Instructions to use ProbeX/Model-J__ResNet__model_idx_0929 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_0929 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_0929") 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_0929") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0929", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22ebe997d4c16d019b1eb4b7a071c62951fdb771fef2d8591d91e39ac2e323f8
- Size of remote file:
- 171 MB
- SHA256:
- 8377846ebf22817d340073043b305992e562b94ff1048bc961fb447159cf3da6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.