Instructions to use ProbeX/Model-J__ResNet__model_idx_0347 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_0347 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_0347") 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_0347") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0347", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 115a55cdb8f46bfed8de471a4196f7dbaf790c7703251ba92f01557816d66f78
- Size of remote file:
- 171 MB
- SHA256:
- a7897096d5a07399fadedcc29c604e0af0c9067fe125432c2210250b17436e8d
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