Instructions to use ProbeX/Model-J__ResNet__model_idx_0179 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_0179 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_0179") 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_0179") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0179") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65f55ecda492ce0aa60b7bee9b8d19da8f533407ae795dffd009d39d83984bc3
- Size of remote file:
- 5.37 kB
- SHA256:
- cbb83808ca7b346511f08430fa414eaed92fda368fa6501a6c5ce8e425d76ad3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.