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. There can be locations in the image that contains no objects. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. This way we can now tackle objects of sizes which are significantly different than 12X12 size. And each successive layer represents an entity of increasing complexity and in doing so, their. Deep dive into SSD training: 3 tips to boost performance¶. For example, SSD512 outputs seven prediction maps of resolutions 64x64, 32x32, 16x16, 8x8, 4x4, 2x2, and 1x1 respectively. The fixed size constraint is mainly for efficient training with batched data. We can see there is a lot of overlap between these two patches(depicted by shaded region). But in this solution, we need to take care of the offset in center of this box from the object center. 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 significantly reduced the computation cost and allows the network to learn features that also generalize better. 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. After the classification network is trained, it can then be used to carry out detection on a new image in a sliding window manner. There can be multiple objects in the image. Therefore ground truth for these patches is [0 0 1]. We repeat this process with smaller window size in order to be able to capture objects of smaller size. 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. So predictions on top of penultimate layer in our network have maximum receptive field size(12X12) and therefore it can take care of larger 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. The patches for other outputs only partially contains the cat. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. In essence, SSD is a multi-scale sliding window detector that leverages deep CNNs for both these tasks. The Matterport Mask R-CNN project provides a library that allows you to develop and train So this saves a lot of computation. You can add it as a pull request and I will merge it when I get the chance. Hard negative mining: Priorbox uses a simple distance-based heuristic to create ground truth predictions, including backgrounds where no matched object can be found. So predictions on top of penultimate layer in our network have maximum receptive field size(12X12) and therefore it can take care of larger sized objects. I had initially intendedfor it to help identify traffic lights in my team's SDCND CapstoneProject. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. An image in the dataset can contain any number of cats and dogs. First, we take a window of a certain size(blue box) and run it over the image(shown in Figure below) at various locations. On the other hand, it takes a lot of time and training data for a machine to identify these objects. So for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. 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. In essence, SSD does sliding window detection where the receptive field acts as the local search window. 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. Create a SSD Object Detection Network The SSD object detection network can be thought of as having two sub-networks. We denote these by. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. 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). We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. 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. Convolutional networks are hierarchical in nature. 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). We then feed these patches into the network to obtain labels of the 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. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. And all the other boxes will be tagged bg. 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. SSD makes the detection drastically more robust to how information is sampled from the underlying image. 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. It will inevitably get poorly sampled information – where the receptive field is off the target. But in this solution, we need to take care of the offset in center of this box from the object center. The papers on detection normally use smooth form of L1 loss. Deep convolutional neural networks can predict not only an object's class but also its precise location. Hi Tiri, there will certainly be more posts on object detection. TensorFlow Object Detection step by step custom object detection tutorial. The system is able to identify different objects in the image with incredible acc… So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… In this case which one or ones should be picked as the ground truth for each prediction? Three sets of this 3X3 filters are used here to obtain 3 class probabilities(for three classes) arranged in 1X1 feature map at the end of the network. Multi-scale increases the robustness of the detection by considering windows of different sizes. For this Demo, we will use the same code, but we’ll do a few tweakings. Let’s see how we can train this network by taking another example. After which the canvas is scaled to the standard size before being fed to the network for training. Since the 2010s, the field of object detection has also made significant progress with the help of deep neural networks. We will skip this minor detail for this discussion. And with MobileNet-SSD inference, we can use it for any kind of object detection use case or application. In practice, only limited types of objects of interests are considered and the rest of the image should be recognized as object-less background. Some other object detection networks detect objects by sliding different sized boxes across the image and running the classifier many times on different sections. Being fully convolutional, the network can run inference on images of different sizes. And shallower layers bearing smaller receptive field can represent smaller sized objects. We will look at two different techniques to deal with two different types of objects. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. 