Instructions to use Promemoria/wool-classifier-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Promemoria/wool-classifier-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Promemoria/wool-classifier-finetuned") 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("Promemoria/wool-classifier-finetuned") model = AutoModelForImageClassification.from_pretrained("Promemoria/wool-classifier-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Promemoria/wool-classifier-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/Promemoria/wool-classifier-finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://Promemoria/wool-classifier-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Promemoria/wool-classifier-finetuned/resolve/main/training_args.bin
5.71 kB
- Xet hash:
- e3045bb935e96ba4b6978c6f40b40f2b9bc21aa3b7dd68e7311a6a3342aad3c5
- Size of remote file:
- 5.71 kB
- SHA256:
- 3cce01b44f9423c878a2978b36c06702fdbb07aebe1c0c393103497f91449052
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.