Instructions to use ProbeX/Model-J__ResNet__model_idx_0700 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_0700 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_0700") 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_0700") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0700", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e7138b403b46749890cd16ab65d91a65e4bf2074567ae8c466541bc9b958a8c
- Size of remote file:
- 5.37 kB
- SHA256:
- 2125be886f1918ed27a05a2266077bcc5e18e392953422228091951c6e94a2b1
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