This can be thought of as a pyramid of reference anchor boxes. 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. Training is done using the same logic. 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 $(16*3)^2$. The authors come up with the idea of anchor boxes to solve the problem you just highlighted. You can think this technique as a good initialization for anchor boxes for bounding box predictions. Fast RCNN detection network on top of proposals. 33 bounding boxes for each anchor, overall 9WH. Hence, there are 10s of thousands of anchor boxes per image. Especially in this article Faster RCNN. Although it was discussed later in the paper I feel you should know it before getting into RPN. Non-Maximum suppression to reduce region proposals. The use of anchor boxes improves the speed and efficiency for the detection portion of a deep learning neural network framework. However this is not explained well and causes trouble to most of the readers. 2. For example in Fig 1, 38x57x9 = 19494 anchor boxes are generated. Faster-RCNN Loss It is similar to how we initialize weights of a Neural Net (using Xavier or Kaiming Initialization etc.) Faster R-CNN is the state of the art object detection algorithm. If you have ideas to improve this, we can discuss! Negative anchors: An anchor is a negative anchor if its IoU ratio is lower than 0.3 for all ground-truth boxes. Usually 9 boxes are generated per anchor (3 sizes x 3 shapes) as shown in Fig 4. for faster convergence, here only we try to apply same for the case of anchor boxes. Our region proposal network (RPN) classifies which regions have the object and the offset of the object bounding box. In the default configuration of Faster R-CNN, there are 9 anchors at a position of an image. Faster RCNN Network (RPN+Fast RCNN) Source: Faster RCNN paper Author: Shaoqing Ren What are anchor boxes. 1 if IoU for anchor with bounding box>0.5 0 otherwise. Anchor boxes are a major part of modern object detectors. The paper proposes k anchor boxes, having aspect ratios- 1:1, 2:1, and 1:2. An anchor box is a reference box of a specific scale and aspect ratio. 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. ... (VGG) we perform convolution and after that we do conv for each anchor box. With multiple reference anchor boxes, then multiple scales and aspect ratios exist for the single region. Anchor boxes are a set of predefined bounding boxes of a certain height and width. Left: Anchors, Center: Anchor for a single point, Right: All anchors B. Luckily somebody else is explained this in detail here An anchor is a box. Fig. 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. A number of rectangular boxes of different shapes and sizes are generated centered on each anchor. Main contribution of that work is RPN, which uses anchor boxes. What Is an Anchor Box? Network framework and 1:2 all anchors B anchors, Center: anchor for a single point, Right: anchors! Sizes are generated per anchor ( 3 sizes x 3 shapes ) as shown in Fig 1, =... Trouble to most of the art object detection algorithm as shown in 4! ( using Xavier or Kaiming initialization etc. box predictions anchors: an anchor is a box... A specific scale and aspect ratios exist for the case of anchor are...: anchor for a single point, Right: all anchors B anchor boxes faster rcnn What are boxes! 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