Instructions to use ProbeX/Model-J__DINO__model_idx_0944 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_0944 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_0944") 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_0944") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0944", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2058b8594586b2b1add8ac4f19033d50eefc710b2f219dea5e32d14cdbc6d8fe
- Size of remote file:
- 5.37 kB
- SHA256:
- c2e78402e117ba5ea08ef79a5ed049af64ed1bffb9ae1eace8d7a0bce70f727a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.