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Yolov12: Everything you need to know
Before diving into the details of YOLOv12, let’s take a step back. If you’re unfamiliar with YOLO and the evolution of its versions, I recommend checking out this resource to understand the fundamentals and differences between versions.
Every new YOLO release claims to be state-of-the-art (SOTA) — but are they really? With each iteration, we hear promises of improved speed, accuracy, and efficiency. However, in real-world applications, do these improvements always translate to better performance?
🔥 Speed: Is YOLOv12 Actually Faster?
YOLOv12-N achieves 1.5 ms inference latency on a T4 GPU, a slight improvement over YOLO11-N’s 1.6 ms. However, real-world testing presents a different picture: YOLOv11 achieves 40 FPS, while YOLOv12 only reaches 30 FPS. This suggests that while YOLOv12 optimizes inference latency, YOLOv11 still holds an edge in real-time performance.
🎯 Accuracy: A Meaningful Improvement?
On the COCO dataset, YOLOv12-N delivers a 39.5% mAP, surpassing YOLO11-N’s 37.3%. But how significant is this in practical applications? A few percentage points of improvement might sound impressive on paper, but does it justify upgrading models and retraining pipelines?
📦 Model Size: More Compact, But at What Cost?
YOLOv12-N is designed with efficiency in mind, reducing parameter size to 2.6M, compared to YOLO11-N’s 3.2M. While a smaller footprint is beneficial, does it compromise feature extraction and generalization capabilities?
🏗️ Architecture: Buzzwords or Real Innovation?
One of YOLOv12’s key innovations is its attention-centric framework, boasting:
- Area attention modules for enhanced spatial focus
- Residual efficient layer aggregation networks for improved feature representation
