Instructions to use ProbeX/Model-J__DINO__model_idx_0975 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_0975 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_0975") 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_0975") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0975") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba6d662aa309e871ca5d2f02b414067c04396cb4cacd4b6fc738846a032960e9
- Size of remote file:
- 5.37 kB
- SHA256:
- e7d4f6aebe3b1b2007099e7a3eac5fa57b657da51de4aece59d88087fd1d81a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.