Instructions to use ProbeX/Model-J__ResNet__model_idx_0960 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_0960 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_0960") 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_0960") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0960", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60c848122375af85d829b07f69e57fbd7d19633570c18343fe3088339c1a825e
- Size of remote file:
- 171 MB
- SHA256:
- ed89d5a4c9db88166ac4f10dcfd2e797ab9fc2d2ac468e3b113a44fe6b74aa6d
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