Instructions to use ProbeX/Model-J__ResNet__model_idx_0176 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_0176 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_0176") 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_0176") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0176", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57de47e30ab9c5f7d5e3660508f552c60ae49ccea78b7554f91f771545fb1c1e
- Size of remote file:
- 171 MB
- SHA256:
- 67e75cfbda5ba722880e6142017d2c46197df327d8d26abcb6b699709ab8d3bc
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