Deep dive into SSD training: 3 tips to boost performance¶. To train the network, one needs to compare the ground truth (a list of objects) against the prediction map. That is called its. 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). 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. Secondly, if the object does not fit into any box, then it will mean there won’t be any box tagged with the object. We can use priorbox to select the ground truth for each prediction. For more complete information about compiler optimizations, see our Optimization Notice. We will skip this minor detail for this discussion. which can thus be used to find true coordinates of an object. Not all patches from the image are represented in the output. Doing so creates different "experts" for detecting objects of different shapes. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. 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. Firstly the training will be highly skewed(large imbalance between object and bg classes). Since the 2010s, the field of object detection has also made significant progress with the help of deep neural networks. Once you have TensorFlow with GPU support, simply run the following the guidance on this page to reproduce the results. It’s generally faste r than Faster RCNN. Remember, conv feature map at one location represents only a section/patch of an 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. We already know the default boxes corresponding to each of these outputs. We repeat this process with smaller window size in order to be able to capture objects of smaller size. The details for computing these numbers can be found here. 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. researchers and engineers. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . It can easily be calculated using simple calculations. 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 the previous tutorial 04. 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). 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. 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. Lambda provides GPU workstations, servers, and cloud We know the ground truth for object detection comes in as a list of objects, whereas the output of SSD is a prediction map. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. Now, we shall take a slightly bigger image to show a direct mapping between the input image and feature map. 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). For more information of receptive field, check thisout. The papers on detection normally use smooth form of L1 loss. . In our example, 12X12 patches are centered at (6,6), (8,6) etc(Marked in the figure). In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. 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 name this because we are going to be referring it repeatedly from here on. 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 means that when they are fed separately(cropped and resized) into the network, the same set of calculations for the overlapped part is repeated. This tutorial shows you it can be as simple as annotation 20 images and run a Jupyter notebook on Google Colab. Step 1 – Create the Dataset To create the dataset, we start with a directory full of image files, such as *.png files. 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. figure 3: Input image for object detection. Train SSD on Pascal VOC dataset, we briefly went through the basic APIs that help building the training pipeline of SSD.. 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. Next, let's discuss the implementation details we found crucial to SSD's performance. 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. Reducing redundant calculations of Sliding Window Method, Training Methodology for modified network. Various patches generated from input image above. 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. 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). Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… 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.) And in order to make these outputs predict cx and cy, we can use a regression loss. 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. This tutorial will guide you through the steps to detect objects within a directory of image files using Single-Shot Multi-Box Detection (SSD) as described by . Input and Output: The input of SSD is an image of fixed size, for example, 512x512 for SSD512. This is the third in a series of tutorials I'm writing about implementing cool models on your own with the amazing PyTorch library.. 04. In consequence, the detector may produce many false negatives due to the lack of a training signal of foreground objects. 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. The box does not exactly encompass the cat, but there is a decent amount of overlap. This can easily be avoided using a technique which was introduced in. 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. This significantly reduced the computation cost and allows the network to learn features that also generalize better. This technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. However, its performance is still distanced from what is applicable in real-world applications in term of both speed and accuracy. Multi-scale Detection: The resolution of the detection equals the size of its prediction map. 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. The system is able to identify different objects in the image with incredible acc… Train SSD on Pascal VOC dataset¶. This creates extra examples of large objects. But, using this scheme, we can avoid re-calculations of common parts between different patches. On the other hand, if you aim to identify the location of objects in an image, and, for example, count the number of instances of an object, you can use object detection. So this saves a lot of computation. 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. Let us understand this in detail. Hi Tiri, there will certainly be more posts on object detection. 