Instructions to use ProbeX/Model-J__DINO__model_idx_0744 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_0744 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_0744") 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_0744") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0744") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcb0a3c7e427376721f4e4987d4a876affdb8827bb2ea42fc78a5e2630fb4952
- Size of remote file:
- 5.37 kB
- SHA256:
- 6305d75667a7627a1be450ebd3f0cf81eb852055a54216a55bc79d6a5c1dc932
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.