Let’s call the predictions made by the network as ox and oy. Notice that, after passing through 3 convolutional layers, we are left with a feature map of size 3x3x64 which has been termed penultimate feature map i.e. In our example, 12X12 patches are centered at (6,6), (8,6) etc(Marked in the figure). Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). For the sake of argument, let us assume that we only want to deal with objects which are far smaller than the default size. The one line solution to this is to make predictions on top of every feature map(output after each convolutional layer) of the network as shown in figure 9. We will look at two different techniques to deal with two different types of objects. We can use priorbox to select the ground truth for each prediction. TensorFlow Object Detection step by step custom object detection tutorial. In this part of the tutorial, we will train our object detection model to detect our custom object. To address this problem, SSD uses hard negative mining: all background samples are sorted by their predicted background scores in the ascending order. It can easily be calculated using simple calculations. Deep convolutional neural networks can predict not only an object's class but also its precise location. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. And then we assign its ground truth target with the class of object. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. Configuring your own object detection model. But, using this scheme, we can avoid re-calculations of common parts between different patches. Welcome to part 5 of the TensorFlow Object Detection API tutorial series. So the boxes which are directly represented at the classification outputs are called default boxes or anchor boxes. My hope is that this tutorial has provided an understanding of how we can use the OpenCV DNN module for object detection. In this post, I will give you a brief about what is object detection, … Pre-trained Feature Extractor and L2 normalization: Although it is possible to use other pre-trained feature extractors, the original SSD paper reported their results with VGG_16. Being fully convolutional, the network can run inference on images of different sizes. Part 3. If you're new to PyTorch, first read Deep Learning with PyTorch: A 60 Minute Blitz and Learning PyTorch with Examples. A classic example is "Deformable Parts Model (DPM) ", which represents the state of the art object detection around 2010. feature map just before applying classification layer. A sliding window detection, as its name suggests, slides a local window across the image and identifies at each location whether the window contains any object of interests or not. For us, that means we need to setup a configuration file. And each successive layer represents an entity of increasing complexity and in doing so, their receptive field on input image increases as we go deeper. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. And all the other boxes will be tagged bg. Smaller objects tend to be much more difficult to catch, especially for single-shot detectors. First, we take a window of a certain size(blue box) and run it over the image(shown in Figure below) at various locations. This is very important. You'll need a machine with at least one, but preferably multiple GPUs and you'll also want to install Lambda Stack which installs GPU-enabled TensorFlow in one line. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. How is it so? 8 Developing SSD-Object Detection Models for Android Using TensorFlow 7. Doing so creates different "experts" for detecting objects of different shapes. This is where priorbox comes into play. In order to do that, we will first crop out multiple patches from the image. However, it turned out that it's not particularly efficient with tinyobjects, so I ended up using the TensorFlow Object Detection APIfor that purpose instead. Just like all other sliding window methods, SSD's search also has a finite resolution, decided by the stride of the convolution and the pooling operation. So let’s look at the method to reduce this time. 04. The Matterport Mask R-CNN project provides a library that allows you to develop and train Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. researchers and engineers. Given an input image, the algorithm outputs a list of objects, each associated with a class label and location (usually in the form of bounding box coordinates). And thus it gives more discriminating capability to the network. The following figure-6 shows an image of size 12X12 which is initially passed through 3 convolutional layers, each with filter size 3×3(with varying stride and max-pooling). So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. Let’s see how we can train this network by taking another example. Before the renaissance of neural networks, the best detection methods combined robust low-level features (SIFT, HOG etc) and compositional model that is elastic to object deformation. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… The class of the ground truth is directly used to compute the classification loss; whereas the offset between the ground truth bounding box and the priorbox is used to compute the location loss. SSD (Single Shot MultiBox Detector) is a popular algorithm in object detection. For a real-world application, one might use a higher threshold (like 0.5) to only retain the very confident detection. Loss values of ssd_mobilenet can be different from faster_rcnn. We compute the intersect over union (IoU) between the priorbox and the ground truth. In a moment, we will look at how to handle these type of objects/patches. Each location in this map stores classes confidence and bounding box information as if there is indeed an object of interests at every location. Object detection is modeled as a classification problem. The prediction layers have been shown as branches from the base network in figure. But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive.Check out the below image as an example. Now that we have taken care of objects at different locations, let’s see how the changes in the scale of an object can be tackled. Smaller priorbox makes the detector behave