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--- |
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license: mit |
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language: |
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- vi |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Structure |
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Each sample will have a structure as follows: |
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``` |
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{ |
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'id': Value(dtype='string', id=None), |
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'images': Value(dtype='binary', id=None), |
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'conversations': [{'from': Value(dtype='string', id=None), 'value': Value(dtype='string', id=None)}] |
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} |
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{ |
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'id': '004309348', |
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'image': <image-bytes>, |
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'conversations': [{'from': 'human', 'value': 'Điều gì được minh họa trong hình ảnh này?\n<image>'}, {'from': 'gpt', 'value': 'bạn nghĩ có bao nhiêu sinh viên ở farbaut sử dụng sản phẩm thuốc lá'}] |
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} |
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``` |
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# How To Use |
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## Convert binary objects |
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Because the returned video will be in bytes, here is a way to extract frames and fps: |
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```python |
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import io |
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import numpy as np |
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from PIL import Image |
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from datasets import load_dataset |
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def extract_image(image_bytes): |
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img = Image.open(io.BytesIO(image_bytes)) |
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arr = np.asarray(img) |
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return arr |
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dataset = load_dataset("Vividbot/instruct500k_vi", name="all", streaming=True) |
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image_bytes = next(iter(dataset["train"]))["image"] |
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image = extract_image(image_bytes) |
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print(f"Image shape: {image.shape}") |
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``` |
