A number of rectangular boxes of different shapes and sizes are generated centered on each anchor. An anchor box is a reference box of a specific scale and aspect ratio. Luckily somebody else is explained this in detail here Faster R-CNN is the state of the art object detection algorithm. Especially in this article Faster RCNN. An anchor is a box. Hence, there are 10s of thousands of anchor boxes per image. Models Faster RCNN consists of mainly four parts: 1) Conv Layers: As a CNN network target detec-tion method, Faster RCNN firstly uses a set of basic Conv+ReLU+pooling layers to extract image feature maps. Fast RCNN detection network on top of proposals. for faster convergence, here only we try to apply same for the case of anchor boxes. It is similar to how we initialize weights of a Neural Net (using Xavier or Kaiming Initialization etc.) 2. However this is not explained well and causes trouble to most of the readers. Although it was discussed later in the paper I feel you should know it before getting into RPN. For example in Fig 1, 38x57x9 = 19494 anchor boxes are generated. Our region proposal network (RPN) classifies which regions have the object and the offset of the object bounding box. Anchor boxes are a major part of modern object detectors. To detect objects of different scales, they change the scale of the anchor boxes such that the areas of each of them are 128², 256², and 512². Negative anchors: An anchor is a negative anchor if its IoU ratio is lower than 0.3 for all ground-truth boxes. Left: Anchors, Center: Anchor for a single point, Right: All anchors B. With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region. Faster RCNN Network (RPN+Fast RCNN) Source: Faster RCNN paper Author: Shaoqing Ren What are anchor boxes. This can be thought of as a pyramid of reference anchor boxes. Fig. What Is an Anchor Box? I don't know the actual answer, but I suspect that the way Faster RCNN works in Tensorflow object detection is as follows: this article says: "Anchors play an important role in Faster R-CNN. If you have ideas to improve this, we can discuss! Usually 9 boxes are generated per anchor (3 sizes x 3 shapes) as shown in Fig 4. Training is done using the same logic. The use of anchor boxes improves the speed and efficiency for the detection portion of a deep learning neural network framework. The paper proposes k anchor boxes, having aspect ratios- 1:1, 2:1, and 1:2. You can think this technique as a good initialization for anchor boxes for bounding box predictions. Non-Maximum suppression to reduce region proposals. Faster-RCNN Loss the receptive field of those $3*3$ spatial locations are $(16*3)^2$ in the original image and I think that that means the anchors area should be smaller than $(16*3)^2$. 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