Instructions to use ProbeX/Model-J__ResNet__model_idx_0628 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_0628 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_0628") 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_0628") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0628") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df268660503e447b4159b55639e3c9adc6f28aad3fd85bbd2dbe15a68eafd5cf
- Size of remote file:
- 171 MB
- SHA256:
- fb216b707e36c445b5e39d80f9c94a01a6d31388a660ab4936427c70f43d9f5d
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