Instructions to use ProbeX/Model-J__ResNet__model_idx_0068 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_0068 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_0068") 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_0068") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0068", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0068: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0068/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0068/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0068/resolve/main/model.safetensors
171 MB
- Xet hash:
- 6e32f0278ca0ea417b7431441bc64740613ad41d08e35b415e4b3ba0381a7f12
- Size of remote file:
- 171 MB
- SHA256:
- 765a2d3f3673391f44de3ca49df6ad359356f7c72a19553c3992615a2f46d2ca
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