Instructions to use ProbeX/Model-J__ResNet__model_idx_0619 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_0619 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_0619") 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_0619") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0619") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91effb99be493b6c2cdd36d5ab6abb5a8c2addedf488c15b605ea8db2e6f13f8
- Size of remote file:
- 5.37 kB
- SHA256:
- 902a72cbe56d2a0e891d8d8beb195c1b1129ff2312018019165f689d846d6a82
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.