关于Keras版YOLOv3输出维度中网格尺寸的技术咨询

Let's break down your questions clearly and practically:

Why those specific grid sizes? #

The 13×13, 26×26, and 52×52 grids are tied directly to YOLOv3's backbone design and multi-scale detection strategy:

• YOLOv3 commonly uses a 416×416 input image (you can adjust this, but 416 is standard because it's divisible by 32, the largest downsampling factor in the network).
• Each grid corresponds to a different downsampling level from the input:
  • 13×13: 416 ÷ 32 = 13 (32x downsampling) — this feature map has the widest receptive field, making it perfect for detecting large objects (like cars, full-body people in distant shots).
  • 26×26: 416 ÷ 16 = 26 (16x downsampling) — medium receptive field, ideal for medium-sized objects (like dogs, close-up upper bodies).
  • 52×52: 416 ÷ 8 = 52 (8x downsampling) — narrowest receptive field, designed for small objects (like traffic signs, face details, tiny items).

The core idea here is that different objects need different levels of feature detail: small objects require fine-grained, high-resolution maps to capture their edges, while large objects can be detected from coarser, lower-resolution maps.

Is the "max 13×13 targets" guess correct? #

That's a common misconception — here's the reality:

• Each grid cell doesn't just predict one object. YOLOv3 assigns 3 anchor boxes per grid cell (this is where the 255 channels come from: 3 boxes × (80 classes + 5 box attributes: x, y, width, height, confidence)). So each grid cell can potentially detect up to 3 distinct objects (though in practice, it's usually one dominant object per cell after filtering).
• Even more importantly, YOLO uses Non-Maximum Suppression (NMS) to eliminate overlapping, redundant bounding boxes. This means the actual number of detectable objects isn't limited by the grid count — you can detect far more than 13×13 objects as long as they fit within the image and the model's anchor boxes match their shapes.

In short: the grid size defines how the model partitions the image for localization, but the number of detectable objects depends on anchor box design, NMS, and the model's ability to distinguish overlapping objects.

内容的提问来源于stack exchange，提问作者Xeyes