Instructions to use ProbeX/Model-J__ResNet__model_idx_0428 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_0428 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_0428") 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_0428") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0428", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d4ce4983d3000c9327575d7be9b970872f23e312e65f742835cd3fdca79d7f9
- Size of remote file:
- 171 MB
- SHA256:
- 30f3f43c8bb320ec5c8f78e7a5d445a4ecce0ffe5641c972e2f807cfd81850a8
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