Instructions to use ProbeX/Model-J__ResNet__model_idx_0736 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_0736 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_0736") 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_0736") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0736", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f144f69dcfe1efeb788ea20efa6bdb640f851ecd4c39e5b0d01e0cb8f6be3e69
- Size of remote file:
- 5.37 kB
- SHA256:
- e7dd986a650cb34695efdd02a99f5cccc45ba414af2893739d7b9e18d26adf5c
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