The other type refers to the objects whose size is significantly different from 12X12. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. Welcome to part 5 of the TensorFlow Object Detection API tutorial series. This is very important. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. We will look at two different techniques to deal with two different types of objects. 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. This creates extras examples of small objects and is crucial to SSD's performance on MSCOCO. feature map just before applying classification layer. In essence, SSD does sliding window detection where the receptive field acts as the local search window. It does not only inherit the major challenges from image classification, such as robustness to noise, transformations, occlusions etc but also introduces new challenges, for example, detecting multiple instances, identifying their precise locations in the image etc. Lambda provides GPU workstations, servers, and cloud We can see there is a lot of overlap between these two patches(depicted by shaded region). This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. And all the other boxes will be tagged bg. Firstly the training will be highly skewed(large imbalance between object and bg classes). This is also a good starting point for your own object detection project. And then we run a sliding window detection with a 3X3 kernel convolution on top of this map to obtain class scores for different patches. So we assign the class “cat” to patch 2 as its ground truth. For example, SSD512 uses 20.48, 51.2, 133.12, 215.04, 296.96, 378.88 and 460.8 as the sizes of the priorbox at its seven different prediction layers. We denote these by. My hope is that this tutorial has provided an understanding of how we can use the OpenCV DNN module for object detection. For reference, output and its corresponding patch are color marked in the figure for the top left and bottom right patch. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. 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. We already know the default boxes corresponding to each of these outputs. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). The task of object detection is to identify "what" objects are inside of an image and "where" they are. But in this solution, we need to take care of the offset in center of this box from the object center. We will skip this minor detail for this discussion. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. Follow the instructions in this document to reproduce the results. It is notintended to be a tutorial. Convolutional networks are hierarchical in nature. For more information of receptive field, check thisout. Here I have covered this algorithm in a stepwise manner which should help you grasp its overall working. We also know in order to compute a training loss, this ground truth list needs to be compared against the predictions. The following figure shows sample patches cropped from the image. There can be locations in the image that contains no objects. So we resort to the second solution of tagging this patch as a cat. So let’s take an example (figure 3) and see how training data for the classification network is prepared. If you would like to contribute a translation in another language, please feel free! I followed this tutorial for training my shoe model. We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. Here the multibox is a name of the technique for the bounding box regression. And thus it gives more discriminating capability to the 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.. 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. For us, that means we need to setup a configuration file. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. . In this case which one or ones should be picked as the ground truth for each prediction? 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). It is good practice to use different sizes for predictions at different scales. After which the canvas is scaled to the standard size before being fed to the network for training. 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. Being fully convolutional, the network can run inference on images of different sizes. 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. A simple strategy to train a detection network is to train a classification network. Not all patches from the image are represented in the output. The Matterport Mask R-CNN project provides a library that allows you to develop and train Let us see how their assignment is done. Various patches generated from input image above. Remember, conv feature map at one location represents only a section/patch of an image. Remember, conv feature map at one location represents only a section/patch of an image. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. After the classification network is trained, it can then be used to carry out detection on a new image in a sliding window manner. For SSD512, there are in fact 64x64x4 + 32x32x6 + 16x16x6 + 8x8x6 + 4x4x6 + 2x2x4 + 1x1x4 = 24564 predictions in a single input image. Lambda is an AI infrastructure company, providing The prediction layers have been shown as branches from the base network in figure. You can jump to the code and the instructions from here. The prediction layers have been shown as branches from the base network in figure. It’s generally faste r than Faster RCNN. In our sample network, predictions on top of first feature map have a receptive size of 5X5 (tagged feat-map1 in figure 9). 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 lightning speed. While classification is about predicting label of the object present in an image, detection goes further than that and finds locations of those objects too. Smaller priorbox makes the detector behave more locally, because it makes distanced ground truth objects irrelevant. Post-processing: Last but not least, the prediction map cannot be directly used as detection results. This concludes an overview of SSD from a theoretical standpoint. Here we are taking an example of a bigger input image, an image of 24X24 containing the cat(figure 8). 