Instructions to use kevanme/practica_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kevanme/practica_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="kevanme/practica_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("kevanme/practica_2") model = AutoModelForObjectDetection.from_pretrained("kevanme/practica_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kevanme/practica_2: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/kevanme/practica_2/resolve/main/training_args.bin
- Command line
-
hf download hf://kevanme/practica_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kevanme/practica_2/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- ceca823134f990c8652d40d64c28d6e8c8738c1eaefffd511dd8f0fde98f569f
- Size of remote file:
- 5.24 kB
- SHA256:
- 757061504884039220147c573b780109102a55bd1143f004606f5ea8bedf88d0
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