Instructions to use ProbeX/Model-J__ResNet__model_idx_0263 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_0263 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_0263") 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_0263") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0263", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60a4ccaabba7dfcb87aa9b2ee0bfff1a4a7903151a9b324a2bf60b483c3302ac
- Size of remote file:
- 171 MB
- SHA256:
- f70435970faea602785da7bce0b9913317943fc3db0cba65dadc7ffcff8c0407
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