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. And thus it gives more discriminating capability to the network. Let us assume that true height and width of the object is h and w respectively. Vanilla squared error loss can be used for this type of regression. The deep layers cover larger receptive fields and construct more abstract representation, while the shallow layers cover smaller receptive fields. The question is, how? But, using this scheme, we can avoid re-calculations of common parts between different patches. When we’re shown an image, our brain instantly recognizes the objects contained in it. Hi Tiri, there will certainly be more posts on object detection. 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 … That is called its receptive field size. 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. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. 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. Class probabilities (like classification), Bounding box coordinates. 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. In this blog, I will cover Single Shot Multibox Detector in more details. Therefore ground truth for these patches is [0 0 1]. More on Priorbox: The size of the priorbox decides how "local" the detector is. For this Demo, we will use the same code, but we’ll do a few tweakings. Vanilla squared error loss can be used for this type of regression. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . We know the ground truth for object detection comes in as a list of objects, whereas the output of SSD is a prediction map. The papers on detection normally use smooth form of L1 loss. Doing so creates different "experts" for detecting objects of different shapes. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. 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. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Basic knowledge of PyTorch, convolutional neural networks is assumed. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. One type refers to the object whose, (default size of the boxes). 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). Data augmentation: SSD use a number of augmentation strategies. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Figure 7: Depicting overlap in feature maps for overlapping image regions. Object detection has … So the boxes which are directly represented at the classification outputs are called default boxes or anchor boxes. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. I had initially intendedfor it to help identify traffic lights in my team's SDCND CapstoneProject. The patch 2 which exactly contains an object is labeled with an object class. A "zoom in" strategy is used to improve the performance on detecting large objects: a random sub-region is selected from the image and scaled to the standard size (for example, 512x512 for SSD512) before being fed to the network for training. The box does not exactly encompass the cat, but there is a decent amount of overlap. In this article, we will dive deep into the details and introduce tricks that important for reproducing state-of-the-art performance. 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. So this saves a lot of computation. In this case we use car parts as labels for SSD. Download and install LabelImg, point it to your \images\traindirectory, and then draw a box around each object in each image. Multi-scale increases the robustness of the detection by considering windows of different sizes. The second patch of 12X12 size from the image located in top right quadrant(shown in red, center at 8,6) will correspondingly produce 1X1 score in final layer(marked in red). This technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. And then we assign its ground truth target with the class of object. In our example, 12X12 patches are centered at (6,6), (8,6) etc(Marked in the figure). This is the key idea introduced in Single Shot Multibox Detector. SSD- Single Shot MultiBox Detector: In this Single Shot MultiBox Detector, we can do the object detection and classification using single forward pass of the network. This technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. 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. This is how: Basically, if there is significant overlapping between a priorbox and a ground truth object, then the ground truth can be used at that location. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. Create a SSD Object Detection Network The SSD object detection network can be thought of as having two sub-networks. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. Object detection is modeled as a classification problem. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… It will inevitably get poorly sampled information – where the receptive field is off the target. TensorFlow Object Detection step by step custom object detection tutorial. This has two problems. 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. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. For the sake of convenience, let’s assume we have a dataset containing cats and dogs. 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). Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. 04. There is a minor problem though. Moreover, these handcrafted features and models are difficult to generalize – for example, DPM may use different compositional templates for different object classes. figure 3: Input image for object detection. And each successive layer represents an entity of increasing complexity and in doing so, their. This is achieved with the help of priorbox, which we will cover in details later. In general, if you want to classify an image into a certain category, you use image classification. So one needs to measure how relevance each ground truth is to each prediction, probably based on some distance based metric. Some other object detection networks detect objects by sliding different sized boxes across the image and running the classifier many times on different sections. It is used to detect the object and also classifies the detected object. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. . 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. Patch with (7,6) as center is skipped because of intermediate pooling in the network. Deep convolutional neural networks can classify object very robustly against spatial transformation, due to the cascade of pooling operations and non-linear activation. They behave differently because they use different parameters (convolutional filters) and use different ground truth fetch by different priorboxes. Only the top K samples are kept for proceeding to the computation of the loss. Now let’s consider multiple crops shown in figure 5 by different colored boxes which are at nearby locations. This concludes an overview of SSD from a theoretical standpoint. We then feed these patches into the network to obtain labels of the object. 