Instructions to use kraftman1/image_classification_ConvNext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kraftman1/image_classification_ConvNext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kraftman1/image_classification_ConvNext") 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("kraftman1/image_classification_ConvNext") model = AutoModelForImageClassification.from_pretrained("kraftman1/image_classification_ConvNext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kraftman1/image_classification_ConvNext: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/kraftman1/image_classification_ConvNext/resolve/main/training_args.bin
- Command line
-
hf download hf://kraftman1/image_classification_ConvNext/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kraftman1/image_classification_ConvNext/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- c5c072c5c41608ba9ebd8e8bfb4d039522940237673634ff087948afdf618bc0
- Size of remote file:
- 3.64 kB
- SHA256:
- 394e229fcdb3ef1d47e2770f3e3e146d1394b50bdb03822fbec9400e9ca0fb26
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