Instructions to use ProbeX/Model-J__ResNet__model_idx_0931 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_0931 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_0931") 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_0931") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0931", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec3d4601a4be89af9c995f624b1505217a3b06c1e2813c5768d7e96d944d720b
- Size of remote file:
- 5.37 kB
- SHA256:
- 980fba9b94e7283fe14ae67dbb27bacf62f3054ca3656ef96c1b62b1aee2b298
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