Instructions to use ProbeX/Model-J__ResNet__model_idx_0049 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_0049 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_0049") 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_0049") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0049") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 041454ab451dbb4b4d7db520d7c410050165954cfd50a7b159e81fa093bb5509
- Size of remote file:
- 5.37 kB
- SHA256:
- 1f233650da0457ae5fa5d78175b9af43e3768e37317738a38cf0aa5d3575c453
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.