Instructions to use ProbeX/Model-J__ResNet__model_idx_0263 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_0263 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_0263") 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_0263") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0263", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1374e500c57a1fbbeb6b424eae5cc2a4a4bf7bfed37f2fca893c7cb18341c95f
- Size of remote file:
- 5.37 kB
- SHA256:
- a57fcbc28681d4d60c0447b9f9ab53fde4001e1850cc48ce73fed761f7ade098
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.