Instructions to use ProbeX/Model-J__ResNet__model_idx_0992 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_0992 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_0992") 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_0992") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0992", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 241a14062d7fbeb4cf698f064c40372825f87573b854a4d0f59af1fa4d126117
- Size of remote file:
- 5.37 kB
- SHA256:
- 323ef5a0eef58ac93aa53db0758905cf63d7b28731ee94fa29e8a7950f97b17a
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