Instructions to use ProbeX/Model-J__ResNet__model_idx_0611 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_0611 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_0611") 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_0611") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0611") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ba0f92e50230cad0e33843c20a7c8d808e6a67f6361e396a5dbb6a287763aac
- Size of remote file:
- 5.37 kB
- SHA256:
- b433b435011a532d4bdb101e49c17633f7efd8f6176182aab1efe597300f72a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.