Instructions to use Qifa/ladabaru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qifa/ladabaru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Qifa/ladabaru")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Qifa/ladabaru") model = AutoModelForObjectDetection.from_pretrained("Qifa/ladabaru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Qifa/ladabaru: direct link, hf CLI and curl.
- Browser
- Download file 26 MB
-
https://huggingface.co/Qifa/ladabaru/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Qifa/ladabaru/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Qifa/ladabaru/resolve/main/pytorch_model.bin
26 MB
- Xet hash:
- 63111bcb38922cfacf8282733e27af7b14a160c0e30fbcdacbaa603e56ed4a97
- Size of remote file:
- 26 MB
- SHA256:
- 309ee71d2fd8bf0cb002104e00bb749f9b0da92ea22522459a5876a6cea05228
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