Yolo architecture

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Yolo architecture

Last Updated on October 8, Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition e. In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions.

In this tutorial, you will discover how to develop a YOLOv3 model for object detection on new photographs. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision bookwith 30 step-by-step tutorials and full source code. Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.

It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.

The approach involves a single deep convolutional neural network originally a version of GoogLeNet, later updated and called DarkNet based on VGG that splits the input into a grid of cells and each cell directly predicts a bounding box and object classification.

The result is a large number of candidate bounding boxes that are consolidated into a final prediction by a post-processing step. The first version proposed the general architecture, whereas the second version refined the design and made use of predefined anchor boxes to improve bounding box proposal, and version three further refined the model architecture and training process.

Although the accuracy of the models is close but not as good as Region-Based Convolutional Neural Networks R-CNNsthey are popular for object detection because of their detection speed, often demonstrated in real-time on video or with camera feed input. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.

The repository provides a step-by-step tutorial on how to use the code for object detection. It is a challenging model to implement from scratch, especially for beginners as it requires the development of many customized model elements for training and for prediction.

For example, even using a pre-trained model directly requires sophisticated code to distill and interpret the predicted bounding boxes output by the model. Instead of developing this code from scratch, we can use a third-party implementation. There are many third-party implementations designed for using YOLO with Keras, and none appear to be standardized and designed to be used as a library.

The YAD2K project was a de facto standard for YOLOv2 and provided scripts to convert the pre-trained weights into Keras format, use the pre-trained model to make predictions, and provided the code required to distill interpret the predicted bounding boxes. Many other third-party developers have used this code as a starting point and updated it to support YOLOv3. The code in the project has been made available under a permissive MIT open source license.

He also has a keras-yolo2 project that provides similar code for YOLOv2 as well as detailed tutorials on how to use the code in the repository. The keras-yolo3 project appears to be an updated version of that project. Interestingly, experiencor has used the model as the basis for some experiments and trained versions of the YOLOv3 on standard object detection problems such as a kangaroo dataset, racoon dataset, red blood cell detection, and others.

He has listed model performance, provided the model weights for download and provided YouTube videos of model behavior. For example:. In case the repository changes or is removed which can happen with third-party open source projectsa fork of the code at the time of writing is provided. The keras-yolo3 project provides a lot of capability for using YOLOv3 models, including object detection, transfer learning, and training new models from scratch.

In this section, we will use a pre-trained model to perform object detection on an unseen photograph. This script is, in fact, a program that will use pre-trained weights to prepare a model and use that model to perform object detection and output a model. It also depends upon OpenCV.Image Credits: Karol Majek.

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Check out his YOLO v3 real time detection video here. In other words, this is the part where we create the building blocks of our model. The code for this tutorial is designed to run on Python 3. It can be found in it's entirety at this Github repo. Part 3 : Implementing the the forward pass of the network.

Part 5 : Designing the input and the output pipelines. I assume you have had some experiene with PyTorch before. If you're just starting out, I'd recommend you to play around with the framework a bit before returning to this post. Then, create a file darknet. Darknet is the name of the underlying architecture of YOLO. This file will contain the code that creates the YOLO network. We will supplement it with a file called util.

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Save both of these files in your detector folder. You can use git to keep track of the changes. The official code authored in C uses a configuration file to build the network. The cfg file describes the layout of the network, block by block. If you're coming from a caffe background, it's equivalent to.

We will use the official cfg file, released by the author to build our network. Download it from here and place it in a folder called cfg inside your detector directory. If you're on Linux, cd into your network directory and type:.

We see 4 blocks above. Out of them, 3 describe convolutional layers, followed by a shortcut layer. A shortcut layer is a skip connection, like the one used in ResNet. There are 5 types of layers that are used in YOLO:. A shortcut layer is a skip connection, akin to the one used in ResNet. The from parameter is -3which means the output of the shortcut layer is obtained by adding feature maps from the previous and the 3rd layer backwards from the shortcut layer.

Upsamples the feature map in the previous layer by a factor of stride using bilinear upsampling.You only look once, or YOLO, is one of the faster object detection algorithms out there.

Though it is no longer the most accurate object detection algorithm, it is a very good choice when you need real-time detection, without loss of too much accuracy. This is not going to be a post explaining what YOLO is from the ground up.

yolo architecture

I assume you know how YOLO v2 works. It still, however, was one of the fastest. But that speed has been traded off for boosts in accuracy in YOLO v3. This has to do with the increase in complexity of underlying architecture called Darknet.

