Instructions to use ProbeX/Model-J__ResNet__model_idx_0392 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_0392 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_0392") 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_0392") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0392") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 822f88ac710aeb605962062179041a2c3f5fb453b8f510c546c3f8298f0a5389
- Size of remote file:
- 5.37 kB
- SHA256:
- 812a9791c48f185d6ca55d9502c7f8ff26191cd9cbd4a422e7eb5d1b72c82ce4
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