Questions tagged [yolo]

"You Only Look Once", a Deep Learning-based object recognition algorithm available in several different software implementations.

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YOLO v2 loss function

I'm trying to understand (and implement) the YOLOv2 loss function, which is not given explicitly in the original paper. There are several posts on this topic, but quite a few seem to confuse the ...
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Balancing a dataset in object detection through data augmentation and then random oclusion of classes?

first of all some info about the model I'm using. I'm using YOLOR, which uses a YOLOV5 formatted dataset. I'm trying to detect components off of PCB's which are divided into some groups (visible on ...
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Math behind predicting the dimensions of bounding boxes

I have been trying to understand the mechanics behind YOLO v3. I got stuck at the section which defines the calculation of bounding box. Going through various blogs, I found that The dimensions of ...
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Questions about mAP results from YOLOv2 paper

In the YOLOv2 paper, is the mAP metric displayed in Table 3 calculated in the same way as the metric in the column '0.5' from Table 5?
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How are the confidence scores in YOLO computed?

I have read Yolo Loss function explanation but none of the answers discuss how box confidence scores are computed. The YOLO paper uses the following loss function: I'm confused about how confidence ...
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Question about center coordinate definition in region proposal networks

In the YOLOv2 paper, page 3, it states that region proposal networks predicts $t_x$ and $t_y$ where $(x,y)$ center coordinates are calculated as: $$x=t_x*w_a-x_a$$ $$y=t_y*h_a-y_a$$ such that for $t_x ...
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Question YOLOv2 bounding box prior

In the YOLO9000 paper, they define the distance between a box and centroid as $d(box, centroid) = 1 - IOU(box, centroid)$. I think box here is a ground truth bounding box, but what is centroid?
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Conditional class probabilities in YOLOv1

In YOLO v1, authors defines conditional class probabilities (P(Class_i/Object)) I don't understand that authors can define the head output to conditional class probabilities. When the model finishes ...
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how to check if unknown object partly covers the object i want to detect

I want to detect an object from an image,I use yolo object detection. I detect car from the image. I consider the case when another unknown object partly covers it, how can my program know that there ...
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In YOLO, can there be more than one class within a cell?

I'm confused by the fact that YOLO** can have multiple bounding boxes per cell, but there is only one class vector. Does YOLO allow the truth/training to have more than one object in a cell, each with ...
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Performing object detection without localization

I'm looking to build a computer vision model that can detect the presence or absence of several classes of object within an image, but the localization of these objects is not required. The end ...
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What is the difference between these error metrics in object detection

I am wondering what is the difference in these error metrics (and what they are) in object detection: mAP_0.5:0.95, mAP_0.5, recall, precision
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Yolov- 3: How are features / detection at 3 different level arranged to give 1 final output?

In yolo-v3, at layer numbers 82,94,106, detection are made. What if Same object was detected in all those places? Grids were different due to no of grids ...
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Does it help to provide training samples with no target objects in object detection?

I am training a Yolo for object detection. I was wondering if it will help to provide images with no instances of the target object present. If so, what would the VOC label of such an instance look ...
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Does the Object you Want to Classify have to be in a Cluttered Background in Yolo?

I'm trying to classify plant diseases with the plant village dataset, shown in the image above. These images are all very consistent, with exactly one leaf and similar backgrounds. However, I am ...
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YoloV3: Training set, true label boxes vs anchor boxes

I understand the anchor boxes as a set of boxes that based on a comprehensive exploration of the data ( all the true bonding boxes) best describe the variable sizes of all true boxes in the training ...
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How to add labels to an already trained Yolo model?

I'm learning ML and I'm exploring object detection and classification. I discovered Yolo few months ago and it's impressively efficient and accurate. There are several pre-trained Yolo models on the ...
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What are "Grids" and Detection at different scales" in YOLOV3?

I've recently started working with Yolov3 and the more I go in depth, the more confused I get. In the simplest terms what I think about YOLOV3 ...
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Darknet and Data Augmentation

In the darknet deep learning framework .cfg files we see parameters like angle, saturation, exposure These parameters are used ...
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Why use multiple anchor boxes with the same positions in a multi-box detector?

What is the benefit of using multiple anchor boxes with the same positions in a single-shot multi-box detector model, like YOLO? In particular, I notice Google's BlazeFace model does this. If the ...
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Why Anchor Boxes in Object Detection

What I understand from the anchor boxes is that they are predefined set of boxes of different sizes and shapes. A model like YOLO predicts offsets of the coordinates for each anchor box along with ...
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1 answer
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1 neuron BCE loss VS 2 neurons CE loss

I built a custom version of YOLO that should only detect one type of objects, where the objectness measure (which tells how likely a bounding-box contains an object of any type), is learned using a ...
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yolo v1: how gradients in grids with no objects overpower

I am reading yolo v1 paper. I am having confusion while trying to understand this: "Also, in every image many grid cells do not contain any object. This pushes the “confidence” scores of those ...
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yolo v1: why we weigh localization error more than classification error

I'm reading yolo v1 paper. I am trying to understand this part of the paper: "We use sum-squared error because it is easy to optimize, however it does not perfectly align with our goal of ...
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In the case of YOLO, how does the network assign a box in it's grid based on the midpoint of the object?

