Instructions to use ProbeX/Model-J__DINO__model_idx_0528 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_0528 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_0528") 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_0528") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0528") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 113d08c1971e25b0f1ef4db204ffa9553dbd9d9ad4ace6c4c1962b4abd0fafdf
- Size of remote file:
- 5.37 kB
- SHA256:
- 232f94b3464c74c5a0dfad6041680d9b5022c87b1cb9a92314f071974693cbf6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.