Instructions to use ProbeX/Model-J__ResNet__model_idx_0690 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_0690 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_0690") 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_0690") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0690", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64f51eb295daca55797a34c3a75629fc08447778067fc6bf46125fe4bc9b3adf
- Size of remote file:
- 5.37 kB
- SHA256:
- e77cc1af0218863a67ff4ce508742293da21e382e2d688dc98569a6cc5e0a688
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