Instructions to use ramsformers/aeroplane with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramsformers/aeroplane with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ramsformers/aeroplane") 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("ramsformers/aeroplane") model = AutoModelForImageClassification.from_pretrained("ramsformers/aeroplane", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ramsformers/aeroplane: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ramsformers/aeroplane/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ramsformers/aeroplane/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ramsformers/aeroplane/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 0c5bb7469091fb62ca9237a2809ebc5a6f3c48bc3ef7ecd4ca9b8b6adf15371d
- Size of remote file:
- 343 MB
- SHA256:
- 81ef2babe60208d2791a21eef9f8b1e35a46d452cd9e938520c6781c8d118011
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.