Instructions to use ProbeX/Model-J__ResNet__model_idx_0249 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_0249 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_0249") 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_0249") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0249", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b58b905f6a1a1d442afb3c242b816d27a13e61282c5e225a732b672164cbaa84
- Size of remote file:
- 5.37 kB
- SHA256:
- 19e121be9fe1bf079991cda95aa8f4d1366b56b16f032da0d54d3c42f07f1014
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