Instructions to use ProbeX/Model-J__ResNet__model_idx_0949 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_0949 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_0949") 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_0949") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0949", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4307ee42a794b88020771e475e23566bef469b1905b9e4f060563294b06e4cc
- Size of remote file:
- 171 MB
- SHA256:
- be3e806a471615c1631b5a8bc37723baa44b7097a778d2f8418ec19b3b677b0e
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