Instructions to use ProbeX/Model-J__ResNet__model_idx_0369 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_0369 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_0369") 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_0369") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0369") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e433a15984ecfaecfbce23825af97d976e441ef09e515a89be0ca023c80f49b
- Size of remote file:
- 5.37 kB
- SHA256:
- 3ad3609bb0c74f4e200b562408c607b819dd8093beda683c750b259ff6e09bc0
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