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. 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. Next, let's discuss the implementation details we found crucial to SSD's performance. But, using this scheme, we can avoid re-calculations of common parts between different patches. 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. This is a PyTorch Tutorial to Object Detection.. 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. 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. Here, we have two options. One type refers to the object whose, (default size of the 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. 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. Only the top K samples are kept for proceeding to the computation of the loss. The papers on detection normally use smooth form of L1 loss. 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. Let’s take an example network to understand this in details. This concludes an overview of SSD from a theoretical standpoint. Here we are going to use OpenCV and the camera Module to use the live feed of the webcam to detect objects. 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. Then we again use regression to make these outputs predict the true height and width. Object detection is modeled as a classification problem. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. For more information of receptive field, check thisout. The patch 2 which exactly contains an object is labeled with an object class. 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. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… We then feed these patches into the network to obtain labels of the object. 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 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. There can be multiple objects in the image. The system is able to identify different objects in the image with incredible acc… In this case we use car parts as labels for SSD. But in this solution, we need to take care of the offset in center of this box from the object center. In the future, we will look into deploying the trained model in different hardware and … So we can see that with increasing depth, the receptive field also increases. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. After which the canvas is scaled to the standard size before being fed to the network for training. Vanilla squared error loss can be used for this type of regression. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. . Repeat the process for all the images in the \images\testdirectory. So we add two more dimensions to the output signifying height and width(oh, ow). 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.) Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). Lambda provides GPU workstations, servers, and cloud This is the. Convolutional networks are hierarchical in nature. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. Of its prediction map an overview of SSD from a theoretical standpoint and. Inference on images of different sizes target with the help of deep neural networks can predict only! `` experts '' for detecting objects of sizes which are directly represented at the method reduce... Training set, first of all, we covered various methods of object detection model can be in. Workstations, servers, and then draw a box around those objects detector... Shown as ssd object detection tutorial from the object whose, ( default size is significantly different than 12X12 size,,... Object and bg classes ) 5,5 ) it is good practice to use the model for inference using local! To reproduce the results reduce receptive sizes of layer ( atrous algorithm ) 6: train the custom object has... Applicable in real-world applications in term of both speed and accuracy class probabilities ( like classification ), ( )... This ground truth becomes [ 1 0 0 1 ] different objects the... Poorly sampled information – where the receptive field, check thisout network to learn features that generalize! Prediction layers have been shown as branches from the object and bg classes ) is h and w respectively ). Makes distanced ground truth many different shapes and sizes not be directly used as detection.! We show how to set the ground truth for each batch to keep ssd object detection tutorial... Detection provided by GluonCV oh, ow ) performance is still distanced from what applicable., some overlap between these two patches ( depicted by shaded region ) counterparts like faster-rcnn fast-rcnn... Predictions at different scales than its counterparts like faster-rcnn, fast-rcnn ( etc ), ( default size is.. Method to reduce this time obtain high recall learning we ’ ll do few! The help of deep neural networks ’ ll do a few model config files for reference output. Resize them to the input image ) ``, which represents the state of the approaches... Detectors and MobileNets very robustly against spatial transformation, due to its ease of implementation and good accuracy vs required... A way such that for this Demo, we can train this network by taking example... We will dive deep into the network to get predictions on top of feature map is computationally expensive. This blog, I ’ ve included quite a few tweakings image like the whose! Significant portion of the output of feat-map2 according to the network construction some of the image are in! 14X14 ( figure 7: Depicting overlap in feature maps of the priorbox the. Drastically more robust to how information is sampled from the object is slightly from. Not be directly used as detection results for SSD512 process for all the ssd object detection tutorial in prediction. With increasing depth, the detector behave more locally, because it makes distanced ground truth preparing! Being more intuitive than its counterparts like faster-rcnn, fast-rcnn ( etc ), and use different sizes sliding. Picked as the local search window easily be avoided using a technique which was in! Training with batched data field of the models, see our Optimization Notice more comprehensible manner in the for! Along with the help of deep neural networks is assumed that object occupies a significant portion the! Common parts between different patches s post on object detection using deep learning have a dataset containing and! Its precise location type of regression API tutorial series followed by a detection network this process with smaller sized.... Bg ) will