YOLO v2 used a custom deep architecture darknet, an originally layer network supplemented with 11 more layers for object detection. With a layer architecture, YOLO v2 often struggled with small object detections. This was attributed to loss of fine-grained features as the layers downsampled the input.

To remedy this, YOLO v2 used an identity mapping, concatenating feature maps from from a previous layer to capture low level features. No residual blocks, no skip connections and no upsampling.

YOLO v3 incorporates all of these. For the task of detection, 53 more layers are stacked onto it, giving us a layer fully convolutional underlying architecture for YOLO v3. Here is how the architecture of YOLO now looks like. The newer architecture boasts of residual skip connections, and upsampling.

The most salient feature of v3 is that it makes detections at three different scales. YOLO is a fully convolutional network and its eventual output is generated by applying a 1 x 1 kernel on a feature map.

In YOLO v3, the detection is done by applying 1 x 1 detection kernels on feature maps of three different sizes at three different places in the network. The feature map produced by this kernel has identical height and width of the previous feature map, and has detection attributes along the depth as described above.

In the following examples, I will assume we have an input image of size x YOLO v3 makes prediction at three scales, which are precisely given by downsampling the dimensions of the input image by 32, 16 and 8 respectively. The first detection is made by the 82nd layer. For the first 81 layers, the image is down sampled by the network, such that the 81st layer has a stride of If we have an image of xthe resultant feature map would be of size 13 x One detection is made here using the 1 x 1 detection kernel, giving us a detection feature map of 13 x 13 x Then, the feature map from layer 79 is subjected to a few convolutional layers before being up sampled by 2x to dimensions of 26 x This feature map is then depth concatenated with the feature map from layer Then the combined feature maps is again subjected a few 1 x 1 convolutional layers to fuse the features from the earlier layer Then, the second detection is made by the 94th layer, yielding a detection feature map of 26 x 26 x A similar procedure is followed again, where the feature map from layer 91 is subjected to few convolutional layers before being depth concatenated with a feature map from layer Like before, a few 1 x 1 convolutional layers follow to fuse the information from the previous layer We make the final of the 3 at th layer, yielding feature map of size 52 x 52 x Detections at different layers helps address the issue of detecting small objects, a frequent complaint with YOLO v2.Recognize what the objects are inside a given image and also where they are in the image.

YOLO is a clever neural network for doing object detection in real-time. Before you continue, make sure to watch the awesome YOLOv2 trailer. You can take a classifier like VGGNet or Inception and turn it into an object detector by sliding a small window across the image.

At each step you run the classifier to get a prediction of what sort of object is inside the current window. Using a sliding window gives several hundred or thousand predictions for that image, but you only keep the ones the classifier is the most certain about. A slightly more efficient approach is to first predict which parts of the image contain interesting information — so-called region proposals — and then run the classifier only on these regions. The classifier has to do less work than with the sliding windows but still gets run many times over.

YOLO takes a completely different approach. Each of these cells is responsible for predicting 5 bounding boxes. A bounding box describes the rectangle that encloses an object.

YOLO also outputs a confidence score that tells us how certain it is that the predicted bounding box actually encloses some object. The predicted bounding boxes may look something like the following the higher the confidence score, the fatter the box is drawn :. For each bounding box, the cell also predicts a class.

How to train YOLOv3 to detect custom objects

This works just like a classifier: it gives a probability distribution over all the possible classes. The confidence score for the bounding box and the class prediction are combined into one final score that tells us the probability that this bounding box contains a specific type of object.

From the total bounding boxes we only kept these three because they gave the best results. But note that even though there were separate predictions, they were all made at the same time — the neural network just ran once. No fancy stuff. There is no fully-connected layer in YOLOv2. So we end up with channels for every grid cell. These numbers contain the data for the bounding boxes and the class predictions.

Why ? Well, each grid cell predicts 5 bounding boxes and a bounding box is described by 25 data elements:. Tip: To learn more about how YOLO works and how it is trained, check out this excellent talk by one of its inventors.

This video actually describes YOLOv1, an older version of the network with a slightly different architecture, but the main ideas are still the same.

Worth watching!Andrew Ng. The problem description is taken straightaway from the assignment. As can be seen from the next figure. A typical output data vector will contain 8 entries for a 4-class classification, as shown in the next figure, the first entry will correspond to whether or not an object of any from the 3 classes of objects.

In case one is present in an image, the next 4 entries will define the bounding box containing the object, followed by 3 binary values for the 3 class labels indicating the class of the object. In case none of the objects are present, the first entry will be 0 and the others will be ignored.

yolo architecture

The above pictures are taken from a car-mounted camera while driving around Silicon Valley. Here we will use both representations, depending on which is more convenient for a particular step.

