LingBot-Vision - A general-purpose visual foundation model open-sourced by Antminer Lingbo Technology

What is LingBot-Vision?

LingBot-Vision is an open-source general-purpose vision foundation model from Antminer Technology. It is the first in the industry to use boundary structures as pre-training targets and employs geometric modeling to achieve a breakthrough in spatial perception training paradigms. The model was trained on a corpus of only 160 million images, possessing sub-pixel-level boundary localization and spatial structure understanding capabilities. It supports continuous tracking of object boundaries in video and is available in four open-source versions: ViT-G, L/B, and S.

Main functions of LingBot-Vision

• Boundary structure pre-trainingThis is the first time in the industry that boundary structures have been used as pre-training targets, achieving a breakthrough in spatial perception paradigms.
• Subpixel-level boundary positioningIt possesses high-precision object boundary recognition and spatial structure understanding capabilities.
• Video Continuous TrackingIt can stably and continuously track object boundaries in video, maintaining temporal consistency.
• Multi-purpose moldIn addition to supporting depth completion, it has general visual representation and multi-task transfer capabilities.
• Multi-specification open sourceIt offers four parameter scales: ViT-G, L/B, and S, to adapt to different deployment needs.

The technical principles of LingBot-Vision

• Breakthrough in Geometric Modeling ParadigmTraditional visual foundational models primarily employ discriminative self-supervised pre-training, learning general visual representations through contrastive learning or mask reconstruction. LingBot-Vision, addressing spatial perception needs, pioneers a geometric modeling paradigm, shifting the core of visual understanding from semantic classification to structural analysis. By modeling object boundaries and spatial relationships, it constructs visual representations oriented towards the physical world.
• Boundary structure pre-training targetAs the industry's first visual foundation model to establish boundary structure as a core pre-training objective, LingBot-Vision forces the model to learn the geometric continuity of object contours, edge curvature variations, and spatial topological relationships of adjacent surfaces during the pre-training phase. This gives the model's encoder a natural high sensitivity to object boundaries.
• Subpixel-level boundary positioning mechanismLingBot-Vision achieves sub-pixel-level boundary localization capabilities through precise geometric supervision signals. The model can distinguish between real object edges and texture noise, and can still accurately infer object contours in low-contrast scenes such as transparent objects and reflective surfaces, thus completing the 3D structure of areas where traditional depth cameras fail.
• Efficient data training strategiesLingBot-Vision's pre-training corpus consists of only 160 million images, an order of magnitude smaller than DINOv3. It demonstrates, through training guided by structured geometric prior knowledge, that visual models can learn stronger spatial perception capabilities with less data under explicit physical constraints.

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How to use LingBot-Vision

• Download ModelAccess the HuggingFace or ModelScope repository to obtain the ViT-G/L/B/S version weight file.
• Reading codeClone the GitHub open-source repository to view inference examples and API documentation.
• Integrated deployment: Load the model weights into the visual encoder module and replace the existing backbone network for forward inference.
• Fine-tuning adaptationThe open-source weights are fine-tuned based on downstream task data and migrated to specific application scenarios.

LingBot-Vision's core advantages

• Training data efficiencyThe model requires 160 million images for pre-training, which is an order of magnitude smaller than DINOv3, resulting in a significant improvement in data efficiency.
• Accurate boundary recognitionIt provides clearer and more stable identification of object boundaries and spatial structures compared to mainstream models.
• Spatial perception nativeIt is designed with a geometric modeling method specifically for spatial perception, breaking through the traditional visual basic model paradigm.
• Video tracking stabilityIt has the ability to continuously track object boundaries in video, and exhibits excellent temporal consistency.
• Improved open source ecosystemThe model weights, technical reports, and code are all fully open source, supporting flexible selection of multiple specifications.

LingBot-Vision project address

• Project official websitehttps://technology.robbyant.com/lingbot-vision
• GitHub repositoryhttps://github.com/robbyant/lingbot-vision
• HuggingFace model libraryhttps://huggingface.co/collections/robbyant/lingbot-vision
• arXiv technical paper: https://github.com/robbyant/lingbot-vision/blob/main/paper.pdf

Comparison of LingBot-Vision with similar products

Application Scenarios of LingBot-Vision

• Embodied intelligent visionIt provides robots with precise spatial structure and boundary perception capabilities, assisting in navigation and operation.
• Deep completion enhancementAs the backbone network of LingBot-Depth 2.0, it improves the depth estimation accuracy of transparent/reflective objects.
• Industrial quality inspectionThe model uses subpixel-level boundary positioning to detect minute defects and contour deviations in components.
• Autonomous driving perceptionEnhance the stability of road boundary and obstacle edge recognition, and improve the reliability of environmental understanding.
• Video content analysisIt continuously tracks the boundaries of target objects in video sequences, supporting security monitoring and behavior analysis.