Instructions to use ProbeX/Model-J__ResNet__model_idx_0643 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_0643 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_0643") 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_0643") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0643", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa2647d470cc1f47f5af69ca808a5e3a4b273447991e1bb3fa2bd024a2ca1cae
- Size of remote file:
- 5.37 kB
- SHA256:
- e5c06d9af13eff701c9c56ae9db9a705b913ed5f3ad4dd2afecf2404b1f714c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.