Instructions to use ProbeX/Model-J__ResNet__model_idx_0869 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_0869 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_0869") 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_0869") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0869") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 174434d4ec31ebcbd8957f218c393d43ab2ca8c66e6f1d4203214786047b845f
- Size of remote file:
- 5.37 kB
- SHA256:
- ab7074a86613dcee68701f58b3edaceda2f56a1eef38dea69d9994551903b900
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.