Instructions to use ProbeX/Model-J__DINO__model_idx_0024 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_0024 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_0024") 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_0024") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0024") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53333813dc4e8d41bd7b567a6dd8cd5c31ca6fc8a7620a8e5955c698963f743b
- Size of remote file:
- 5.37 kB
- SHA256:
- d5af275838918c7b4a4a9ecf925b234ba2b53d6bf38c56cbab9d469e8a392b22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.