Instructions to use ProbeX/Model-J__ResNet__model_idx_0653 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_0653 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_0653") 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_0653") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0653", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77a31c3084e9b891352f84b70c3f655d3a733b34ea10922b68219f5dbc13df7d
- Size of remote file:
- 5.37 kB
- SHA256:
- ba70daadc2cb468f1175dc22f1aec8335e8b9c087c2e4b10175a17c80a6e9869
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