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