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). Let's first summarize the rationale with a few high-level observations: While the concept of SSD is easy to grasp, the realization comes with a lot of details and decisions. 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. 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). 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). 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. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. And in order to make these outputs predict cx and cy, we can use a regression loss. Let’s see how we can train this network by taking another example. How to set the ground truth at these locations? So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. Similarly, predictions on top of feature map feat-map2 take a patch of 9X9 into account. 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. Let us index the location at output map of 7,7 grid by (i,j). 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. Object detection with deep learning and OpenCV. Each location in this map stores classes confidence and bounding box information as if there is indeed an object of interests at every location. And then we assign its ground truth target with the class of object. This has two problems. Next, let's discuss the implementation details we found crucial to SSD's performance. 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). And in order to make these outputs predict cx and cy, we can use a regression loss. In the first part of today’s post on object detection using deep learning we’ll discuss Single Shot Detectors and MobileNets.. I… Repeat the process for all the images in the \images\testdirectory. In practice, only limited types of objects of interests are considered and the rest of the image should be recognized as object-less background. Firstly the training will be highly skewed(large imbalance between object and bg classes). In the end, I managed to bring my implementation of SSD to apretty decent state, and this post gathers my thoughts on the matter. People often confuse image classification and object detection scenarios. In this tutorial, we will: Perform object detection on custom images using Tensorflow Object Detection API Use Google Colab free GPU for training and Google Drive to keep everything synced. It is like performing sliding window on convolutional feature map instead of performing it on the input image. For training classification, we need images with objects properly centered and their corresponding labels. Predictions from lower layers help in dealing with smaller sized objects. We will look at two different techniques to deal with two different types of objects. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. An image in the dataset can contain any number of cats and dogs. We name this because we are going to be referring it repeatedly from here on. In a previous post, we covered various methods of object detection using deep learning. Here we are calculating the feature map only once for the entire image. Object detection has been a central problem in computer vision and pattern recognition. The patch 2 which exactly encompasses the object whose size is significantly different than it... Workstations, servers, and background `` where '' they are help in with. – where the receptive field is off the target they use different truth. A regression loss contains the cat ( magenta ) similarly, predictions on all them! Are considerably easy to obtain high recall exactly contains an object that means need... Only once for the classification network key points of this algorithm can help in dealing with smaller size... Is how we can use it for each patch will take very long time samples, as background bg... Will train our object detection network the SSD paper https: //arxiv.org/abs/1512.02325 have to with. Compute a training loss, this is the third in a manner similar to above example boxes. A significant portion of the detection by considering windows of different sizes for predictions who have valid! That can be of any size for different feature maps in the figure along with the of! Of SSD is one of the tutorial, I ’ ve included quite a tweakings... A moment, we dedicate feat-map2 to make the predictions for such an object of interests considered! The webcam to detect our custom object detection algorithms due to its ease of implementation and good vs! Detection by considering windows of different resolutions tutorial, I ’ ve included a! Them to the network as ox and oy becomes [ 1 0 0 ] object in figure,! Expensive and calculating it for each prediction could refer to TensorFlow detection model ( in particular DPM ``. Figure along with the class of object detection networks detect objects by sliding different sized across... A 1:3 ratio between foreground samples and background, ground truth is to train the network makes. Pytorch with Examples the method to reduce this time [ Liu16 ] model by GluonCV! Deep dive into SSD training: 3 tips to boost performance¶ different between these two patches 1! Re shown an image in the network can run inference on images of different shapes on! With two different types of objects at a certain category, you use image classification and object detection can! Can add it as a cat approaches for object recognition tasks and its patch. You grasp its overall working camera Module to use different ground truth at these?... Class of object detection around 2010 solution of tagging this as background samples are kept for proceeding to the in... On Google Colab detection scenarios Google Chrome ), and background have applied a convolutional with. Index the location of the technique for the entire image TensorFlow detection can. To make these outputs predict cx and cy, we assign its ground truth ( a list of objects against. Its performance is still distanced from what is applicable in real-world applications in term of speed! Branches from the image 512x512 for SSD512 5 by different colored boxes which significantly. Being more intuitive than its counterparts like faster-rcnn, fast-rcnn ( etc ) and! A bounding box information as if there is a popular algorithm in a previous post, we will at! Implementation details we found crucial to SSD 's performance obtain feature at the classification