Instructions to use ProbeX/Model-J__DINO__model_idx_0675 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_0675 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_0675") 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_0675") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0675", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62cd2c78a93dc8892d3d24a4c54ea70265eb7264b747225a89ce3ed6c2361b48
- Size of remote file:
- 5.37 kB
- SHA256:
- 05341f6302fd4dce89a05d5565c75cb98c0c8a82d97cfa278c202276876db2ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.