Instructions to use ProbeX/Model-J__ResNet__model_idx_0972 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_0972 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_0972") 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_0972") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0972", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 706fe076e97804c5fd90d2ea4464597bba6dc988e82aff4ec4ec5e81d8287fee
- Size of remote file:
- 171 MB
- SHA256:
- b5a9f4588c9c73793846b36554c8a032d87d97e588c76c592bacdc3e20e7ac80
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