Instructions to use ProbeX/Model-J__ResNet__model_idx_0946 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_0946 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_0946") 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_0946") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0946", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cd1fb3da299822ff924e8867cd7635611647cf3ed73382182f7d21f5596638f
- Size of remote file:
- 171 MB
- SHA256:
- d95b50c8074249c97674adaf5746bafcb5051d7091ea59457d518d5f42e64fc0
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