Instructions to use ProbeX/Model-J__DINO__model_idx_0827 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_0827 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_0827") 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_0827") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0827", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41d38829e210fe968ec48c4f36977a7e62adc34e42ccbeb9451b03ef40d1782c
- Size of remote file:
- 5.37 kB
- SHA256:
- af45bb8903508cc9491631d51c1e7bf22675e8fafd86fa5fdef117ef72e80c7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.