Instructions to use ProbeX/Model-J__DINO__model_idx_0065 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_0065 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_0065") 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_0065") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0065", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d235d68f0b2ed881a76726c7ef73a272d9d67c44a418bd6e5be2dc69ab014417
- Size of remote file:
- 5.37 kB
- SHA256:
- 9003ec9d5ac97e7078084c23b10e45de991751ea5f9316d8b81759220ab79131
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.