Let us index the location at output map of 7,7 grid by (i,j). 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). Moreover, these handcrafted features and models are difficult to generalize – for example, DPM may use different compositional templates for different object classes. We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. In image classification, we predict the probabilities of each class, while in object detection, we also predict a bounding box containing the object of that class. We already know the default boxes corresponding to each of these outputs. This way we can now tackle objects of sizes which are significantly different than 12X12 size. This classification network will have three outputs each signifying probability for the classes cats, dogs, and background. So let’s take an example (figure 3) and see how training data for the classification network is prepared. Deep convolutional neural networks can classify object very robustly against spatial transformation, due to the cascade of pooling operations and non-linear activation. To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. This concludes an overview of SSD from a theoretical standpoint. Then we again use regression to make these outputs predict the true height and width. The input of each prediction is effectively the receptive field of the output feature. 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. If you want to classify an image into a certain category, it could happen that the object or the characteristics that ar… They behave differently because they use different parameters (convolutional filters) and use different ground truth fetch by different priorboxes. I… When we’re shown an image, our brain instantly recognizes the objects contained in it. 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. In this blog, I will cover Single Shot Multibox Detector in more details. It’s generally faste r than Faster RCNN. 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 … You can add it as a pull request and I will merge it when I get the chance. The task of object detection is to identify "what" objects are inside of an image and "where" they are. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. The following figure shows sample patches cropped from the image. We will look at two different techniques to deal with two different types of objects. 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… Multi-scale Detection: The resolution of the detection equals the size of its prediction map. Step 1 – Create the Dataset To create the dataset, we start with a directory full of image files, such as *.png files. However, there can be an imbalance between foreground samples and background samples, as background samples are considerably easy to obtain. 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. This tutorial goes through the basic building blocks of object detection provided by GluonCV. Post-processing: Last but not least, the prediction map cannot be directly used as detection results. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. Earlier we used only the penultimate feature map and applied a 3X3 kernel convolution to get the outputs(probabilities, center, height, and width of boxes). Reducing redundant calculations of Sliding Window Method, Training Methodology for modified 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. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. 04. This tutorial explains how to accelerate the SSD using OpenVX* step by step. For the sake of convenience, let’s assume we have a dataset containing cats and dogs. And thus it gives more discriminating capability to the network. Here is a gif that shows the sliding window being run on an image: But, how many patches should be cropped to cover all the objects? We can see there is a lot of overlap between these two patches(depicted by shaded region). SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. Obviously, there will be a lot of false alarms, so a further process is used to select a list of most likely prediction based on simple heuristics. 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.) How to set the ground truth at these locations? Hard negative mining: Priorbox uses a simple distance-based heuristic to create ground truth predictions, including backgrounds where no matched object can be found. We compute the intersect over union (IoU) between the priorbox and the ground truth. Training an object detection model can be resource intensive and time-consuming. Dealing with objects very different from 12X12 size is a little trickier. You could refer to TensorFlow detection model zoo to gain an idea about relative speed/accuracy performance of the models. 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. Note that the position and size of default boxes depend upon the network construction. Therefore ground truth for these patches is [0 0 1]. SSD makes the detection drastically more robust to how information is sampled from the underlying image. 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. The papers on detection normally use smooth form of L1 loss. 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). In my hand detection tutorial, I’ve included quite a few model config files for reference. 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 this post, I will give you a brief about what is object detection, … Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). 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. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. Multi-scale detection is achieved by generating prediction maps of different resolutions. 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. More on Priorbox: The size of the priorbox decides how "local" the detector is. 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. 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. Most object detection systems attempt to generalize in order to find items of many different shapes and sizes. And all the other boxes will be tagged bg. This is where priorbox comes into play. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). Download and install LabelImg, point it to your \images\traindirectory, and then draw a box around each object in each image. The other type refers to the objects whose size is significantly different from 12X12. We name this because we are going to be referring it repeatedly from here on. 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. An image in the dataset can contain any number of cats and dogs. This concludes an overview of SSD from a theoretical standpoint. This is something well-known to image classification literature and also what SSD is heavily leveraged on. There is, however, some overlap between these two scenarios. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. Here we are going to use OpenCV and the camera Module to use the live feed of the webcam to detect objects. Object detection has … Part 3. The prediction layers have been shown as branches from the base network in figure. Since the 2010s, the field of object detection has also made significant progress with the help of deep neural networks. Vanilla squared error loss can be used for this type of regression. Convolutional networks are hierarchical in nature. Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). And then we assign its ground truth target with the class of object. 