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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d63b075762122fbbbe70e009c9b13e04f50b1d67d2f3ce61f5a41c7c4489150
- Size of remote file:
- 171 MB
- SHA256:
- daa00b310f0b762a2eafb146d29c2288084456db27f4549ebd842a88823d5913
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