Instructions to use ProbeX/Model-J__ResNet__model_idx_0954 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_0954 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_0954") 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_0954") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0954") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c69fcedb5a4fcec501a3c9588371572042c99263287120927299ab62d57a1103
- Size of remote file:
- 5.37 kB
- SHA256:
- 99d409b28bd5955f8aa8d3701ff7c48279200df484b0a3f90fea78b0394a6bcf
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