Instructions to use ProbeX/Model-J__ResNet__model_idx_0865 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_0865 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_0865") 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_0865") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0865", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd234b38dd420fc6aecf5371300015ffe8ef0266122b3cc5d4930ff602a3a772
- Size of remote file:
- 5.37 kB
- SHA256:
- 14530080e49f4800f9d617c5ab094f7daaa02c399826928c11442a52d7d7f4ba
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