Instructions to use ProbeX/Model-J__ResNet__model_idx_0788 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_0788 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_0788") 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_0788") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0788", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c639f0777469905d88495fd838274debad5d073db11594de66c55800064651c2
- Size of remote file:
- 5.37 kB
- SHA256:
- e4f907c29c26b688c2a9887b60697d6932148abb72205b6e2d9e60ff6a48863b
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