Update README

README.md

@@ -54,7 +54,7 @@ The XAMI dataset contains 1000 annotated images of observations from diverse sky
 
 ### Artefacts
 
-A particularity of our XAMI dataset compared to every-day images datasets are the locations where artefacts usually appear. 
+A particularity of the dataset compared to every-day images 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 in the dataset:
@@ -63,71 +63,65 @@ Here are some examples of common artefacts in the dataset:
 
 # Annotation platforms
 
-The images have been annotated using the following projects:
+The images have been annotated using the following platform:
 
-- [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. 
+- [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 technique (**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. 
+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 and YOLOv8-Seg format.
+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
 
-The dataset repository can be found on [HuggingFace](https://huggingface.co/datasets/iulia-elisa/XAMI-dataset) and [Github](https://github.com/IuliaElisa/XAMI-dataset).
+### *(Option 1)* Downloading the dataset **archive** from HuggingFace
 
-### Downloading the dataset archive from HuggingFace:
+- using a python script:
 
 ```python
-from huggingface_hub import hf_hub_download
+import os
+import json
 import pandas as pd
+from huggingface_hub import hf_hub_download
 
-dataset_name = 'dataset_archive' # the dataset name of Huggingface
+dataset_name = 'xami_dataset' # 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(
+hf_hub_download(
     repo_id="iulia-elisa/XAMI-dataset", # the Huggingface repo ID
     repo_type='dataset', 
-    filename=filename, 
+    filename=dataset_name+'.zip', 
     local_dir=images_dir
-  );
+);
 
 # Unzip file
-!unzip "dataset_archive.zip"
+!unzip -q "xami_dataset.zip" 
+
+# Read the train json annotations file
+annotations_path = os.path.join(images_dir, dataset_name, 'train/', '_annotations.coco.json')
 
-# Read the json annotations file
 with open(annotations_path) as f:
     data_in = json.load(f)
+
+data_in['images'][0]
 ```
 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
+huggingface-cli download iulia-elisa/XAMI-dataset xami_dataset.zip --repo-type dataset --local-dir '/path/to/local/dataset/dir'
 
-<!-- The dataset can be generated to match our baseline (this is helpful for recreating dataset and model results).  -->
+```
 
-Clone the repository locally:
+### *(Option 2)* Cloning the repository for more visualization tools
 
 ```bash
 # Github
 git clone https://github.com/ESA-Datalabs/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. 
@@ -140,7 +134,7 @@ 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 
 ...