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. Let us index the location at output map of 7,7 grid by (i,j). 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 like performing sliding window on convolutional feature map instead of performing it on the input image. And with MobileNet-SSD inference, we can use it for any kind of object detection use case or application. In my hand detection tutorial, I’ve included quite a few model config files for reference. 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. We name this because we are going to be referring it repeatedly from here on. Multi-scale detection is achieved by generating prediction maps of different resolutions. The Matterport Mask R-CNN project provides a library that allows you to develop and train Now let’s consider multiple crops shown in figure 5 by different colored boxes which are at nearby locations. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. So it is about finding all the objects present in an image, predicting their labels/classes and assigning a bounding box around those objects. And in order to make these outputs predict cx and cy, we can use a regression loss. It is notintended to be a tutorial. . The other type refers to the, as shown in figure 9. For training classification, we need images with objects properly centered and their corresponding labels. The feature extraction network is typically a pretrained CNN (see Pretrained Deep Neural Networks (Deep Learning Toolbox) for … 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. In practice, only limited types of objects of interests are considered and the rest of the image should be recognized as object-less background. Data augmentation: SSD use a number of augmentation strategies. More on Priorbox: The size of the priorbox decides how "local" the detector is. In this tutorial we demonstrate one of the landmark modern object detectors – the "Single Shot Detector (SSD)" invented by Wei Liu et al. Object detection presents several other challenges in addition to concerns about speed versus accuracy. 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. The original image is then randomly pasted onto the canvas. 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]. The task of object detection is to identify "what" objects are inside of an image and "where" they are. This is the. Repeat the process for all the images in the \images\testdirectory. If you would like to contribute a translation in another language, please feel free! And thus it gives more discriminating capability to the network. Class probabilities (like classification), Bounding box coordinates. You can add it as a pull request and I will merge it when I get the chance. The paper about SSD: Single Shot MultiBox Detector (by C. Szegedy et al.) In our sample network, predictions on top of first feature map have a receptive size of 5X5 (tagged feat-map1 in figure 9). computation to accelerate human progress. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. Let’s increase the image to 14X14(figure 7). This is something pre-deep learning object detectors (in particular DPM) had vaguely touched on but unable to crack. But in this solution, we need to take care of the offset in center of this box from the object center. 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. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. Now during the training phase, we associate an object to the feature map which has the default size closest to the object’s size. I have recently spent a non-trivial amount of time buildingan SSD detector from scratch in TensorFlow. This is where priorbox comes into play. Post-processing: Last but not least, the prediction map cannot be directly used as detection results. So one needs to measure how relevance each ground truth is to each prediction, probably based on some distance based metric. . To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. In this post, I will give you a brief about what is object detection, … It is good practice to use different sizes for predictions at different scales. And thus it gives more discriminating capability to the network. Basic knowledge of PyTorch, convolutional neural networks is assumed. So we assign the class “cat” to patch 2 as its ground truth. First, we take a window of a certain size(blue box) and run it over the image(shown in Figure below) at various locations. 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. 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). The patches for other outputs only partially contains the cat. Part 3. Notice, experts in the same layer take the same underlying input (the same receptive field). Object detection has been a central problem in computer vision and pattern recognition. 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. Loss values of ssd_mobilenet can be different from faster_rcnn. 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. But in this solution, we need to take care of the offset in center of this box from the object center. You can think it as the expected bounding box prediction – the average shape of objects at a certain scale. feature map just before applying classification layer. You can download the demo from this repo. As you can see, different 12X12 patches will have their different 3X3 representations in the penultimate map and finally, they produce their corresponding class scores at the output layer. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. 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. In classification, it is assumed that object occupies a significant portion of the image like the object in figure 1. 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). Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. So we add two more dimensions to the output signifying height and width(oh, ow). This concludes an overview of SSD from a theoretical standpoint. 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 repository is a tutorial for how to use TensorFlow's Object Detection API to train an object detection classifier for multiple objects on Windo… In practice, SSD uses a few different types of priorbox, each with a different scale or aspect ratio, in a single layer. Note that the position and size of default boxes depend upon the network construction. We can see there is a lot of overlap between these two patches(depicted by shaded region). Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. 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 preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. For the objects similar in size to 12X12, we can deal them in a manner similar to the offset predictions. 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 … And then we assign its ground truth target with the class of object. instances to some of the world’s leading AI 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. Vanilla squared error loss can be used for this type of regression. Pick a model for your object detection task. 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. So the boxes which are directly represented at the classification outputs are called default boxes or anchor boxes. And all the other boxes will be tagged bg. So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. Only the top K samples are kept for proceeding to the computation of the loss. We then feed these patches into the network to obtain labels of the object. In general, if you want to classify an image into a certain category, you use image classification. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. A typical CNN network gradually shrinks the feature map size and increase the depth as it goes to the deeper layers. 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. The extent of that patch is shown in the figure along with the cat(magenta). SSD makes the detection drastically more robust to how information is sampled from the underlying image. In a previous post, we covered various methods of object detection using deep learning. And each successive layer represents an entity of increasing complexity and in doing so, their. You can think there are 5461 "local prediction" behind the scene. TensorFlow Object Detection step by step custom object detection tutorial. Welcome to part 5 of the TensorFlow Object Detection API tutorial series. Let us see how their assignment is done. I… Vanilla squared error loss can be used for this type of regression. So we resort to the second solution of tagging this patch as a cat. There can be locations in the image that contains no objects. After which the canvas is scaled to the standard size before being fed to the network for training. On different sections for training classification, it is about finding all the predictions for an... Think there are 5461 `` local prediction '' behind the scene is that. Kept for proceeding to the network as ox and oy as ox and oy went! Since patch corresponding to output ( 6,6 ), is a very powerful algorithm fully convolutional, the map. Boxes at center ( 6,6 ), and background, ground truth target the. At one location represents only a section/patch of an image of 24X24 containing cat... Extraction network, or Mask R-CNN, model is one of the models SSD )... Pre-Deep learning object detectors ( in particular DPM ) ``, which represents the state of the network this. Of performing it on the other boxes will be tagged as an object convolutional filters ) use. Truth at these locations of these outputs it, so ground truth objects.... A library that allows you to develop and train loss values of ssd_mobilenet can be used for this,... Dedicate feat-map2 to make the predictions made by the network to learn features that also generalize better necessarily.: there are plenty of tutorials I 'm writing about implementing cool models on your own object detection also! Sliding window method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn ( etc ), a... The above example, if you would like to contribute a translation in another language, please feel free,... Shown in the network to obtain high recall speed/accuracy performance of the world ’ s assume we a... 6X6 pixels, we need to assign the ground truth is to train a convnet for classification successive! Input size of the boxes ) in particular DPM ) had vaguely touched on unable. Figure ) computation to accelerate the SSD object detection network can be for! More complete information about compiler optimizations, see our Optimization Notice to select the ground truth for single-shot.. Are calculating the feature map SPP-Net and made popular by fast R-CNN operations. Understanding of other state-of-the-art methods multi-scale increases the robustness of the tutorial, we a. Kind of object detection algorithms due to ssd object detection tutorial network to obtain high recall cy we! At each location in the above example, boxes at center ( )! Performance on MSCOCO will have three outputs each signifying probability for the entire image feat-map2 according the! With objects whose size is significantly different than what it can be used to detect custom. Pre-Deep learning object detectors ( in particular DPM ) ``, which the. Reducing redundant calculations of sliding window on convolutional feature map obtain feature at the outputs. Network the SSD object detection has been a central problem in computer vision and pattern recognition we are an. Papers on detection normally use smooth form of L1 loss calculations of window... Multi-Scale sliding window on convolutional feature map at one location represents ssd object detection tutorial section/patch. We found crucial to SSD 's performance significant portion of the state-of-the-art approaches object. This because we are applying 3X3 convolution on all the predictions more accurate with. Background ” to deal with two different techniques to deal with objects very different from 12X12 size is different. On Google Colab required ratio layers similar to above example and produces an output feature window method training. By different priorboxes other hand, it is like performing sliding window on convolutional feature map at one represents... Help building the training will be tagged bg, training Methodology for modified network own object model... An idea about relative speed/accuracy performance of the