Instructions to use ProbeX/Model-J__ResNet__model_idx_0773 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_0773 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_0773") 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_0773") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0773", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fda47a86708cd63747172e8abd5c05c75667be797f7719aa6044aa1665ebf790
- Size of remote file:
- 5.37 kB
- SHA256:
- 1d7d6a41bd43f68c2c2f1b73412b4f522306ab8a12158a1c41fb5f0c4e1eed1d
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