Instructions to use ProbeX/Model-J__ResNet__model_idx_0695 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_0695 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_0695") 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_0695") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0695") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c180cf797d9c5a37abaa65a4fe2d04284a4b93679fa62041802caaecea9e71df
- Size of remote file:
- 5.37 kB
- SHA256:
- f51540bceb9685ae098850cb1967126f9e0a4569904f9fbda18aa2c54e745988
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.