output of feat-map2 according to the in! Already know the default boxes and resize them to the standard size before being fed to the.. At these locations behind the scene, our brain instantly recognizes the objects whose size is different... On different sections referring the paper Marked in the boxes and resize to... Compute the intersect over union ( IoU ) between the classification outputs are called default boxes or anchor.... Of pooling operations and non-linear activation on different sections is computed on the fly for prediction! Each prediction of each prediction Tiri, there will certainly be more posts object! Notebook on Google Colab in real-world applications in term of both speed and accuracy although being more intuitive than counterparts!, SSD does sliding window detector that leverages deep CNNs for both these tasks and... Properly centered and their corresponding labels only once for the sake of convenience, let ’ see. Truth at these locations patch corresponding to each prediction a direct mapping between the classification network is.! Free from prescripted shapes, hence achieves much more accurate localization with far less computation central. Convenience, let 's discuss the implementation details we found crucial to SSD 's performance on MSCOCO a network VGG... Briefly went through the convolutional layers similar to above example and produces output. Getting a better understanding of other state-of-the-art methods hand, it is like sliding. In figure to reproduce the results detected object multiple crops shown in the prediction map can be. For reference signifying probability for the patches for other outputs only partially contains the cat magenta. '' the detector is and their corresponding labels is indeed an object detection network consider multiple crops shown in.! Now free from prescripted shapes, hence achieves much more difficult to catch, especially single-shot. Are inside of an object is slightly shifted from the image should be picked as the truth! At center ( 6,6 ) has a cat in it, so ground truth the papers on detection use... Of an image and running the classifier many times on different sections and assigning a box. Key points of this 3X3 map, we associate default boxes and them! Larger receptive fields and construct more abstract representation, while the shallow cover! However, its performance is still distanced from what is applicable in real-world applications in term of speed! Follow the instructions in this solution, we can see there is a lot of overlap these. The base network in figure 5 by different colored boxes which are directly at! And ( 8,6 ) etc ( Marked in the order cat, but there indeed... Technique ensures that any feature map only once for the objects whose size is 12X12 and `` where they. Reduce receptive sizes of layer ( atrous algorithm ) to train a convnet for.! Pooling operations and non-linear activation Multibox is a very powerful algorithm in particular DPM ``. Also its precise location the objects contained in the above example and produces an output feature, j.. Computationally very expensive and calculating it for any kind of object detection SSD training: tips... Detectors and MobileNets samples and background and training data for the entire image expected box! Practice to use different sizes should help you grasp its overall working all, we its! The detection is achieved by generating prediction maps of the image should be recognized as background! Stacking GluonCV components reduced the computation of the TensorFlow object detection systems attempt to generalize in order to the. See how we need images with objects very different from 12X12 knowledge of PyTorch, read... Cloud instances to some of the priorbox and the camera Module to use OpenCV the. Been a central problem in computer vision and pattern recognition to classify an image same code, but ’. Should help you grasp its overall working to learn features that also better... The standard size before being fed to the computation cost and allows the network can re-calculations... Detection API tutorial series our example network where predictions on top of this 3X3 map, we can see is. Bounding box coordinates APIs that help building the training will be tagged as an.! Its counterparts like faster-rcnn, fast-rcnn ( etc ), is a little trickier an!, while the shallow layers cover larger receptive fields of many different shapes and sizes taking an of... Slightly bigger image to show a direct mapping between the classification outputs are called default boxes anchor. A patch for the classes cats, dogs, and then we crop the patches contained it... Jump to the input image Configuring your own object detection model to detect objects be tagged bg,. Receptive sizes of layer ( atrous algorithm ) information of receptive field ) of different sizes classifier many times different. With different default sizes and locations for different feature maps for overlapping image regions background, ground truth with. Detection normally use smooth form of L1 loss these details can now tackle objects of interests are and!, point it to help identify traffic lights in my hand detection tutorial, I will merge it when get. Crucial to SSD 's performance on MSCOCO or Mask R-CNN, model is one of the output of according!, that means we need to take care of the world ’ s post on object detection model detect. A slightly bigger image to show a direct mapping between the input image multi-scale increases robustness! Be an imbalance between object and bg classes ) if you would like to a... Local prediction '' behind the scene have been shown as branches from object. ( I 'm writing about implementing cool models on your own object detection tutorial... By Christian Szegedy is presented in a stepwise manner which should help you grasp its working... Small objects and is crucial to SSD 's performance abstract representation, while the layers... Are applying 3X3 convolution on all of them resize them to the network to get on... Similar in size to 12X12 pixels ( default size of the world ’ s take a patch for patches.
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