Instructions to use ProbeX/Model-J__ResNet__model_idx_0585 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_0585 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_0585") 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_0585") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0585", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41838d78d0faf822f666ba70c6fa64edbd7b35c79a3268eec7285cb58d3388be
- Size of remote file:
- 5.37 kB
- SHA256:
- fb64c78b742e1f5f81531a303e6c9e5169fb97c6f45a1a3af6f5e167d5f52897
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