Instructions to use ProbeX/Model-J__ResNet__model_idx_0672 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_0672 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_0672") 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_0672") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0672", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70e3f26092791054fb00c98606e2d3790f842c4ca5771d18266c7386040a8b2b
- Size of remote file:
- 171 MB
- SHA256:
- 7b50b2d7862d5150b7f8087321971b8ab02fe361b8f93ee1d846a5db8b1dcf0f
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