Instructions to use ProbeX/Model-J__ResNet__model_idx_0916 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_0916 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_0916") 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_0916") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0916", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32437f4f34cbe1cb574ad6ff2058ddeef5e77a5c186c9e7842f4d76b222d58ec
- Size of remote file:
- 5.37 kB
- SHA256:
- bb52a6758786e37ac73b2a5f246e49f1302e408897bc2136f9e3a7432773211a
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