Instructions to use ProbeX/Model-J__ResNet__model_idx_0546 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0546 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0546") 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("ProbeX/Model-J__ResNet__model_idx_0546") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0546", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0546: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0546/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0546/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0546/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- d05f28f8af0565cd6ed508503de31784c53915c739df18571e68c12f558bcadc
- Size of remote file:
- 5.37 kB
- SHA256:
- 77e3a6329c559c4c7fe73dd2974b379f62ebc08ec57bbd8d543cb2420fa3ebc0
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