Instructions to use ProbeX/Model-J__ResNet__model_idx_0465 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_0465 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_0465") 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_0465") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0465") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2194851ea66ed1e0517d6a1db461110f4190d654b214aa21d7fb216804a0017d
- Size of remote file:
- 5.37 kB
- SHA256:
- 0c6d06bac5a736e6a81f72ac4fa1783d9cff1ab31e502ec4ec701a144aa23a53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.