Instructions to use ProbeX/Model-J__ResNet__model_idx_0156 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_0156 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_0156") 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_0156") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0156") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9bf460b9cd604a03048f43bf12aa8be49aae9697ce4d64f7b01e05144c94ce6
- Size of remote file:
- 5.37 kB
- SHA256:
- a3e5145750e14703bbc949b3cebcf5389b107ba667ce95e891948155fab06b5d
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