Instructions to use ProbeX/Model-J__DINO__model_idx_0072 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_0072 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_0072") 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_0072") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0072", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb4d4a56464bb3818a2506842e6518a4dabc2bc6ef6d9352acfa9ed8d663b26b
- Size of remote file:
- 5.37 kB
- SHA256:
- d24400b8358aeb52ecd025073da3313f2881c925da8cbb5d799bf53d014e030f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.