SenseNova-Vision-MoT

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What SenseNova-Vision-MoT is

Vision as Unified Multimodal Generation 🌟 Overview SenseNova-Vision is a unified multimodal model for computer vision. It reformulates heterogeneous visual perception tasks as text generation, image generation, or mixed text-image generation, instead of relying on task-specific heads, decoders, or loss functions for each individual task. The model supports structured visual understanding, dense geometric prediction, segmentation, and multi-view visual geometry within a shared instruction-following interface. 🚀 Model Description SenseNova-Vision rethinks computer vision as unified multimodal generation. Traditional computer-vision systems usually attach task-specific prediction heads for detection, segmentation, depth, surface normal, or 3D geometry. SenseNova-Vision instead expresses these heterogeneous tasks through the native input-output spaces of a unified multimodal model. Natural-language instructions and optional visual prompts specify the target task, regions, views, output schema, and decoding convention. The model then generates different target formats depending on the task: This formulation allows a single model to cover structured visual understanding, dense geometric prediction, segmentation, and multi-view visual geometry while keeping outputs decodable for standard benchmarks. 🌐 Key Features - Unified vision-task formulation: Heterogeneous computer-vision tasks are cast into the native text, image, and mixed generation spaces of a unified multimodal model. - No task-specific heads: The model does not rely on separate detection, segmentation, depth, normal, or geometry heads. - Decodable outputs: Generated text and images can be converted back into benchmark-compatible boxes, points, OCR strings, masks, depth maps, normal maps, point maps, and camera records. - Broad task coverage: The same model handles structured visual understanding, segmentation, dense geometry, and multi-view visual geometry. - Instruction-defined task variants: Natural-language instructions enable flexible task definitions beyond fixed benchmark schemas. 🛠️ How to Use Please use the official inference code from the SenseNova-Vision GitHub repository: Environment Setup Create

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys sensenova-vision-mot for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (sensenova-vision-mot below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sensenova-vision-mot","messages":[{"role":"user","content":"Hello"}]}'

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Commercial licence needed

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