Instructions to use ProbeX/Model-J__DINO__model_idx_0020 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_0020 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_0020") 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_0020") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c01c81a8f66a9813142758a2a1a78aa424f0e3d10b7d9b5c338f4810868aab4
- Size of remote file:
- 5.37 kB
- SHA256:
- e55e4c3929392d9db7648af524d2230de8bc921d8e08f19c568b8fa381f0de18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.