Instructions to use ProbeX/Model-J__DINO__model_idx_0038 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_0038 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_0038") 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_0038") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0038", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45438732b639cb84ab36e53021fae1740b28bf14b17380b1db7f42736accd8d2
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ed5f5b0f65fc80d4b16ab2ab00631919733ad3e2b8cf44fd3b2e6b750a6dbca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.