Depth Estimation
Transformers
Safetensors
qwen3_vl
image-text-to-text
vision-language-model
3d-vision
multimodal
qwen3-vl
Instructions to use JonnyYu828/DepthVLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonnyYu828/DepthVLM-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="JonnyYu828/DepthVLM-4B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("JonnyYu828/DepthVLM-4B") model = AutoModelForImageTextToText.from_pretrained("JonnyYu828/DepthVLM-4B") - Notebooks
- Google Colab
- Kaggle
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# DepthVLM
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DepthVLM serves as a unified foundation model for both low-level dense geometry prediction and high-level multimodal understanding, while achieving substantially faster inference compared with existing VLM-based approaches such as DepthLM and Youtu-VL.
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# DepthVLM-4B
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DepthVLM serves as a unified foundation model for both low-level dense geometry prediction and high-level multimodal understanding, while achieving substantially faster inference compared with existing VLM-based approaches such as DepthLM and Youtu-VL.
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