YOLO vs SAM vs Grounding DINO: Task-Based Selection

The 3-Second Answer to "Which Model Should I Use?"

Know your bounding boxes ahead of time? YOLO. Need pixel-perfect masks from user clicks? SAM. Want to detect objects you've never trained on by describing them in text? Grounding DINO.

That's the short version. But here's the problem: most real projects don't fit neatly into one bucket. You end up combining these models, chaining them together, and suddenly your 30ms inference pipeline is taking 400ms.

I ran a systematic comparison across three detection scenarios—closed-set detection, open-vocabulary detection, and interactive segmentation—on the same hardware (RTX 4090, CUDA 12.1) with the same 1920x1080 images. The results pushed me to rethink when to use each tool.

Photo by Frank van Dijk on Pexels

Closed-Set Detection: YOLO Wins, But Not Always

When you have a fixed set of classes and enough labeled data, YOLO remains the fastest option. YOLOv8x runs at 8.2ms per frame on 640x640 input with 45.2 mAP on COCO val2017.

python
from ultralytics import YOLO
import torch
import time

# YOLOv8x - 68.2M params, ~130MB checkpoint
model = YOLO('yolov8x.pt')


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*Continue reading the full article on [TildAlice](https://tildalice.io/yolo-sam-grounding-dino-detection-guide/)*