Instructions to use ProbeX/Model-J__ResNet__model_idx_0554 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_0554 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_0554") 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_0554") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0554") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa242a593790a7a0402dd118a5b3df7873311fd8442496059be54c15671abf16
- Size of remote file:
- 5.37 kB
- SHA256:
- 58c11fde981e20f7005eaf1e4ff87ca83460b5698bf58fce69a754d880962f5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.