Instructions to use ProbeX/Model-J__ResNet__model_idx_0797 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_0797 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_0797") 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_0797") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0797") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ff19b0b873153a02a8518a6fbde4f163a32626217224909eaecaa46c5a31d0c
- Size of remote file:
- 5.37 kB
- SHA256:
- a2435c304843204262fc424e507cc42911b56fdf39cb60e052bb4c2d2c49f499
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.