Instructions to use ArrayDice/Vehicle_Detection_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArrayDice/Vehicle_Detection_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="ArrayDice/Vehicle_Detection_Model")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("ArrayDice/Vehicle_Detection_Model") model = AutoModelForObjectDetection.from_pretrained("ArrayDice/Vehicle_Detection_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ArrayDice/Vehicle_Detection_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/ArrayDice/Vehicle_Detection_Model/resolve/0b83efbc818d572b4fe77f21b7772b60a4179282/training_args.bin
- Command line
-
hf download hf://ArrayDice/Vehicle_Detection_Model@0b83efbc818d572b4fe77f21b7772b60a4179282/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ArrayDice/Vehicle_Detection_Model/resolve/0b83efbc818d572b4fe77f21b7772b60a4179282/training_args.bin
5.11 kB
- Xet hash:
- cb8c30efa7aca199a196296780f1092f596fd722132f629bd41e6df311b67485
- Size of remote file:
- 5.11 kB
- SHA256:
- 1df1998ea04ca70a0a038ae00e48dd2555c686bd8a92a52b678498a861c91e0a
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