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| 🇰🇭 For the development of AI 🇰🇭 |
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| <h2>Download the annotations as jsonl file</h2> |
| <h3>The annotations are in Pascal VOC format</h3> |
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| <h5>You can use <code>datasets</code> package of huggingface to load the downloaded dataset. See demo code below.</h5> |
| <div class="python-code-block"> |
| <pre><code class="language-python"> |
| from datasets import load_dataset |
| your_data = load_dataset('imagefolder', data_dir='annotated_data') # this will use metadata.jsonl in annotated_data to create your_data |
| print(your_data) |
| # optional: you can easily plot one instance of your_data in jupyter notebook |
| import torch |
| from torchvision.utils import draw_bounding_boxes |
| from torchvision.transforms.functional import pil_to_tensor, to_pil_image |
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| example = your_data['train'][0] |
| boxes_xyxy = torch.tensor(example['objects']['bbox']) |
| labels = [x for x in example['objects']['names']] |
| to_pil_image( |
| draw_bounding_boxes( |
| pil_to_tensor(example['image'].convert('RGB')), |
| boxes_xyxy, |
| colors="red", |
| labels=labels, |
| ) |
| ) |
| </code></pre> |
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| link.href = "{{ url_for('download', download='annotations_pascal_voc.csv') }}"; |
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