Instructions to use ProbeX/Model-J__ResNet__model_idx_0800 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_0800 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_0800") 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_0800") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0800", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 041aca6b5e8d7444cdc8fddb4b1bac2a9e3d6a2e754694ed6d8acd0cc3bc4c7c
- Size of remote file:
- 171 MB
- SHA256:
- f5c19e792f5336abcac35f3233d4b9e0487f0e37a804a20368b627749c300509
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