Instructions to use ProbeX/Model-J__ResNet__model_idx_0729 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_0729 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_0729") 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_0729") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0729", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b44870b51beedc442e7420e9a2800ce056f9a9f549b3c380d9b95444cf6f419f
- Size of remote file:
- 5.37 kB
- SHA256:
- 85a56dd05fd778975586c05467c07c1e1c09e5805bf5aede5a5bfbff1eee0288
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