Instructions to use ArrayDice/car_orientation_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArrayDice/car_orientation_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ArrayDice/car_orientation_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ArrayDice/car_orientation_classification") model = AutoModelForImageClassification.from_pretrained("ArrayDice/car_orientation_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ArrayDice/car_orientation_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ArrayDice/car_orientation_classification/resolve/main/model.safetensors
- Command line
-
hf download hf://ArrayDice/car_orientation_classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ArrayDice/car_orientation_classification/resolve/main/model.safetensors
343 MB
- Xet hash:
- a44f4717fa9ddc905c33c63bd4d910f2cb9f93d57ab52a8da5eef8cf81dba447
- Size of remote file:
- 343 MB
- SHA256:
- a11a7cc19d243e779f0090ae83e2a2980772575f482e0f6d842acc9cca6fd636
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.