Instructions to use Qifa/lada50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qifa/lada50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Qifa/lada50")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Qifa/lada50") model = AutoModelForObjectDetection.from_pretrained("Qifa/lada50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Qifa/lada50: direct link, hf CLI and curl.
- Browser
- Download file 26 MB
-
https://huggingface.co/Qifa/lada50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Qifa/lada50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Qifa/lada50/resolve/main/pytorch_model.bin
26 MB
- Xet hash:
- 5f9645187f058123d9aca0f207707652b11a53331a40e19bee191738fe140be2
- Size of remote file:
- 26 MB
- SHA256:
- c954d71766436997ceca1503b72e478434af052849c51c2a2aae3344c55f1201
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