Instructions to use ProbeX/Model-J__ResNet__model_idx_0446 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_0446 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_0446") 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_0446") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0446") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27519a0806a3aa7979896985bec32c9f7ff841c48b9f0a4fdfd4bf27d6a9bd65
- Size of remote file:
- 5.37 kB
- SHA256:
- a97c9867db24023d3a58f0bc397e6e418c31643a005a4aa24acd9be808b50d18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.