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--- |
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license: cc-by-nc-4.0 |
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dataset_info: |
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features: |
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- name: image |
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dtype: string |
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- name: label |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 17286021131 |
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num_examples: 405055 |
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download_size: 17266005314 |
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dataset_size: 17286021131 |
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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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--- |
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### Install datasets package |
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First, make sure you have the datasets library installed. If not, you can install it using: |
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```bash |
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pip install datasets |
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``` |
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### Load Dataset from Arrow File |
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Download all arrow files to local_path. The follow is how to load arrow files and decode image: |
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```python |
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from datasets import load_from_disk |
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from io import BytesIO |
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import base64 |
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from PIL import Image |
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import mmengine |
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# Path to your Arrow dataset directory |
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arrow_dataset_path = 'path_to_your_arrow_dataset_directory' |
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# Load the dataset |
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dataset = load_from_disk(arrow_dataset_path) |
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cat_tree = mmengine.load('v3det_2023_v1_category_tree.json') |
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# Each dataset entry is composed of an image in the format of base64 string and its corresponding imagenet label id |
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# Here is an example of how to decode image, and convert imagenet label id to v3det class name |
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# You can download v3det_2023_v1_category_tree.json here: https://v3det.openxlab.org.cn/download |
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image = Image.open(BytesIO(base64.b64decode(dataset[0]['image']))) |
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cat_name = cat_tree['id2name'][dataset[0]['label']] |
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``` |
