Instructions to use ProbeX/Model-J__ResNet__model_idx_0132 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_0132 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_0132") 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_0132") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0132", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48bf425ae9f15e1424d8acad78dbaaa3dbfc733ca6759a94bc1001dcdeafaa3d
- Size of remote file:
- 5.37 kB
- SHA256:
- a1a40ff7824471c6aefd878592576377a3cf8ec06fb97f766512d0e807f3f0f5
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