Instructions to use ProbeX/Model-J__ResNet__model_idx_0045 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_0045 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_0045") 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_0045") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0045", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e8d948fdca5a9a8367cc98f806d072184be76505f74542e9633df6dfb0e558
- Size of remote file:
- 171 MB
- SHA256:
- d878bec74ea5fab91ebb3c1feae239f76d2dd8eaa02a0f0fddabe68e96c67765
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