Instructions to use ProbeX/Model-J__ResNet__model_idx_0967 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_0967 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_0967") 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_0967") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0967", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b62cad83eeb34035999d72cd3adbe079938d9644b381238d12358bcb01277ae
- Size of remote file:
- 171 MB
- SHA256:
- edcdb966b8971baed3ed2264afdbd51b8327a358d3d01b34927548b8bd6a9cd9
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