Instructions to use ProbeX/Model-J__ResNet__model_idx_0880 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_0880 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_0880") 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_0880") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0880", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f39d20b8ea7ab6ee4160d2e1c05b9482bc5e10320ab7dd63a63f275db7096d1
- Size of remote file:
- 5.37 kB
- SHA256:
- 045e185812a61c20753921ed3f6d4a1a1f4be8deb2f1db275ced175c4f3b9c45
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