Instructions to use ProbeX/Model-J__DINO__model_idx_0363 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_0363 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_0363") 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_0363") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0363", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e24ead9b81f90786205939bbc3d9a3845b23448fec9f76733ba17e5aeed9484e
- Size of remote file:
- 5.37 kB
- SHA256:
- 745eb99044499eafef614967b2e61d0fb4f97f3c5b159dbb0da7ccc8bc765e81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.