Instructions to use ProbeX/Model-J__ResNet__model_idx_0340 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_0340 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_0340") 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_0340") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0340", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c818b4ccc6483ed7330910a7d5b41b9fd5a05424a0332ef55190885f1971c909
- Size of remote file:
- 171 MB
- SHA256:
- 7175c565abb205498550d67d98d786f1e43ce53bcfbe2c7a6f8c4ce88599ad72
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