Instructions to use ProbeX/Model-J__ResNet__model_idx_0778 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_0778 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_0778") 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_0778") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0778", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3f5ac4fa6d1b7b02d489851a5cd4914bd4a154b7c6e8db0aa492adfd4794f3a
- Size of remote file:
- 171 MB
- SHA256:
- 437cc1a7c6d59b8dad485334dd035831704194e6ebc7b066748b2324e9c888c3
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