Falcon Perception
Hugging Face Blog
Falcon Perception is a 0.6-billion-parameter early-fusion Transformer model that handles open-vocabulary image segmentation and object grounding from natural language prompts using a single unified architecture rather than separate modular pipelines. The model achieves 68.0 Macro-F1 on the SA-Co benchmark compared to 62.3 for SAM 3, with particularly large gains on attribute-heavy (+8.2 points), food and drink (+12.2 points), and spatial understanding tasks (+21.9 points on PBench's spatial tier). The unified design allows the model to excel at compositional prompts requiring text reading, spatial reasoning, and dense scene understanding where modular approaches typically fail.