Instructions to use ProbeX/Model-J__ResNet__model_idx_0628 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_0628 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_0628") 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_0628") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0628", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e87be731e81c7d42e667379fc2fc0474a4d76307a8d1e66d8c42c316d619915
- Size of remote file:
- 5.37 kB
- SHA256:
- e2a9bfee90ffbc020bc7e1c508f53267ae5c3fba9c9cc45f65c6a999bb967616
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