Hugging Face· Platform

Falcon Perception

TL;DR — Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural language prompts. The model processes image patches + text in one sequence using a hybrid attention mask, and produces variable numbers of instances with a small, structured token interface and lightweight output heads. On SA-Co , Falcon Perception reaches 68.0 Macro-F1 (vs. 62.3 for SAM 3) with the main remaining gap being presence calibration (MCC 0.64 vs. 0.82 ). We also introduce PBench , a diagnostic benchmark that breaks down performance by capability (attributes, OCR-guided disambiguation, spatial constraints, relations) and by dense long-context crowded scenes. We also relase Falcon OCR , a 0.3B-parameter model which reaches a score of 80.3 and 88.6 on the olmOCR benchmark and OmniDocBench respectively, while having the highest throughput of any open source OCR model. This post is a brief, practical write-up of what we built, why we built it this way, and what we learned along the way. …

Announced

01 Apr 2026 07:13 UTC

Detected

03 Aug 2026 21:12 UTC (+124d after)