Instructions to use ProbeX/Model-J__DINO__model_idx_0158 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_0158 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_0158") 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_0158") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0158", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 272c21ef36425a20ae4358695f6e8ed7bb7ee40d21c335cd9dc619ca9893ff2a
- Size of remote file:
- 5.37 kB
- SHA256:
- d6a315298a9e7631281d1f46a32ad5d1b86faf48ec13c76137e1edcc1decd9f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.