Instructions to use ProbeX/Model-J__ResNet__model_idx_0008 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_0008 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_0008") 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_0008") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0008") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0258436d6838673eba1297205f9c75b9e36e5828ee7b7b7ac53f76b87489afbb
- Size of remote file:
- 5.37 kB
- SHA256:
- 45c2b4d84861b598af88e5ad09903b07cd291cf31499ebbc1658c01c0650541a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.