Instructions to use ProbeX/Model-J__ResNet__model_idx_0226 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_0226 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_0226") 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_0226") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0226") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9da32155c1f18f192211c189ed0e167c400a46fb709cbcd26bbaceabb21490a1
- Size of remote file:
- 5.37 kB
- SHA256:
- fef7878ae89c5e168b0cdb78e5bd028edaf19ca12c37d986cc321cfde3e17866
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