Instructions to use ProbeX/Model-J__ResNet__model_idx_0890 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_0890 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_0890") 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_0890") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0890") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ad73fcc560cbaaf425babcd0020d081123676f11bbfdd2d2c32c2a58a0de00a
- Size of remote file:
- 5.37 kB
- SHA256:
- c97f14ebd172facc40a7fda60fcbdaa6ff234330f119d96c69a89d3b153b386a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.