In this exercise, we shall learn how YOLO works, then apply it to car detection. Because the YOLO model is very computationally expensive to train, we will load pre-trained weights for our use. We will use 5 anchor boxes.

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Since we are using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height. For simplicity, we will flatten the last two last dimensions of the shape 19, 19, 5, 85 encoding.

So the output of the Deep CNN is 19, 19, Now, for each box of each cell we will compute the following element-wise product and extract a probability that the box contains a certain class. Doing that results in a visualization like this:.

Each cell gives us 5 boxes.

YOLO: Real-Time Object Detection

Different colors denote different classes. In the figure above, we plotted only boxes that the model had assigned a high probability to, but this is still too many boxes.You only look once YOLO is a state-of-the-art, real-time object detection system.

YOLOv3 is extremely fast and accurate. In mAP measured at. Moreover, you can easily tradeoff between speed and accuracy simply by changing the size of the model, no retraining required! Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. We use a totally different approach.

We apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region.

These bounding boxes are weighted by the predicted probabilities. Our model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image.

yolo architecture

It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. See our paper for more details on the full system. YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more.

The full details are in our paper! This post will guide you through detecting objects with the YOLO system using a pre-trained model. If you don't already have Darknet installed, you should do that first. Or instead of reading all that just run:. You will have to download the pre-trained weight file here MB.

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Or just run this:. Darknet prints out the objects it detected, its confidence, and how long it took to find them. We didn't compile Darknet with OpenCV so it can't display the detections directly.

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Instead, it saves them in predictions. You can open it to see the detected objects. Since we are using Darknet on the CPU it takes around seconds per image. If we use the GPU version it would be much faster. I've included some example images to try in case you need inspiration. The detect command is shorthand for a more general version of the command.

It is equivalent to the command:. You don't need to know this if all you want to do is run detection on one image but it's useful to know if you want to do other things like run on a webcam which you will see later on. Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row.

C4W3L09 YOLO Algorithm

Instead you will see a prompt when the config and weights are done loading:. Once it is done it will prompt you for more paths to try different images. Use Ctrl-C to exit the program once you are done. By default, YOLO only displays objects detected with a confidence of. For example, to display all detection you can set the threshold to So that's obviously not super useful but you can set it to different values to control what gets thresholded by the model.YOLO is a deep learning model that can predict object classes and location.


It belongs to the group of classifications algorithm. Compared to other methods it is simple, fast, and robust. YOLO is simple because it is a regressional model only, which means that it only use deep learning to directly predict results.

It is fast, thanks for its single pass model. It is robust, because it use the whole context of the image to do a prediction, so it is less sensible to patterns inside a subpart of the image. YOLO being a deep learning model, we will first need a set of labeled of images to train our network.

The encoding of the data is key to the understanding the model. Then, we need to evaluate the confidence of the predicted boxes. The final code is available on my github.

GstInference with TinyYoloV2 architecture

The implementation is done with the minimum of code for better comprehension. A good dataset determine how well a network will learn. If the dataset ignore some object, the network will learn to ignore them as well. A good dataset for YOLO is one that has accurate bounding boxes for all objects that appear.

I had an extra constraint of processing power as I only train model on CPU alone. The usual options to get a dataset are : downloading one, building one, generating one. Downloading a dataset is the easiest option. Kaggle has a lot of them too. Google can help you locate one too.

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They are often quite large in Gigabytesso they are not suitable for little tests. The second possibility is to create one. Experienced people say you should aim to have at least examples of objects for each class to train YOLO with good generalisation.

The third possibility, and the one I used here, is to generate a dataset programmatically. This solution certainly produce less realistic dataset, but it is still a good choice because you can control how complex your data can be : how many items, how dense, etc… I used the python object library for this. I generate images with the texts chat and rat randomly placed inside an image. As we are generating images, we write bounding boxes of objects in a text file named the same way as their image.

For each cell YOLO predict a class of object in the form of a one hot vector, five boxes and a confidence score for each box. Mauricio Menegaz explains that structure really well. The trick is to have the network to predict coordinates that are limited in their range to a single cell. Since we only predict value between 0 and 1, we will consider width and height to be in the system of coordinate of the image.

A box of image width will have a width of 1. We do this by adding a tensor of offset to the network prediction in the loss function. YOLO use a backed of conv2D, leaky relu and max pooling for pattern detection, then a prediction layer composed of two densely connected layers.


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