Instructions to use ProbeX/Model-J__ResNet__model_idx_0771 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_0771 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_0771") 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_0771") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0771", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 856a5e311c1fed1a3b3be39cc137208b2e8f771f46592faa3903fee5ae2ade0b
- Size of remote file:
- 5.37 kB
- SHA256:
- b1e77f9a32753472c7be2844176595ed93863cf2d8c702a1f776a3e6da6c8310
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.