Instructions to use ProbeX/Model-J__ResNet__model_idx_0989 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_0989 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_0989") 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_0989") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0989", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 358275c5f7ded359e2a00c44307a804c5f9182339623c6add94b7e8cfeaf7209
- Size of remote file:
- 171 MB
- SHA256:
- 0e71f2a8504d0254e63f72d6816f19d2c58ea592f0ffa2fca3fb0e5864fb840d
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