Instructions to use ProbeX/Model-J__ResNet__model_idx_0141 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_0141 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_0141") 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_0141") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0141", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6c7ba0ed1014f9bcc820dffad390dede7436a77c961e61ce66f43ad5821027e
- Size of remote file:
- 171 MB
- SHA256:
- c7102b56c94c9fbfdb22b5ee8225e9b6cbb659cd1633e4411f0cf3b3232e11df
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