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.) Training an object detection model can be resource intensive and time-consuming. It is used to detect the object and also classifies the detected object. 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). When we’re shown an image, our brain instantly recognizes the objects contained in it. Follow the instructions in this document to reproduce the results. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. Figure 7: Depicting overlap in feature maps for overlapping image regions. Welcome to part 5 of the TensorFlow Object Detection API tutorial series. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. Here we are calculating the feature map only once for the entire image. Secondly, if the object does not fit into any box, then it will mean there won’t be any box tagged with the object. This is where priorbox comes into play. SSD (Single Shot MultiBox Detector) is a popular algorithm in object detection. In practice, SSD uses a few different types of priorbox, each with a different scale or aspect ratio, in a single layer. Then we again use regression to make these outputs predict the true height and width. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. 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. 8 Developing SSD-Object Detection Models for Android Using TensorFlow 7. Let’s increase the image to 14X14(figure 7). Lambda provides GPU workstations, servers, and cloud So let’s take an example (figure 3) and see how training data for the classification network is prepared. figure 3: Input image for object detection. In this tutorial we demonstrate one of the landmark modern object detectors – the "Single Shot Detector (SSD)" invented by Wei Liu et al. There is, however, a few modifications on the VGG_16: parameters are subsampled from fc6 and fc7, dilation of 6 is applied on fc6 for a larger receptive field. This basically means we can tackle an object of a very different size by using features from the layer whose receptive field size is similar. 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. computation to accelerate human progress. This has two problems. 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. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. 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. 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). This is the third in a series of tutorials I'm writing about implementing cool models on your own with the amazing PyTorch library.. In consequence, the detector may produce many false negatives due to the lack of a training signal of foreground objects. 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. The detection is now free from prescripted shapes, hence achieves much more accurate localization with far less computation. Object Detection using Hog Features: In a groundbreaking paper in the history of computer vision, … Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). 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. It is notintended to be a tutorial. 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. Let's first remind ourselves about the two main tasks in object detection: identify what objects in the image (classification) and where they are (localization). You can jump to the code and the instructions from here. Dealing with objects very different from 12X12 size is a little trickier. Let’s call the predictions made by the network as ox and oy. researchers and engineers. 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 technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . More on Priorbox: The size of the priorbox decides how "local" the detector is. Only the top K samples are kept for proceeding to the computation of the loss. 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. In the previous tutorial 04. This is the. 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. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. In this part of the tutorial, we will train our object detection model to detect our custom object. Remember, conv feature map at one location represents only a section/patch of an image. Object detection is modeled as a classification problem. The box does not exactly encompass the cat, but there is a decent amount of overlap. In this blog, I will cover Single Shot Multibox Detector in more details. . 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). Let’s call the predictions made by the network as ox and oy. Now, we shall take a slightly bigger image to show a direct mapping between the input image and feature map. 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. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. In my hand detection tutorial, I’ve included quite a few model config files for reference. We repeat this process with smaller window size in order to be able to capture objects of smaller size. The following figure shows sample patches cropped from the image. A typical CNN network gradually shrinks the feature map size and increase the depth as it goes to the deeper layers. In the end, I managed to bring my implementation of SSD to apretty decent state, and this post gathers my thoughts on the matter. So one needs to measure how relevance each ground truth is to each prediction, probably based on some distance based metric. Class probabilities (like classification), Bounding box coordinates. For more complete information about compiler optimizations, see our Optimization Notice. There is a minor problem though. To compute mAP, one may use a low threshold on confidence score (like 0.01) to obtain high recall. The extent of that patch is shown in the figure along with the cat(magenta). Not all patches from the image are represented in the output. For more information of receptive field, check thisout. It has been explained graphically in the figure. 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. I followed this tutorial for training my shoe model. In general, if you want to classify an image into a certain category, you use image classification. 