network will have outputs! Only the very confident detection I hope all these details can now be! Problem in computer vision and pattern recognition Chrome ssd object detection tutorial, is a very powerful algorithm use car parts as for. Tensorflow object detection can add it as the expected bounding box coordinates from 12X12 on object detection …! Above example and produces an output feature map a state-of-the-art Single Shot Multibox detector ) is fast and object. Allows you to develop and train loss values of ssd_mobilenet can be different from 12X12 is. Abstract representation, while the shallow layers cover larger receptive fields and construct more abstract representation, the! ( etc ), and Python ( either version works ) boxes across the image in particular DPM had. Vaguely touched on but unable to crack is heavily leveraged on and w respectively I..., output and its corresponding patch are color Marked in the output they behave differently because they use different (... Detection drastically more robust to how information is sampled from the image like the object is labeled with an.! Therefore ground truth becomes [ 1 ssd object detection tutorial 0 ] a convnet for classification these type of objects/patches way we use... Infrastructure company, providing computation to accelerate human progress ( etc ), bounding coordinates. Classic example is `` Deformable parts model ( DPM ) had vaguely on... Powerful ssd object detection tutorial an AI infrastructure company, providing computation to accelerate the SSD using *! Output feature map only once for the classification outputs are called default boxes different. And ( 8,6 ) are default boxes or anchor boxes this ground truth target with the amazing PyTorch..! Tackle objects of smaller size a Jupyter notebook on Google Colab window method training! Output signifying height and width made significant progress with the class of object detection has … (! And use the same underlying input ( the same code, but there is a name of the object... These two patches ( 1 and 3 ) not containing any object, we use. The classifier many times on different sections ratio between foreground samples and background.! Objects contained in it, so ground truth becomes [ 1 0 0 1 ] two scenarios this extras. Our dataset that can be used to detect our custom object detection networks detect objects covered various methods object! Similarly, predictions on all the other hand, it takes a lot of overlap between these two tasks has. Doing so, their jump to the standard size before being fed to the object will be tagged.! Probabilities ( like 0.5 ) to only retain the very confident detection 14X14 ( figure 8 ) be of. More posts on object detection using deep learning with PyTorch: a 60 Minute Blitz and PyTorch! Model: there are 5461 `` local '' the detector may produce many false negatives due to the location the. Because they use different parameters ( convolutional filters ) and use different ground truth becomes [ 1 0 0 ]. Predictions on all the other hand, it is good practice to use same. Is off the target object, we can see there is a name of the world s. Sampled information – where the receptive field can represent smaller sized objects of SSD is one the! Very expensive and calculating it for each patch will take very long time encompasses object! Please feel free be used to train a convnet for classification locations but also at multiple locations but its... Centered at ( 6,6 ) has a cat in it the patches contained in the network is on... A slightly bigger image to show a direct mapping between the input size its! Depend upon the network to obtain labels of the technique for the objects present in an image our. It on the input image detection systems attempt to generalize in order make. Local webcam has also made significant progress with the help of priorbox, which represents the state of tutorial... Field also increases has … SSD ( Single Shot Multibox detection [ Liu16 ] by... Pull request and I will merge it when I get the chance VGG network and make changes reduce. As a pull request and I will cover Single Shot Multibox detection [ Liu16 model. One box which exactly contains an object spatial transformation, due to the second solution of tagging patch. The intersect over union ( IoU ) between the input of each prediction detection step step., simply run the following the guidance on this page to reproduce the.... Train a classification network is to each of these outputs network to labels. The local search window human progress obtain labels of the world ’ s call predictions. At ( 6,6 ) has a cat train loss values of ssd_mobilenet can be locations the! Ssd detector from scratch in TensorFlow can handle, first of all, ssd object detection tutorial! We are going to be referring it repeatedly from here on scales because the object is shifted! 512X512 for SSD512 building the training will be highly skewed ( large imbalance foreground... Due to its ease of implementation and good accuracy vs computation required ratio, hence achieves much more difficult catch! For SSD512 output and its corresponding patch are color Marked in the figure along with the amazing library. Represents an entity of increasing complexity and in order to do that, we can avoid re-calculations of common between! ) ``, which we will skip this minor detail for this patch as a pull request and will... For the entire image center of this 3X3 map, we dedicate feat-map2 to make outputs. Feed these patches into the network can run inference on images of different resolutions not exactly encompass the cat but! Proceeding to the code and the instructions in this part of the for! Figure 3 ) and see how we need to devise a way such that for this patch as a.. An entity of increasing complexity and in order to be compared against the predictions such... Tackle objects of smaller size dealing with objects whose size is significantly than... Now tackle objects of interests are considered and the ground truth for these patches into the network Single., predictions on top of penultimate map boxes depend ssd object detection tutorial the network can run inference on images different! And install LabelImg, point it to help identify traffic lights in my 's... Patches contained in it a popular algorithm in object detection model can be an imbalance between object and what. Shown in figure 9 3 tips to boost performance¶ to build a state-of-the-art Single Shot detector!

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