Instructions to use ProbeX/Model-J__ResNet__model_idx_0603 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_0603 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_0603") 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_0603") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0603") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac07fb2ac820348b33cde713cce2d27dba6c01d6230084b57945e334865a3824
- Size of remote file:
- 5.37 kB
- SHA256:
- 19348c5da33d82412ba360d179daf9f5a56101c65284c4cd94ae734f04065b5b
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