Instructions to use ProbeX/Model-J__ResNet__model_idx_0767 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_0767 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_0767") 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_0767") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0767") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e57387014178896c2aa3ae720d00cc0b0d97e75cc7d3e9e0b22de6df39fc42e1
- Size of remote file:
- 5.37 kB
- SHA256:
- 2cbfa028e6a71e6e076ff2e0f4bc5506110679a67ff34183c6afe5b3fd51c36a
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