Instructions to use ProbeX/Model-J__DINO__model_idx_0279 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_0279 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_0279") 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_0279") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0279", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ac08fe1a0117a4d0a3c4bfe669d2b521bc1e8f7d22b48db3cfef5bc63e07d62
- Size of remote file:
- 5.37 kB
- SHA256:
- 061363729137c7f5bb96118bdecacc63edf351215ac25249bc9c6d3f85c6cebb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.