Instructions to use ProbeX/Model-J__ResNet__model_idx_0362 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_0362 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_0362") 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_0362") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0362", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef879e776ae0b31a24082286b42bd436165e567462d678b7c60e3b1ef9ff3ab1
- Size of remote file:
- 171 MB
- SHA256:
- 21764a9cc5ef2a120066d93d1cbfbfe6c3795f76383033df6c3023a1eef07d64
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