more locally, because it makes distanced ground truth objects irrelevant. It is like performing sliding window on convolutional feature map instead of performing it on the input image. And then since we know the parts on penultimate feature map which are mapped to different paches of image, we direcltly apply prediction weights(classification layer) on top of it. Specifically, we show how to build a state-of-the-art Single Shot Multibox Detection [Liu16] model by stacking GluonCV components. Let us understand this in detail. The paper about SSD: Single Shot MultiBox Detector (by C. Szegedy et al.) Some other object detection networks detect objects by sliding different sized boxes across the image and running the classifier many times on different sections. A simple strategy to train a detection network is to train a classification network. The patch 2 which exactly contains an object is labeled with an object class. If you would like to contribute a translation in another language, please feel free! The task of object detection is to identify "what" objects are inside of an image and "where" they are. Remember, conv feature map at one location represents only a section/patch of an image. I hope all these details can now easily be understood from referring the paper. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. Train SSD on Pascal VOC dataset, we briefly went through the basic APIs that help building the training pipeline of SSD.. In practice, only limited types of objects of interests are considered and the rest of the image should be recognized as object-less background. There can be multiple objects in the image. The ground truth object that has the highest IoU is used as the target for each prediction, given its IoU is higher than a threshold. For predictions who have no valid match, the target class is set to the. Let’s take an example network to understand this in details. Now, let’s move ahead in our Object Detection Tutorial and see how we can detect objects in Live Video Feed. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. Multi-scale increases the robustness of the detection by considering windows of different sizes. For example, SSD512 use 4, 6, 6, 6, 6, 4, 4 types of different priorboxes for its seven prediction layers, whereas the aspect ratio of these priorboxes can be chosen from 1:3, 1:2, 1:1, 2:1 or 3:1. Data augmentation: SSD use a number of augmentation strategies. This means that when they are fed separately(cropped and resized) into the network, the same set of calculations for the overlapped part is repeated. You could refer to TensorFlow detection model zoo to gain an idea about relative speed/accuracy performance of the models. 05. . Predictions from lower layers help in dealing with smaller sized objects. This technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. We can see that the object is slightly shifted from the box. We can see that 12X12 patch in the top left quadrant(center at 6,6) is producing the 3×3 patch in penultimate layer colored in blue and finally giving 1×1 score in final feature map(colored in blue). After which the canvas is scaled to the standard size before being fed to the network for training. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. This tutorial goes through the basic building blocks of object detection provided by GluonCV. The papers on detection normally use smooth form of L1 loss. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. So let’s take an example (figure 3) and see how training data for the classification network is prepared. The deep layers cover larger receptive fields and construct more abstract representation, while the shallow layers cover smaller receptive fields. However, its performance is still distanced from what is applicable in real-world applications in term of both speed and accuracy. For the sake of convenience, let’s assume we have a dataset containing cats and dogs. We also know in order to compute a training loss, this ground truth list needs to be compared against the predictions. Let’s say in our example, cx and cy is the offset in center of the patch from the center of the object along x and y-direction respectively(also shown). Likewise, a "zoom out" strategy is used to improve the performance on detecting small objects: an empty canvas (up to 4 times the size of the original image) is created. Object detection has … But, using this scheme, we can avoid re-calculations of common parts between different patches. How to set the ground truth at these locations? Now, this is how we need to label our dataset that can be used to train a convnet for classification. So this saves a lot of computation. For more complete information about compiler optimizations, see our Optimization Notice. which can thus be used to find true coordinates of an object. Since the 2010s, the field of object detection has also made significant progress with the help of deep neural networks. Convolutional networks are hierarchical in nature. I hope all these details can now easily be understood from, A quick complete tutorial to save and restore Tensorflow 2.0 models, Intro to AI and Machine Learning for Technical Managers, Human pose estimation using Deep Learning in OpenCV. We already know the default boxes corresponding to each of these outputs. More on Priorbox: The size of the priorbox decides how "local" the detector is. Note that the position and size of default boxes depend upon the network construction. This has two problems. We then feed these patches into the network to obtain labels of the object. was released at the end of November 2016 and reached new records in terms of performance and precision for object detection tasks, scoring over 74% mAP (mean Average Precision) at 59 frames per second on standard datasets such as PascalVOC and COCO. Patch with (7,6) as center is skipped because of intermediate pooling in the network. For this Demo, we will use the same code, but we’ll do a few tweakings. Precisely, instead of mapping a bunch of pixels to a vector of class scores, SSD can also map the same pixels to a vector of four floating numbers, representing the bounding box. computation to accelerate human progress. . When combined together these methods can be used for super fast, real-time object detection on resource constrained devices (including the Raspberry Pi, smartphones, etc.) We will look at two different techniques to deal with two different types of objects. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. One type refers to the object whose, (default size of the boxes). This is something well-known to image classification literature and also what SSD is heavily leveraged on. This creates extra examples of large objects. Train SSD on Pascal VOC dataset¶. You can think there are 5461 "local prediction" behind the scene. Being simple in design, its implementation is more direct from GPU and deep learning framework point of view and so it carries out heavy weight lifting of detection at. Let’s increase the image to 14X14(figure 7). So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. In this case we use car parts as labels for SSD. This is something pre-deep learning object detectors (in particular DPM) had vaguely touched on but unable to crack. This creates extras examples of small objects and is crucial to SSD's performance on MSCOCO. The patches for other outputs only partially contains the cat. This is the. This can easily be avoided using a technique which was introduced in SPP-Net and made popular by Fast R-CNN. Given an input image, the algorithm outputs a list of objects, each associated with a class label and location (usually in the form of bounding box coordinates). Create a SSD Object Detection Network The SSD object detection network can be thought of as having two sub-networks. Vanilla squared error loss can be used for this type of regression. The following figure shows sample patches cropped from the image. Multi-scale Detection: The resolution of the detection equals the size of its prediction map. This tutorial explains how to accelerate the SSD using OpenVX* step by step. Hi Tiri, there will certainly be more posts on object detection. This will amount to thousands of patches and feeding each of them in a network will result in huge of amount of time required to make predictions on a single image. Here the multibox is a name of the technique for the bounding box regression. Lambda is an AI infrastructure company, providing
We can see there is a lot of overlap between these two patches(depicted by shaded region). So one needs to measure how relevance each ground truth is to each prediction, probably based on some distance based metric. So for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. I had initially intendedfor it to help identify traffic lights in my team's SDCND CapstoneProject. In this tutorial, we'll create a simple React web app that takes as input your webcam live video feed and sends its frames to a pre-trained COCO SSD model to detect objects on it. That is called its receptive field size. Last but not least, SSD allows feature sharing between the classification task and the localization task. It’s generally faste r than Faster RCNN. figure 3: Input image for object detection. Let us see how their assignment is done. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. Intuitively, object detection is a local task: what is in the top left corner of an image is usually unrelated to predict an object in the bottom right corner of the image. Such a brute force strategy can be unreliable and expensive: successful detection requests the right information being sampled from the image, which usually means a fine-grained resolution to slide the window and testing a large cardinality of local windows at each location. If output probabilities are in the order cat, dog, and background, ground truth becomes [1 0 0]. This concludes an overview of SSD from a theoretical standpoint. For reference, output and its corresponding patch are color marked in the figure for the top left and bottom right patch. This tutorial shows you it can be as simple as annotation 20 images and run a Jupyter notebook on Google Colab. We then feed these patches into the network to obtain labels of the object. The only requirements are a browser (I'm using Google Chrome), and Python (either version works). In this part and few in future, we're going to cover how we can track and detect our own custom objects with this API. Here is a gif that shows the sliding window being run on an image: We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. We know the ground truth for object detection comes in as a list of objects, whereas the output of SSD is a prediction map. We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. Firstly the training will be highly skewed(large imbalance between object and bg classes). As you can imagine this is very resource-consuming. So we resort to the second solution of tagging this patch as a cat. The details for computing these numbers can be found here. The prediction layers have been shown as branches from the base network in figure. If you want a high-speed model that can work on detecting video feed at high fps, the single shot detection (SSD) network is the best. They behave differently because they use different parameters (convolutional filters) and use different ground truth fetch by different priorboxes. To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. Object detection has been a central problem in computer vision and pattern recognition. Basic knowledge of PyTorch, convolutional neural networks is assumed. But in this solution, we need to take care of the offset in center of this box from the object center. Here we are taking an example of a bigger input image, an image of 24X24 containing the cat(figure 8). So it is about finding all the objects present in an image, predicting their labels/classes and assigning a bounding box around those objects. Reducing redundant calculations of Sliding Window Method, Training Methodology for modified network. This is the third in a series of tutorials I'm writing about implementing cool models on your own with the amazing PyTorch library.. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. The feature extraction network is typically a pretrained CNN (see Pretrained Deep Neural Networks (Deep Learning Toolbox) for … And all the other boxes will be tagged bg. And with MobileNet-SSD inference, we can use it for any kind of object detection use case or application. Let us assume that true height and width of the object is h and w respectively. The input of each prediction is effectively the receptive field of the output feature. And shallower layers bearing smaller receptive field can represent smaller sized objects. This will amount to thousands of patches and feeding each of them in a network will result in huge of amount of time required to make predictions on a single image. In this blog, I will cover Single Shot Multibox Detector in more details. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. For SSD512, there are in fact 64x64x4 + 32x32x6 + 16x16x6 + 8x8x6 + 4x4x6 + 2x2x4 + 1x1x4 = 24564 predictions in a single input image. This repository is a tutorial for how to use TensorFlow's Object Detection API to train an object detection classifier for multiple objects on Windo… Let’s call the predictions made by the network as ox and oy. And in order to make these outputs predict cx and cy, we can use a regression loss. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. This is the key idea introduced in Single Shot Multibox Detector. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). Notice, experts in the same layer take the same underlying input (the same receptive field). The output of SSD is a prediction map. The system is able to identify different objects in the image with incredible acc… The fixed size constraint is mainly for efficient training with batched data. In consequence, the detector may produce many false negatives due to the lack of a training signal of foreground objects. . It has been explained graphically in the figure. So we assign the class “cat” to patch 2 as its ground truth. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. To do this, we need the Images, matching TFRecords for the training and testing data, and then we need to setup the configuration of the model, then we can train. instances to some of the world’s leading AI
We will skip this minor detail for this discussion. A typical CNN network gradually shrinks the feature map size and increase the depth as it goes to the deeper layers. A feature extraction network, followed by a detection network. In my hand detection tutorial, I’ve included quite a few model config files for reference. There are few more details like adding more outputs for each classification layer in order to deal with objects not square in shape(skewed aspect ratio). The original image is then randomly pasted onto the canvas. TF has an extensive list of models (check out model zoo) which can be used for transfer learning.One of the best parts about using TF API is that the pipeline is extremely optimized, i.e, your … Object Detection using Hog Features: In a groundbreaking paper in the history of computer vision, … There is a minor problem though. I followed this tutorial for training my shoe model. So we add two more dimensions to the output signifying height and width(oh, ow). I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. So the idea is that if there is an object present in an image, we would have a window that properly encompasses the object and produce label corresponding to that object. In essence, SSD does sliding window detection where the receptive field acts as the local search window. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . Pick a model for your object detection task. The papers on detection normally use smooth form of L1 loss. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. Deep convolutional neural networks can classify object very robustly against spatial transformation, due to the cascade of pooling operations and non-linear activation. Various patches generated from input image above. Most object detection systems attempt to generalize in order to find items of many different shapes and sizes. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. It is also important to add apply a per-channel L2 normalization to the output of the conv4_3 layer, where the normalization variables are also trainable. This classification network will have three outputs each signifying probability for the classes cats, dogs, and background. . Now during the training phase, we associate an object to the feature map which has the default size closest to the object’s size. This concludes an overview of SSD from a theoretical standpoint. Live Object Detection Using Tensorflow. Tensorflow object detection API is a powerful tool for creating custom object detection/Segmentation mask model and deploying it, without getting too much into the model-building part. We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Single Shot MultiBox Detector (SSD *) is fast and accurate object detection with a single network. ResNet9: train to 94% CIFAR10 accuracy in 100 seconds with a single Turing GPU, NVIDIA RTX A6000 Deep Learning Benchmarks, Install TensorFlow & PyTorch for the RTX 3090, 3080, 3070, 1, 2 & 4-GPU NVIDIA Quadro RTX 6000 Lambda GPU Cloud Instances, (AP) IoU=0.50:0.95, area=all, maxDets=100, Hardware: Lambda Quad i7-7820X CPU + 4 x GeForce 1080 Ti. Only the top K samples are kept for proceeding to the computation of the loss. SSD makes the detection drastically more robust to how information is sampled from the underlying image. We can see there is a lot of overlap between these two patches(depicted by shaded region). When we’re shown an image, our brain instantly recognizes the objects contained in it. This significantly reduced the computation cost and allows the network to learn features that also generalize better. In the first part of today’s post on object detection using deep learning we’ll discuss Single Shot Detectors and MobileNets.. Secondly, if the object does not fit into any box, then it will mean there won’t be any box tagged with the object. Then we again use regression to make these outputs predict the true height and width. So we can see that with increasing depth, the receptive field also increases. The question is, how? To compute mAP, one may use a low threshold on confidence score (like 0.01) to obtain high recall. Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected.
ssd object detection tutorial 2021