Datasets:
Iulia Elisa
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updated huggingface readme
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README.md
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@@ -67,51 +67,36 @@ Please check [Dataset Structure](Datasets-Structure.md) for a more detailed stru
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# Downloading the dataset
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###
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import pandas as pd
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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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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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!unzip -q "xami_dataset.zip"
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annotations_path = os.path.join(images_dir, dataset_name, 'train/', '_annotations.coco.json')
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```
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or
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- using a CLI command
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```bash
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```
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```bash
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# Github
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git clone https://github.com/ESA-Datalabs/XAMI-dataset.git
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cd XAMI-dataset
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```
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<!--
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# Dataset Split with SKF (Optional)
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# Downloading the dataset
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### Clone the repository
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```bash
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git clone https://github.com/ESA-Datalabs/XAMI-dataset.git
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cd XAMI-dataset
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conda env create -f requirements.yaml # create an environment with the package requirements
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```
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Then
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### Download the dataset archive from HuggingFace
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- using a python script (see [load_and_visualise_dataset.pynb](https://github.com/ESA-Datalabs/XAMI-dataset/blob/main/load_and_visualise_dataset.ipynb))
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```python
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from xami_dataset import XAMIDataset
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# Download the dataset
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xami_dataset = XAMIDataset(
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repo_id="iulia-elisa/XAMI-dataset",
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dataset_name="xami_dataset",
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data_path='./dest_dir')
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```
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- 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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<!--
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# Dataset Split with SKF (Optional)
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