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. We need to devise a way such that for this patch, the. This is achieved with the help of priorbox, which we will cover in details later. 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. Object detection presents several other challenges in addition to concerns about speed versus accuracy. A classic example is "Deformable Parts Model (DPM) ", which represents the state of the art object detection around 2010. The question is, how? It’s generally faste r than Faster RCNN. Basic knowledge of PyTorch, convolutional neural networks is assumed. In the future, we will look into deploying the trained model in different hardware and … This classification network will have three outputs each signifying probability for the classes cats, dogs, and background. We put one priorbox at each location in the prediction map. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. Since the patches at locations (0,0), (0,1), (1,0) etc do not have any object in it, their ground truth assignment is [0 0 1]. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. We name this because we are going to be referring it repeatedly from here on. 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. 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. which can thus be used to find true coordinates of an object. Here we are calculating the feature map only once for the entire image. So we resort to the second solution of tagging this patch as a cat. Object detection has been a central problem in computer vision and pattern recognition. It can easily be calculated using simple calculations. Input and Output: The input of SSD is an image of fixed size, for example, 512x512 for SSD512. In this article, we will dive deep into the details and introduce tricks that important for reproducing state-of-the-art performance. . SSD uses some simple heuristics to filter out most of the predictions: It first discards weak detection with a threshold on confidence score, then performs a per-class non-maximum suppression, and curates results from all classes before selecting the top 200 detections as the final output. 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). 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. The papers on detection normally use smooth form of L1 loss. Similarly, predictions on top of feature map feat-map2 take a patch of 9X9 into account. I hope all these details can now easily be understood from referring the paper. 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. Once you have TensorFlow with GPU support, simply run the following the guidance on this page to reproduce the results. Train SSD on Pascal VOC dataset, we briefly went through the basic APIs that help building the training pipeline of SSD.. The output of SSD is a prediction map. Train SSD on Pascal VOC dataset¶. Configuring your own object detection model. From EdjeElectronics' TensorFlow Object Detection Tutorial: For my training on the Faster-RCNN-Inception-V2 model, it started at about 3.0 and quickly dropped below 0.8. Let us understand this in detail. 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. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. . 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. 05. We need to devise a way such that for this patch, the network can also predict these offsets which can thus be used to find true coordinates of an object. This is a PyTorch Tutorial to Object Detection.. To train the network, one needs to compare the ground truth (a list of objects) against the prediction map. How is it so? On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Remember, conv feature map at one location represents only a section/patch of an image. Nonetheless, thanks to deep features, this doesn't break SSD's classification performance – a dog is still a dog, even when SSD only sees part of it! People often confuse image classification and object detection scenarios. By utilising this information, we can use shallow layers to predict small objects and deeper layers to predict big objects, as smal… This is also a good starting point for your own object detection project. If output probabilities are in the order cat, dog, and background, ground truth becomes [1 0 0]. Part 3. You can think it as the expected bounding box prediction – the average shape of objects at a certain scale. Each location in this map stores classes confidence and bounding box information as if there is indeed an object of interests at every location. 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. This tutorial shows you it can be as simple as annotation 20 images and run a Jupyter notebook on Google Colab. Detailed steps to tune, train, monitor, and use the model for inference using your local webcam. Doing so creates different "experts" for detecting objects of different shapes. For predictions who have no valid match, the target class is set to the. The only difference is: I use ssdlite_mobilenet_v2_coco.config and ssdlite_mobilenet_v2_coco pretrained model as reference instead of ssd_mobilenet_v1_pets.config and ssd_mobilenet_v1_coco.. And you are free to choose your own … This creates extras examples of small objects and is crucial to SSD's performance on MSCOCO. You could refer to TensorFlow detection model zoo to gain an idea about relative speed/accuracy performance of the models. 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. A feature extraction network, followed by a detection network. This is something well-known to image classification literature and also what SSD is heavily leveraged on. So, the output of the network should be: Class probabilities should also include one additional label representing background because a lot of locations in the image do not correspond to any object. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. And then we assign its ground truth target with the class of object. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). My hope is that this tutorial has provided an understanding of how we can use the OpenCV DNN module for object detection.

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