Instructions to use ProbeX/Model-J__DINO__model_idx_0284 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_0284 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_0284") 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_0284") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0284", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 126f85ea8e9f11748595d151647f52f7ab3fe0441b4ed34dfeebd3ebe9117f15
- Size of remote file:
- 5.37 kB
- SHA256:
- 9e36ba5b5541882e110b5406f730d9b934a9de6898f1553ccfa8a21e676079f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.