My question is that how in YOLO, the networks does the midpoint of grid cell think ? I'm not completely sure I understand it. How can we know the midpoint of any object before actually knowing where ...
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Best way for robust hand tracking on PI 4

I need a robust hand tracking which should be running on a Raspberry Pi 4 model. Does anyone have some experience with that? One way could be to use OpenCV but I think it's just using the skin color ...
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Yolov3: Single-class vs multiple class

I'm really new to object detection with Yolov3. Let's say I have 10 classes and the amount of data is approximately the same. Do I achieve better average precision when I use 10 Yolo models and train ...
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1 answer
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Performance of MaskRCNN/YOLO as a function of object size in pixels

I am trying to find references on how the resolution of an object affects the ability of object detection systems such as MaskRCNN and YOLO to correctly identify the object. For example, if the ...
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1 answer
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How exactly the midpoint of the object is selected in YOLO algorithm?

The boundaries of the object are encoded into 4 scalars: Bx, By, Bh, Bw. Bh and Bw are calculated using the ratio of the width of the bounding box to the width of the relative grid cell. But I can't ...
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4 votes
2 answers
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How are YOLO anchor boxes generated?

I am recently trying out darkflow, a Tensorflow implementation of Darknet written by Joseph Redmon. Looking at the configuration files, I noticed a section called region as shown below. ...
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The reason for multiple bounding boxes use for each grid cell - YOLO

I'm not quite sure what is the main reason why YOLO uses multiple bounding boxes for a grid cell. An answer I can find on web is for multiple aspect ratio in the prediction. However I don’t think it’s ...
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What is sigma function in the YOLO object detector?

I have gone through the YOLO9000 paper, in that they have mentioned that network predicts 5 coordinates of the bounding box, and from that we find the exact centre coordinates and the width and height....
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Combining custom YOLO network for face detection with another CNN

I am looking for a way to build and train an end-to-end CNN that contains two steps: 1) a CNN for finding a face and hands in the image and 2) CNN that works on the crops of the face and hands. To ...
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How to "use" Yolo Loss Function

I am dealing with Yolo Loss Function (the following). $$\begin{align} &\lambda_{coord} \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}[(x_i-\hat{x}_i)^2 + (y_i-\hat{y}_i)^2 ] \\&+ \lambda_{...
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YOLOv3 loss function

Follow-up to stats.stackexchange.com/questions/373266/yolo-v3-loss-function: In trying to finalize the development of my training labels and loss function I'm confused by the part in bold in the ...
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what purpose do the grid cells serve in YOLO object detection algorithm?

so I was looking at YOLO and I read several blogs online, but one concept I'm having trouble understanding is why do we want to divide the image into different grids, and then predict the bounding box ...
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Is YOLO a good algorithm for defect detection on images?

I wish to train an algorithm to detect defects on images of labels. These may be such things as scratches, tears and voids. I would like to try to train a YOLO algorithm to do this, but it is very ...
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Yolo v3 loss function

The original loss function can be seen here and is more or less explained in Yolo Loss function explanation: \begin{align} &\lambda_{coord} \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}[(x_i-...
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7 votes
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On Yolo, and its loss function

I'm having a hard time understanding some on the inner-working of YOLO, especially the loss function depicted in this seminal paper. Bear in mind that I'm nowhere closed to being a specialist in deep ...
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What is the need of multiple Bounding Boxes per grid cell in YOLO v1?

This question is regarding YOLO v1 architecture as in here. I am confused as to why authors have used 2 bounding boxes per grid cell for training. Assuming there can be only one object per grid cell. ...
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Understanding the YOLO algorithm

I am trying to understand the logic behind object detection and the YOLO algorithm. I have read numerous blogs, tutorials, videos, papers, yet I am still not sure if I understood it correctly. Let's ...
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YOLO loss function width and height component explanation

I am reading this paper on how yolo defines loss function. https://arxiv.org/abs/1506.02640 I did research on other posts, but these posts did not seem to answer my confusion: (How to calculate the ...
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1 answer
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yolo cost function

At the output of the final layer of yolo, a leaky-relu is applied to the output, so if we have negative values for the width and height, the cost function will return a null value since we would have ...
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1 answer
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object detection loss function YOLO

as we know , in detection we have 2 loss : one is localization loss other is classification loss , in the formulation the 1th , 2th term related to localization loss and 3th term is related to ...
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Yolo loss function for detecting 1 class

I'm trying to work on a Yolo implementation which searches a 19x19 grid to find a specific item. There is only a single class in all of these images I am looking to get bounding boxes for. I'm a ...
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2 votes
2 answers
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How does the YOLO network create boundaries for object detection?

I'm not sure I understood how the YOLO network works. If you look at the description, https://pjreddie.com/darknet/yolo/, it appears to me that all is done thanks to convolution only. You end up with ...
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2 answers
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Adjusting Grid Size in YOLO?

I was going through the YOLO Object Detection Paper by Joseph Redmon. The authors use a grid size of $S = 7$. If I am not wrong, the network architecture has carefully been curated for this specific ...
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1 vote
3 answers
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You Only Look Once (YOLO): Convolutional Neural Network

This question refers to the YOLO architecture (figure 3). In their architecture they define a convolutional layer 7x7x64-s-2 followed by a maxpool layer ...
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31 votes
5 answers
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Yolo Loss function explanation

I am trying to understand the Yolo v2 loss function: \begin{align} &\lambda_{coord} \sum_{i=0}^{S^2}\sum_{j=0}^B \mathbb{1}_{ij}^{obj}[(x_i-\hat{x}_i)^2 + (y_i-\hat{y}_i)^2 ] \\&+ \lambda_{...
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2 votes
1 answer
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How to calculate the class probability of a grid cell in YOLO object detection algorithm?

I am going through the YOLO paper by Redmon, Divvala, Girshick & Farhadi (2015), "You Only Look Once: Unified, Real-Time Object Detection" (arXiV page here ) On the fourth page it mentions the ...
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