Instructions to use ProbeX/Model-J__ResNet__model_idx_0976 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_0976 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_0976") 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_0976") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0976") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc6ba26f0d3562377b2fb843c6200164b267243afd85e31158a4905d11868a8b
- Size of remote file:
- 5.37 kB
- SHA256:
- 35b8709e6f0e642c25de6880789ed5b933c15128853df2a785b86746351bc39f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.