Update README.md

README.md

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----
-license: mit
----
+---
+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: validation
+    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: 66654394.0
+    num_examples: 105
+  - name: validation
+    num_bytes: 74471782.0
+    num_examples: 126
+  download_size: 141102679
+  dataset_size: 141126176.0
+---
+
+# XAMI-dataset
+
+
+*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).
+<!-- 
+The XMM-Newton Optical Monitor (XMM-OM) image catalog holds about 9 million detections of nearly 6 million distinct sources. It is crucial for analyzing individual objects and contributes significantly to survey science. However, source flagging in the XMM-OM data needs improvement due to artefacts that can cause false detections and affect the precision of real sources, thie being a common issue for other space missions. -->
+
+### Artefacts
+
+A particularity of our XAMI dataset compared to every-day images datasets are the locations where artefacts usually appear. 
+<img src="https://huggingface.co/datasets/iulia-elisa/XAMI-dataset/resolve/main/plots/artefact_distributions.png" alt="Examples of an image with multiple artefacts." />
+
+Here are some examples of common artefacts:
+
+<img src="https://huggingface.co/datasets/iulia-elisa/XAMI-dataset/resolve/main/plots/artefacts_examples.png" alt="Examples of common artefacts in the OM observations." width="400"/>
+
+# Annotation platforms
+
+The dataset images have been annotated using the following project:
+
+- [Zooniverse project](https://www.zooniverse.org/projects/ori-j/ai-for-artefacts-in-sky-images), where the resulted annotations are not externally visible. 
+- [Roboflow project](https://universe.roboflow.com/iuliaelisa/xmm_om_artefacts_512/), which allows for more interactive and visual annotation projects. 
+
+# The dataset format
+The XAMI dataset is splited into train and validation categories and contains annotated artefacts in COCO format for Instance Segmentation. We use multilabel Stratified K-fold technique (**k=4**) to balance class distributions across training and validation splits. We choose to work with a single dataset splits version (out of 4), but also provide means to train all 4 versions. 
+
+A more detailed structure of our dataset in COCO and YOLOformat can be found in [Dataset Structure](Datasets-Structure.md).
+
+# Downloading the dataset
+
+The dataset repository on can be found on [HuggingFace](https://huggingface.co/datasets/iulia-elisa/XAMI-dataset) and [Github](https://github.com/IuliaElisa/XAMI-dataset).
+
+### Downloading the dataset archive from HuggingFace:
+
+```python
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+dataset_name = 'dataset_archive' # the dataset name of Huggingface
+images_dir = '.' # the output directory of the dataset images
+annotations_path = os.path.join(images_dir, dataset_name, '_annotations.coco.json')
+
+for filename in [dataset_name, utils_filename]:
+  hf_hub_download(
+    repo_id="iulia-elisa/XAMI-dataset", # the Huggingface repo ID
+    repo_type='dataset', 
+    filename=filename, 
+    local_dir=images_dir
+  );
+
+# Unzip file
+!unzip "dataset_archive.zip"
+
+# Read the json annotations file
+with open(annotations_path) as f:
+    data_in = json.load(f)
+```
+or
+
+```
+- using a CLI command:
+```bash
+huggingface-cli download iulia-elisa/XAMI-dataset dataset_archive.zip --repo-type dataset --local-dir '/path/to/local/dataset/dir'
+```
+
+### Cloning the repository for more visualization tools
+
+<!-- The dataset can be generated to match our baseline (this is helpful for recreating dataset and model results).  -->
+
+Clone the repository locally:
+
+```bash
+# Github
+git clone https://github.com/IuliaElisa/XAMI-dataset.git
+cd XAMI-dataset
+```
+or 
+```bash
+# HuggingFace
+git clone https://huggingface.co/datasets/iulia-elisa/XAMI-dataset.git
+cd XAMI-dataset
+```
+
+# Dataset Split with SKF (Optional)
+
+- The below method allows for dataset splitting, using the pre-generated splits in CSV files. This step is useful when training multiple dataset splits versions to gain mor generalised view on metrics. 
+```python
+import utils
+
+# run multilabel SKF split with the standard k=4
+csv_files = ['mskf_0.csv', 'mskf_1.csv', 'mskf_2.csv', 'mskf_3.csv'] 
+
+for idx, csv_file in enumerate(csv_files):
+    mskf = pd.read_csv(csv_file)
+    utils.create_directories_and_copy_files(images_dir, data_in, mskf, idx)
+```
+
+## Licence 
+...