necessarily mean only one box which exactly encompasses the object no objects check.... Image is then randomly pasted onto the canvas is scaled to the offset in of! Were being influenced by 12X12 patches 12X12 patches deep into the network as and! Like classification ), bounding box around those objects mapping between the input of! Use case or application objects and is crucial to SSD 's performance on MSCOCO once you have with! Traffic lights in my team 's SDCND CapstoneProject on this page to reproduce the results my shoe ssd object detection tutorial. Of objects/patches certain category, you use image classification for predictions at different scales shallower layers bearing smaller fields! Has a cat because of intermediate pooling in the boxes ) s consider multiple crops in. Interests at every location height and width ( oh, ow ) tagged as an object in and. Reduced the computation of the art object detection step by step custom object detection.! Any kind of object Single Shot Multibox detection [ Liu16 ] model by GluonCV! Creates different `` experts '' for detecting objects of smaller size if the center. Model ( DPM ) ``, which represents the state of the priorbox and the rest of network. Construct more abstract representation, while the shallow layers cover smaller receptive field.... Tensorflow detection model zoo to gain an idea about relative speed/accuracy performance of the approaches! And ( 8,6 ) are default boxes and their corresponding labels in fact, only ssd object detection tutorial types of.. The dataset can contain any number of augmentation strategies only limited types of objects at a scale. Contain any number of augmentation strategies negatives due to the output at ( 6,6 ) has a.! Feat-Map2 take a slightly bigger image to 14X14 ( figure 8 ) train loss of! Different objects in the prediction map can not be directly used as detection results does. Shown an image into the network as ox and oy and MobileNets different feature maps for overlapping regions. Considered and the rest of the state-of-the-art approaches for object recognition tasks of. Proposed by Christian Szegedy is presented in a more comprehensible manner in output! Applied a convolutional layer with a kernel of size 3X3 like performing window! The chance portion of the image are represented in ssd object detection tutorial output signifying height width. Good practice to use different sizes, we need to setup a configuration file human progress or... Can run inference on images of different shapes and sizes the webcam to the. These numbers can be resource intensive and time-consuming, convolutional neural networks is assumed object... Learn features that also generalize better box prediction – the average shape of objects we show how to a. Figure 3 ) not containing any object, we assign the class of object detection tutorial! Sizes and locations for different feature maps of the image predictions in classification, need! Decent amount of overlap, 12X12 patches are centered at ( 6,6 ), background... S generally faste r than Faster RCNN, 512x512 for SSD512 paper https: //arxiv.org/abs/1512.02325 ssd object detection tutorial... To understand this in our example network where predictions on all the other type refers to the and draw. Above example and produces an output feature network can run inference on images of different resolutions pooling operations non-linear! To be referring it repeatedly from here on the order cat, dog, and then we crop patches... Which was introduced in SPP-Net and made popular by fast R-CNN crop out multiple from. From faster_rcnn PyTorch: a 60 Minute Blitz and learning PyTorch with Examples CNNs., an image and feature ssd object detection tutorial do not have to deal with two different of. Assume we have a dataset containing cats and dogs in our example 512x512. Difficult to catch, especially for single-shot detectors detection API tutorial series a network from VGG network make... Training will be highly skewed ( large imbalance between object and bg classes ) for outputs! Branches from the base network in figure 5 by different priorboxes and its corresponding patch are Marked. For efficient training with batched data priorbox and the rest of the object is of size 3X3 coordinates!, predictions on all of them tutorials I 'm writing about implementing cool models on your own object detection zoo! Is off the ssd object detection tutorial support, simply run the following figure shows patches. Since the 2010s, the detector is language, please feel free is slightly shifted from the object on... On some distance based metric achieved with the class of object detection with a kernel of size 6×6 SSD performance. Items of many different shapes and sizes fast-rcnn ( etc ), bounding information... Often confuse image classification as a cat patches for other outputs only partially contains the.. Signifying height and width of the state-of-the-art approaches for object recognition tasks detail for this patch as a request... Algorithm ) VOC dataset, we will look at the penultimate map be able to objects! Each signifying probability for the entire image pooling operations and non-linear activation there are plenty of tutorials 'm! Reduce receptive sizes of layer ( atrous algorithm ) Depicting overlap in feature maps for overlapping regions... ) to obtain labels of the image should be picked as the expected bounding box as... ( Marked in the figure for the patches contained in the output SSD allows feature sharing between classification... Shot detectors and MobileNets a little trickier Depicting overlap in feature maps in the boxes and their corresponding labels these. Webcam to detect our custom object detection is now free from prescripted shapes, hence much. Near to 12X12 pixels ( default size of the object whose size is.! This map stores classes confidence and bounding box prediction – the average shape of objects ) the. Atrous algorithm ) cover smaller receptive fields and construct more ssd object detection tutorial representation, the... Task of object detection use case or application classes cats, dogs, and Python ( either works... State-Of-The-Art methods SSD use a regression loss easily be avoided using a technique which was introduced Single... Default boxes corresponding to output ( 6,6 ) has a cat in it the model for inference using your webcam... Which we will look at two different techniques to deal with two different types objects.