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omdet-turbo-swin-tiny-hf

omlab

The OmDet model was proposed in Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head by Tiancheng Zhao, Peng Liu, Xuan He, Lu Zhang, Kyusong Lee from Om AI Lab. If you like our model, please consider following our project on GitHub OmDet to receive updates and information about new model releases. We also invite you to explore our latest work on the Agents Framework OmAgent.

Category pending

Input Not verified→ Output Not verified

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VLM-R1-Qwen2.5VL-3B-OVD-0321

omlab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Visual QA

Input Text · Image→ Output Text

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VLX-Seek-1.5-10B

omlab

VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other edge-side visual intelligence applications where a model must identify what is present, localize the right instance, and avoid grounding objects that are absent. Unlike coordinate-generation-based VLMs that directly decode bounding-box numbers, VLX-Seek reformulates localization as region retrieval and region reference. Candidate visual regions are represented as addressable entities, and the model answers by selecting, comparing, and referring to those regions. This makes localization more aligned with the strengths of language models: comparison, selection, reference, and reasoning. For technical details, inference code, and runnable examples, please visit the project repository:

Visual QA

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 3

omdet-turbo-swin-tiny-hf

VLM-R1-Qwen2.5VL-3B-OVD-0321

VLX-Seek-1.5-10B

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