Instructions to use ProbeX/Model-J__DINO__model_idx_0523 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_0523 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_0523") 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_0523") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0523", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1ef72383ebbdee857fb907468a63da3292706e4b569be01a3a68e43f5e91ca9
- Size of remote file:
- 343 MB
- SHA256:
- 7330b205fc81904a03368bed1ca07a81b4d25681977063757db1395c25c4b81f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.