Instructions to use ProbeX/Model-J__ResNet__model_idx_0579 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_0579 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_0579") 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_0579") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0579", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0419136e5490a120c98ba56322f120bfb2b8e951da0660c6a76315f532d9f13
- Size of remote file:
- 5.37 kB
- SHA256:
- b6afc2124ece8aa4cf3a24efc2cabd24637dbdbcf8ed329d70350e6b829a4bad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.