Instructions to use ProbeX/Model-J__ResNet__model_idx_0516 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_0516 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_0516") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0516") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0516", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0516: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0516/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0516/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0516/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 9f7dbd3d371703cd8b6f9b04b6e6a26a945afc4e8f7eacdac74d124efa49c36d
- Size of remote file:
- 5.37 kB
- SHA256:
- 84c64c61e7430dca76c31e8ea286cd6a0710c10a0363ce6a85f900505dadaaca
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