Instructions to use ProbeX/Model-J__DINO__model_idx_0665 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_0665 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_0665") 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_0665") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0665", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1362c21a4eaabb6a3bb4b12b92a1065b0c0b6adef411d20768d8df97daa1a4b9
- Size of remote file:
- 5.37 kB
- SHA256:
- 17c309a3f1969c22cb7f2ac55b152df01260a522266327fe3387e8c298dbf98e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.