Instructions to use ProbeX/Model-J__ResNet__model_idx_0947 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_0947 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_0947") 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_0947") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0947") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b5207e6a33792bddef91307edb1e1b5a8ed505c88c69af3f1d702552b80f4ed
- Size of remote file:
- 5.37 kB
- SHA256:
- a17888856d22bac45aecea38ba56cb3331470927f353f8e6add7399f75b7e1ba
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