Instructions to use ProbeX/Model-J__DINO__model_idx_0940 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__DINO__model_idx_0940 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__DINO__model_idx_0940") 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__DINO__model_idx_0940") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0940") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 933cd5faa602131c25888bca48d50410edffd0defc9a1bbc62d7284135cd9661
- Size of remote file:
- 5.37 kB
- SHA256:
- 4d16c533de811aaf59027a08fe6a6e30d36203266f16ce082108354d9797ced8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.