Instructions to use ProbeX/Model-J__ResNet__model_idx_0843 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_0843 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_0843") 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_0843") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0843", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 099f8d8f5ff7e8895c544f1375fff924bf5f98e7e456b6f6e71f8ae237f3ae5c
- Size of remote file:
- 171 MB
- SHA256:
- 4be73ec3d212fe3037c1acfc2535f276a02d8df995eeef8685bc27d874e5144c
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