Instructions to use ProbeX/Model-J__ResNet__model_idx_0354 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_0354 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_0354") 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_0354") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0354") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d4920ab93f41fb9ebffd19d201405effe29c4ffa61839f5289dce65ee88fd93
- Size of remote file:
- 5.37 kB
- SHA256:
- 9753a7d32a8b8088fc9fc6894519ec458485a36fc83de8d85dc19cf5d89f26ce
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