Instructions to use ProbeX/Model-J__ResNet__model_idx_0489 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_0489 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_0489") 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_0489") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0489", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47fd8088fdff815ecea68ceb46e0d55463f8aaf38bc0eabe350dc43232bf8373
- Size of remote file:
- 171 MB
- SHA256:
- 9ee023de82b58539dff9ea7d8f5a16c5ee820ee7a947ffd9734b15a25cb19d1f
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