Instructions to use ProbeX/Model-J__ResNet__model_idx_0561 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_0561 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_0561") 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_0561") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0561") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e292f150a125ad2c0ab7f880b6ed5da58cf64585fe87271275dc49bce0447132
- Size of remote file:
- 5.37 kB
- SHA256:
- 7eef76ccb1cc29fad730b73b7da396a5c72d4b772716ba8eabe8449ce5a55a85
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.