Instead of having all these different heads or decoders for specific tasks, it just handles text and image outputs for a bunch of vision stuff. you feed it images and some natural language, and it spits out the answer. pretty straightforward, honestly.

it can output a bunch of different things, like object categories, bounding boxes, OCR text, even camera pose. and for images, it does segmentation masks, depth maps, surface normals, all that. apparently it can even mix and match for compositional tasks, like segmentation and depth in one go.

they trained it on a massive 50M instruction-response dataset, built from all sorts of CV annotations. everything's open too: the dataset, the training pipeline, and the inference code. nice.

quick heads up on hardware: the demo space apparently needs a single 80GB GPU. and if you want to run the full benchmark setup, you're looking at 8x80GB. so yeah, this isn't gonna run on your average consumer card, unfortunately. definitely for folks with serious hardware or if you can get API access.

model: [https://huggingface.co/sensenova/SenseNova-Vision-7B-MoT](https://huggingface.co/sensenova/SenseNova-Vision-7B-MoT)

demo: [https://huggingface.co/spaces/sensenova/SenseNova-Vision](https://huggingface.co/spaces/sensenova/SenseNova-Vision)

code: [https://github.com/OpenSenseNova/SenseNova-Vision](https://github.com/OpenSenseNova/SenseNova-Vision)

Sensenova-vision: 7B model that treats computer vision like one big generation problem