Instructions to use ProbeX/Model-J__ResNet__model_idx_0491 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_0491 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_0491") 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_0491") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0491", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ProbeX/Model-J__ResNet__model_idx_0491: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0491/resolve/main/training_args.bin
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0491/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0491/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 9c1074ff353cebabe3e629b0e4e9f67eb9ccb1c406c6c6d3ce3b573bbfad9cac
- Size of remote file:
- 5.37 kB
- SHA256:
- 5f3659f14c0236b7fbb5932b00560f3570b1ecfc478c92e15f7789892738a7db
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