Instructions to use ProbeX/Model-J__ResNet__model_idx_0296 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_0296 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_0296") 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_0296") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0296", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75b87c6b19bd01b0bcff2f50571f6b5f78321f2fc7c85dc9ce69ae2856c25d5a
- Size of remote file:
- 171 MB
- SHA256:
- 5f4d9d174124e12b75f9b2b0c573211774c7c457f6e8b554767a9471405d0b4c
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