Instructions to use ProbeX/Model-J__ResNet__model_idx_0050 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_0050 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_0050") 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_0050") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0050") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e81b3517db0dd203ea1c00a1bf42aaefbd1696e2f03606ef92dad30de4e9c01
- Size of remote file:
- 5.37 kB
- SHA256:
- c581a4273b0cc619061fa467c3d345e3c912800a48abe994c23ff08448d071b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.