Instructions to use ProbeX/Model-J__ResNet__model_idx_0897 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_0897 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_0897") 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_0897") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0897") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 202d0323bf4203d0635252b122ddb3923c7dc3ffe47a83be6a8fa432172d7200
- Size of remote file:
- 5.37 kB
- SHA256:
- aa3a9d69fffcbd35650006998101297f4983dee668bbe5c9a9731d891a7681dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.