Instructions to use ProbeX/Model-J__ResNet__model_idx_0306 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_0306 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_0306") 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_0306") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0306", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3bec86df745ed533127495d826e63a36fff13ac75443101c9ba04a4788a58e
- Size of remote file:
- 5.37 kB
- SHA256:
- fe4df004acbf72ceaccc8a4a67e6fac090af4f3a91a4770ddcbb7242828a98a3
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