Instructions to use ProbeX/Model-J__ResNet__model_idx_0109 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_0109 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_0109") 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_0109") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0109") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b6a34ac036fe5cd9a948a7ca90f923a4183ab2c561a489672d483ff30782600
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ba60d8aef8a63982d555fed95bda76f6b131a9208be993a9fcbd5bdfd49eff1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.