Instructions to use ProbeX/Model-J__ResNet__model_idx_0261 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_0261 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_0261") 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_0261") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0261", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04ec06ac5a6f63d5fee702ee1af687b6800969ce3cd7002cf909135406b7f080
- Size of remote file:
- 171 MB
- SHA256:
- 02250abc23d71654f444fb3cc8848794aaacd872fc6c652e20f9f422cd34e04d
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