Instructions to use ProbeX/Model-J__ResNet__model_idx_0291 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_0291 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_0291") 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_0291") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0291") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48b2675f1c453bfe83ef5ee27a9a66cc2b6cf99ad3f31104888c520da47fc982
- Size of remote file:
- 5.37 kB
- SHA256:
- 0e756771d9539ebe9ca2ae20d520fd45c68cb84b02f12227332cc1b42f0905db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.