Instructions to use ProbeX/Model-J__ResNet__model_idx_0650 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_0650 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_0650") 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_0650") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0650") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acf0e21f2e4ba4a5106a4138f93ad30b6f3b42697a6d7da48507317883a69176
- Size of remote file:
- 5.37 kB
- SHA256:
- c1989c55703f3fb82091edb059ee1c4360b92a5c9a47718bb1ed5dc6dac1588c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.