Instructions to use dl1wjd2/Logistics_AX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dl1wjd2/Logistics_AX with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("dl1wjd2/Logistics_AX", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 586 Bytes
bbae060 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | __all__ = ["popCount", "bit_count", "bit_indices"]
try:
bit_count = int.bit_count
except AttributeError:
def bit_count(v):
return bin(v).count("1")
"""Return number of 1 bits (population count) of the absolute value of an integer.
See https://docs.python.org/3.10/library/stdtypes.html#int.bit_count
"""
popCount = bit_count # alias
def bit_indices(v):
"""Return list of indices where bits are set, 0 being the index of the least significant bit.
>>> bit_indices(0b101)
[0, 2]
"""
return [i for i, b in enumerate(bin(v)[::-1]) if b == "1"]
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