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Hot answers tagged yolo

4 votes

Can YOLO detect large objects?

The example given based on the yolov1 paper: The last layer has a tensor of the dimension 7x7x30. but the dimension of the last tensor is not in every case 7x7x30. let be: S: the number of grid ...
• 116
3 votes
Accepted

Is it difficult to learn the rotated bounding box for a (rotated) object?

Cartesian Bias and Pipeline Efficiency You are experiencing a techno-cultural artifact of Cartesian-centric imaging running all the way back to the dawn of coordinate systems. It is the momentum of ...
• 7,503
3 votes

Is it difficult to learn the rotated bounding box for a (rotated) object?

Here's a recent paper that does what you're looking for. It looks like they achieve this simply by adding a couple rotated prior boxes and regressing the angles in between. This is similar to what ...
• 527
3 votes
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What's the role of bounding boxes in object detection?

A bounding box is a rectangle superimposed over an image within which all important features of a particular object is expected to reside. It's purpose is to reduce the range of search for those ...
• 7,503
3 votes
Accepted

Taxonomy of terms in DL

1.1 NVIDIA's architecture of processing cores designed for certain specific types of calculations, like 3D graphics that also can speed up DL calculations. 1.2 Both. CUDA cores are great for speeding ...
• 1,260
2 votes
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How to label training data for YOLO

In effect, the midpoint is contained in cell $2$. Cells $1,3,4$ will be shown at $P_c=0$ according to the YOLO algorithm, which only takes in count the cell that contains the midpoint and calculates ...
• 76
2 votes

Is it difficult to learn the rotated bounding box for a (rotated) object?

It should not be much more difficult to predict a rotated rectangle compared to a bounding box. A bounding box can be parameterized with 4 floats: $x_c$, $y_c$, width, height. A rotated rectangle can ...
2 votes
Accepted

What are the reasons behind slow YOLO training?

I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file: ...
• 1,396
2 votes

What are the main differences between YOLOv3 and RetinaNet object detection algorithms?

There are several key differences between YOLOv3 and RetinaNet. RetinaNet is an object detection model that utilizes two-stage cascade and sampling heuristics to address class imbalance during ...
• 1,114
2 votes

How to treat (label and process) edge case inputs in machine learning?

Unfortunately, the answer here is that "it depends". People have taken different approaches to this problem and I'll describe a few here. None of which however is the "right" ...
• 527
2 votes
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What is a unified neural network model?

A unified neural network model consists of one neural network as opposed to other models that rely on two or more neural networks. For example, from page two of the YOLO paper: 2. Unified Detection ...
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2 votes
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How are IOUs for ground truth boxes in YOLO calculated?

My assumption was correct: the ground truth bounding box is aligned with an anchor box such that they share the same center In other words, only the widths and heights are used to calculate the ...
• 73
2 votes

How does YOLO handle non-class objects?

The output of YOLO is (x,y,w,h,confidence,class). The confidence value presents whether the rectangle holds an object, the rectangle is non-classed when confidence is low. The ...
• 1,283
2 votes

Object detection: combine many classes into one?

I think there is no absolute answer for this. Often its kind of trial and error. In general the CNN tries to generalize the problem, so using all logos with different augmentations and ground truths ...
• 116
2 votes

YOLO - are the anchor boxes used only in training?

Object detection models behave the same during both, training and test phase, i.e. they just return thousands and thousands of bounding boxes as predictions, plus a confidence score for each box (...
• 5,298
2 votes

How to identify and diferentiate several edge lines of an object?

I don't think that more advanced AI would necessarily produce more consistent results. Check something as simple as the Prewitt operator, which is pretty damn good at edge detection. I would suggest ...
2 votes
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Darknet as a part of Yolo v3

Lets start by listing what is what. RCNN : Is a type of CNN Model Resnet50, DarkNet53 and VGG : Are implementations of a CNN Model Now moving to your questions. Yes Darknet53 is the backbone of ...
2 votes
Accepted

Can DeepSort be made to track objects beside people?

In my experience, if you have really discriminative objects with distinct features then yes! The original DeepSORT's reid model can be borrowed to track those things. But for the best result, you ...
• 124
1 vote

What are the reasons behind slow YOLO training?

It depends upon the factors such as 1. Batch size (GPU memory capacity) 2. CPU speed and number of cores(multi-threading to load the images) Number of classes increase the number of convolution ...
1 vote
Accepted

Multiple labels for the same rectbox?

Duplicate boxes with unique labels make the problem too complex for the model. What I suggest is you use the horse face detection model to get a bounding box of the horse's face, crop the face image ...
• 329
1 vote

Should I label static objects on video dataset?

Model architecture: In machine learning, static image detectors can be is very different from video detectors, as movement plays a big role on the task. So, even when comparing frames the objects are ...
1 vote
Accepted

Object Detection: Can I modify this script to support larger images (Scaled YOLOv4)?

Yes, the functionality should is there. But, don't you think you are overdoing the scales. You have at least 18 scales mentioned here. Too much of anything is bad. There is a reason it likes things ...
1 vote
Accepted

Preparing data set for the YOLO algorithm

Ok, let go step by step. What you are working on is YOLOv1, in this version of the YOLO algorithm, the maximum bounding boxes that the model can return is 7x7 = 49 boxes as 49 cells since the output ...
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1 vote

Get object's orientation or angle after object detection

There is a paper face pose estimation It uses a very straight forward technique, and very obvious augmentaions to achieve nice results. You could use exactly the same if you have a tagged dataset for ...
• 759
1 vote

Get object's orientation or angle after object detection

I think the problem can be phrased (more generally) as a Pose Estimation Problem. That term might help in obtaining better search results when searching for relevant papers. One paper that I found on ...
• 815
1 vote

Feeding YOLOv4 image data into LSTM layer?

You can save the extracted features coming out of YOLOv4 and save it to pickle file and use them for later. You can also find related information in this project made by Jason Brownlee https://...
1 vote
Accepted

YOLOv3 Model Structure: Why is filters = (classes + coords + 1) * num?

As said by @brale in the comment below the question: ...
• 1,283
1 vote

Calculation of FPS on object detection task

You can use the dataset test set as "frames" of video. Test the images with your model and calculate the images per second of the result and that is the same as frames per second. However you should ...
• 1,745
1 vote

What are the differences between Yolo v1 and CenterNet?

You've already mentioned some technical differences between the two architectures. I think a key difference is in their "philosophy" and what they are trying to achieve. CenterNet's abstract ...
1 vote

Would YOLO be able to detect objects in "different" positions?

As I know about the YOLO, its algorithm splits the whole picture into many small frames and performs classification and boundaries detection at once for every frame, so that the location of the object ...

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