Instructions to use ProbeX/Model-J__DINO__model_idx_0801 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_0801 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_0801") 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_0801") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0801", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a814d4b04a07c53930b3f0543e12270a22bfa2e3121168b89737a88c55cfded7
- Size of remote file:
- 5.37 kB
- SHA256:
- 5d5fd7c28eeb1ed931d64e38d4ddedf91d9273f66e57d48f5130451ecad03c28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.