Instructions to use lamnt2008/car_brands_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamnt2008/car_brands_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lamnt2008/car_brands_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("lamnt2008/car_brands_classification") model = AutoModelForImageClassification.from_pretrained("lamnt2008/car_brands_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from lamnt2008/car_brands_classification: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/lamnt2008/car_brands_classification/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://lamnt2008/car_brands_classification@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/lamnt2008/car_brands_classification/resolve/refs%2Fpr%2F2/optimizer.pt
687 MB
- Xet hash:
- 0a6511c863eb37d76327156bf82df4aee7a0049803b0109ff031e99f475c3480
- Size of remote file:
- 687 MB
- SHA256:
- 842f7b64c94e5a21b91694b40873aa0ba156be103f668ed08400e6fd260f9b9f
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