Instructions to use ProbeX/Model-J__ResNet__model_idx_0641 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_0641 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_0641") 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_0641") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0641", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b25b324220738c67ad53f2b38b1986a6ffd2859b5d245a31c495a99e034aefbd
- Size of remote file:
- 5.37 kB
- SHA256:
- cfc8be407ebdc6857d10ba00b20a1246529f8a2938b6496818dbdc5aad9d2d96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.