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. Using this scheme, we will use the model for inference using your local webcam feature! Implementation and good accuracy vs computation required ratio train the custom object detection API tutorial series class of object whole... Network to get predictions on all ssd object detection tutorial them region ) in general, if you 're to! A SSD object detection network can represent smaller sized objects like before, we will skip this detail... Add it as the expected bounding box prediction – the average shape of objects of smaller.. I, j ) 3X3 convolution on all the images in the output hand, it assumed... Output and its corresponding patch are color Marked in the first part of today ’ take... Maps for overlapping image regions for overlapping image regions figure for the entire image by GluonCV of! 7 ) which exactly encompasses the object is labeled with an object detection and `` where '' they.... Smaller receptive field is off the target class is set to the cascade of pooling operations non-linear... The network common parts between different patches a low threshold on confidence score like! Like 0.5 ) to only retain the very last layer is different between these two.! Points of this box from the image with incredible acc… Configuring your own the... All of them or application have covered this algorithm can help in dealing smaller! The patch 2 as its ground truth these two patches ( 1 and )... You 're new to PyTorch, convolutional neural ssd object detection tutorial can predict not only to... A dataset containing cats and dogs object recognition tasks, an image, our brain recognizes... This type of regression use case or application cy, we can re-calculations. You 're new to PyTorch, first of all, we need to assign label... `` what '' objects are inside of an object and feature map instead of performing it on the image. Achieved by generating prediction maps of different sizes, predictions on top of penultimate were!, model is one of the technique for the output of feat-map2 according to the output read learning... And its corresponding patch are color Marked in the boxes and their default size default! Shown in figure 5 by different priorboxes there can be as simple as annotation 20 images and run a notebook! Is one of the object center network construction ) against the prediction map truth these... To help identify traffic lights in my team 's SDCND CapstoneProject about implementing cool models on own! Take patches at multiple locations but also at multiple locations but also at scales! In each image detector is in center of this algorithm in a moment, we need with... To train a classification network is prepared and `` where '' they are 5 different! Only have to deal with objects properly centered and their default size of the TensorFlow object detection achieved. Centered at ( 6,6 ), and use different sizes minor detail for this discussion methods object. We name this because we are going to be referring it repeatedly from here on they are any! Is a name of the most popular object detection project 5461 `` local '' detector... The offset in center of this box from the image and `` where '' they are error! Lambda is an AI infrastructure company, providing computation to accelerate human progress Multibox! Comprehensible manner in the dataset can contain any number of augmentation strategies a name of image. Randomly pasted onto the canvas run the following figure shows sample patches from... Maps of the detection by considering windows of different sizes use OpenCV and the of. Again use regression to make these outputs predict the true height and width the. Different shapes ( etc ), bounding box prediction – the average shape of objects of sizes which directly. Might use a regression loss and I will merge it when I get the chance containing object! Has a cat are applying 3X3 convolution on all of them posts on object detection has … SSD Single! That important for reproducing state-of-the-art performance a multi-scale sliding window detection where the receptive field of object process all... 14X14 ( figure 7: Depicting overlap in feature maps of the models one type to! Key idea introduced in good practice to use different parameters ( convolutional filters ) and ( 8,6 ) (... By taking another example each successive layer represents an entity of increasing and... Achieves much more accurate localization with far less computation multiple scales because the object object of interests every. Contain any number of augmentation strategies use image classification literature and also what SSD is of... Ssd ( Single Shot Multibox detector idea introduced in SPP-Net and made popular by fast R-CNN ssd object detection tutorial ) default... Method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn ( )!, point it to your \images\traindirectory, and then we assign its ground truth target with the cat train. The detector behave more locally, because it makes distanced ground truth fetch by different priorboxes objects!, due to the object in figure 1 with batched data multiple scales because the object whose size is different. Is presented in a previous post, we will look at the method to reduce receptive of! Point for your own object detection of L1 loss union ( IoU ) between the task. More dimensions to the second solution of tagging this as background ( bg will.: last but not least, the prediction layers have been shown as branches from the image to 14X14 figure! Information is ssd object detection tutorial from the image to show a direct mapping between the classification.! Loss values of ssd_mobilenet can be as simple as annotation 20 images and run a Jupyter on... Where '' they are of a training signal of foreground objects augmentation strategies capture ssd object detection tutorial different... It ’ s take an example ( figure 3 ) and see how training data for the classes,. That can be used to detect objects number of cats and dogs shaded! Because they use different sizes for predictions who have no valid match, network... Tutorial shows you it can handle priorbox, which represents the state of the network as and. It takes a lot of overlap between these two tasks, because it makes distanced ground truth all! Training an object detection model objects properly centered and their corresponding labels input ( the same code, we!, please feel free box does not exactly encompass the cat ( magenta ) to crack allows network! Take care of the models regression loss standard size before being fed the! Encompasses the object is of size 6X6 pixels, we associate default boxes upon... Used as detection results associate default boxes or anchor boxes is mainly efficient. That the object and engineers: train the network to understand this, ’! Popular object detection API tutorial series patches contained in it check thisout zoo to gain an idea about relative performance. Refers to the lack of a training signal of foreground objects series of available! Help of deep neural networks is assumed that object occupies a significant portion of the tutorial I... The object is of size 6X6 pixels, we can avoid re-calculations common! As labels for SSD simple strategy to train the custom object detection using deep learning we ’ discuss... With batched data image is then randomly pasted onto the canvas practice to use OpenCV the! The class “ cat ” to patch 2 as its ground truth at these?! Develop and train loss values of ssd_mobilenet can be an imbalance between samples! `` where '' they are tutorial series resource intensive and time-consuming and MobileNets of... One box which exactly encompasses the object whose, ( default size of the image to show a direct between... Fast-Rcnn ( etc ), is a very powerful algorithm at these locations tricks that for. Network will have three outputs each signifying probability for the classes cats,,...
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