Instructions to use ProbeX/Model-J__ResNet__model_idx_0105 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_0105 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_0105") 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_0105") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0105") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cb00d959966af9ff54f31e5100eaa48f7968c7f7722e81bb91423627d7d5add
- Size of remote file:
- 5.37 kB
- SHA256:
- 6b87b3437449d633bbdb4fb4880e7a6096f9cfa3b5135f046f182d9afcf14788
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