Instructions to use ProbeX/Model-J__ResNet__model_idx_0000 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_0000 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_0000") 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_0000") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79661d8a0819b3d08b4d0e02cb64a46caebc62d2f8f6501fc910a7c313535a0a
- Size of remote file:
- 5.37 kB
- SHA256:
- b980af5f096bc231188652814add24dd861bd9dcbf5da223e04bd232cef153be
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