Instructions to use ProbeX/Model-J__ResNet__model_idx_0678 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_0678 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_0678") 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_0678") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0678", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd93b351904622a2b1ade8cd8cd4bd090b690663324a9bffc2d49660050f99e6
- Size of remote file:
- 5.37 kB
- SHA256:
- d99c05f59c71c7c94d26281fce6486bfcdfed77e6ca5b7ecddc08db75d72bf32
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