Image Classification
Transformers
Safetensors
tipsv2_dpt
feature-extraction
vision
depth-estimation
surface-normals
semantic-segmentation
dense-prediction
tipsv2
vision-language-model
custom_code
Instructions to use Rikunarita-ORG/tipsv2-so400m14-dpt-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rikunarita-ORG/tipsv2-so400m14-dpt-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rikunarita-ORG/tipsv2-so400m14-dpt-merged", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("Rikunarita-ORG/tipsv2-so400m14-dpt-merged", trust_remote_code=True) model = AutoModel.from_pretrained("Rikunarita-ORG/tipsv2-so400m14-dpt-merged", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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