Instructions to use ProbeX/Model-J__ResNet__model_idx_0583 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_0583 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_0583") 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_0583") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0583", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93fd57057ce8c920ae0c9de918b771da589c3239e5767787ff169ea2cc4553f1
- Size of remote file:
- 171 MB
- SHA256:
- 803d21ef109e691d6baa7f05e366d0f754b4dace8f82cdf7e93a86a5c4213014
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