Instructions to use ProbeX/Model-J__DINO__model_idx_0782 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_0782 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_0782") 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_0782") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0782") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 366dfd8aceb04030445b93f0a009849b5a30226242aaa385fee9d5c21ad189d6
- Size of remote file:
- 5.37 kB
- SHA256:
- fb84b46c3ec705ecf42b9e70dbf77beca6e541bf86b195d69dd6c160942015d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.