Instructions to use ProbeX/Model-J__DINO__model_idx_0029 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_0029 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_0029") 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_0029") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0029", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e01e4fdd4dfdcb48f8b7dc139ccf37b29ee88b9acd1e9f46aa60b3a0eec2118
- Size of remote file:
- 343 MB
- SHA256:
- c05f7716980689483aeaa028acc3a631c1dc0f634d3a400df3a736108a9a3b9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.