Instructions to use ProbeX/Model-J__ResNet__model_idx_0136 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_0136 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_0136") 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_0136") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0136") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5acdee585bfd13899ea4f71a638fe7d9fbc6d42604885026dfb3d4c158ec45e1
- Size of remote file:
- 5.37 kB
- SHA256:
- e89964d6d21b7e783cffcb2a8893da7ec91ecef8fd62c1a68872e12bfbc989e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.