Instructions to use ProbeX/Model-J__ResNet__model_idx_0973 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_0973 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_0973") 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_0973") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0973", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cba6e505aff5e9817265fc2e74e270b5b0bfabb2712e4364ec8d4c641ac8c1ff
- Size of remote file:
- 171 MB
- SHA256:
- 5f8c7484c8786b67309d00e3c742cd660aedc01ed8b6b0704d5e870840dfc8e1
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