Instructions to use ProbeX/Model-J__DINO__model_idx_0294 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_0294 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_0294") 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_0294") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0294", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54314b6217dc75d87d071b75f02319ea624c97ba3ca7b421e5631586f58936d5
- Size of remote file:
- 5.37 kB
- SHA256:
- 711e4d2009b4dfcbeb546a78e6b3d7115e3834486771d0dfb437981e4207223f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.