by-nc/3.0/igo/deed.en).** |
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