YOLO11 vs SAM 3, Florence-2, and the Open-Vocabulary Shift

YOLO11 Internals

YOLO11 uses a CSPDarknet-inspired backbone with C3k2 (CSP bottleneck with 2 convolutions) replacing YOLOv8’s C2f blocks. The new C2PSA (Position-Sensitive Attention) module adds self-attention in the backbone head — a first for the YOLO family. The neck uses PANet-style bidirectional feature fusion, and the head is fully anchor-free with decoupled branches for classification, regression, and mask prediction.

Key constraint: closed-vocabulary only. It detects whatever it was trained on (default: 80 COCO classes). To add new classes, you must fine-tune.

SAM 3 Internals

Meta’s SAM 3 introduces Promptable Concept Segmentation (PCS) — you pass a text phrase like “red car” and it returns pixel-level masks for all matching instances in the image. This is a major upgrade over SAM 2, which only accepted point/box/mask prompts.

The architecture uses a Hiera-based vision transformer backbone with a CLIP-like text encoder feeding into a transformer mask decoder. SAM 3 handles 270K unique concepts, achieving 75–80% of human performance on the SA-CO benchmark.

SAM 3 also includes a built-in Agent mode where any OpenAI-compatible VLM (Qwen 3.5, Gemma 4) acts as the reasoning layer — decomposing complex queries like “the leftmost child wearing a blue vest” into SAM 3 text prompts.

Florence-2 Internals

Microsoft’s Florence-2 uses a DaViT (Dual Attention Vision Transformer) encoder with a unified text decoder that routes to different tasks via text prompts. At just 0.23B params (base), it handles:

• <OD> — Object detection → bounding boxes + labels
• <CAPTION> — Image description
• <CAPTION_TO_PHRASE_GROUNDING> — Text → bbox grounding
• <DENSE_REGION_CAPTION> — All regions with descriptions
• <OCR> — Text detection and recognition

The trade-off: no pixel-level segmentation (bboxes only). Pair it with EfficientSAM3 for masks when needed.

GroundingDINO

The classic open-vocabulary detector. Uses a Swin Transformer backbone + BERT text encoder with bidirectional attention fusion. Given text queries like “cat . remote control .”, it outputs bounding boxes + labels for any matching object.

• GroundingDINO-tiny: ~60M params, ~48.4 novel mAP50-95 (zero-shot), ~30 FPS
• GroundingDINO-base: ~200M params, ~56.7 novel mAP50-95 (zero-shot), ~10 FPS
• License: Apache 2.0

YOLO-World

Combines YOLO speed with open-vocabulary detection via CLIP text encoding. The “prompt-then-detect” paradigm re-parameterizes text vocabulary as model weights, enabling real-time inference.

• ~50 FPS on RTX 3090 for detection
• Works with EfficientSAM3 for segmentation
• License: GPL-3.0 (less restrictive than AGPL, but still copyleft)

EfficientSAM3

The most practical SAM 3 alternative. Distills SAM 1 + SAM 2 + SAM 3 into mobile-friendly architectures — 90–95% smaller than full SAM 3 while retaining text-prompted segmentation capability. Available with EfficientViT, TinyViT, and RepViT backbones. ONNX and CoreML exports included.

• License: Apache 2.0

The AGPL Trap

If you use YOLO11 in any product accessible over a network, you must either open-source your entire application under AGPL-3.0 or buy the Enterprise License from Ultralytics. This applies even if you only use the model weights — the ultralytics Python package itself is AGPL-3.0.

Options if you’re already using YOLO11:

1. Buy the Enterprise License — cheapest if you’re deeply committed
2. Switch to Florence-2 + EfficientSAM3 — MIT + Apache 2.0, zero restrictions
3. Switch to YOLO-World + EfficientSAM3 — GPL-3.0 + Apache 2.0, same paradigm

Pipeline Tiers

Tier 1 — Ultra-Fast (< 100ms, single GPU 8GB): Florence-2-base → EfficientSAM3 (B0) → SmolVLM2-256M

Tier 2 — Fast (< 500ms, single GPU 12GB): Florence-2-large → EfficientSAM3 (TinyViT-11M) → Qwen3.5-0.8B

Tier 3 — Quality (< 2s, single GPU 24GB): SAM3-LiteText-S1 → Qwen3.5-4B

Tier 4 — Maximum Quality (multi-GPU): SAM 3 (bf16) → Qwen3.5-9B or Gemma-4-26B-A4B

Recommended VLMs

All Qwen 3.5 variants are natively multimodal — vision is built in, not bolted on. They use early-fusion training and outperform the separate Qwen3-VL models on visual benchmarks.

Integration Pattern: SAM 3 Agent

Meta provides an official Agent mode where the VLM decomposes complex queries into SAM 3 prompts:

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User: "the leftmost child wearing blue vest"
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  → VLM (Qwen 3.5 / Gemma 4) decomposes into SAM 3 text prompts
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  → SAM 3 generates pixel masks for matching objects
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  → VLM interprets results, provides natural language description

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from sam3 import build_sam3_image_model
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from sam3.model.sam3_image_processor import Sam3Processor
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from sam3.agent.inference import run_single_image_inference
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from sam3.agent.client_llm import send_generate_request
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from sam3.agent.client_sam3 import call_sam_service
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from functools import partial
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model = build_sam3_image_model()
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processor = Sam3Processor(model, confidence_threshold=0.5)
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send_fn = partial(send_generate_request,
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    server_url="http://localhost:8001/v1",
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    model="Qwen/Qwen3.5-9B",
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    api_key="EMPTY"
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)
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sam_fn = partial(call_sam_service, sam3_processor=processor)
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output = run_single_image_inference(
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    "image.jpg", "the leftmost child wearing blue vest",
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    {}, send_fn, sam_fn,
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    debug=True, output_dir="output"
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)

Important: run SAM 3 and vLLM in separate conda environments — they have conflicting dependency chains (SAM 3 needs Python 3.12 + PyTorch 2.7+).

Keep YOLO11 When

• You need real-time performance (> 30 FPS)
• Object classes are fixed and known
• You can fine-tune on your dataset
• You have an Enterprise License or your project is open-source (AGPL)

Switch to SAM 3 + VLM When

• You need open-vocabulary detection (“find the leftmost child wearing blue”)
• You need pixel-perfect masks, not just bboxes
• You need video object tracking
• You’re building an AI agent pipeline
• Meta’s license terms are acceptable for your use case

Switch to Florence-2 + EfficientSAM3 When

• MIT/Apache 2.0 licensing is non-negotiable
• You need detection + captioning + OCR in one model
• Bounding boxes are sufficient (add EfficientSAM3 for masks)
• You want the smallest possible model with the broadest capability

Recommended Migration Path

For commercial products, the fully permissive stack is:

Florence-2 (MIT) + EfficientSAM3 (Apache 2.0) + Qwen3.5-4B (Apache 2.0)

Detection, segmentation, OCR, captioning, and natural language understanding — no license fees, no copyleft, no gatekeeping.