Instructions to use Qifa/lada100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qifa/lada100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Qifa/lada100")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Qifa/lada100") model = AutoModelForObjectDetection.from_pretrained("Qifa/lada100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Qifa/lada100: direct link, hf CLI and curl.
- Browser
- Download file 26 MB
-
https://huggingface.co/Qifa/lada100/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Qifa/lada100/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Qifa/lada100/resolve/main/pytorch_model.bin
26 MB
- Xet hash:
- 4f2e3ec2af18adcf6c630eef43bb8a49719c0c158a2ba19e9de0851dc9a65123
- Size of remote file:
- 26 MB
- SHA256:
- 2a619d755395458ce80ec2f4f8c2fa0d7f3ad50dadb943c49907733b91ca5ac8
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