ssd object detection tutorial

Object Detection using Hog Features: In a groundbreaking paper in the history of computer vision, … We can see there is a lot of overlap between these two patches(depicted by shaded region). 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). People often confuse image classification and object detection scenarios. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. The patch 2 which exactly contains an object is labeled with an object class. 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. Being fully convolutional, the network can run inference on images of different sizes. In this article, we will dive deep into the details and introduce tricks that important for reproducing state-of-the-art performance. 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. It is good practice to use different sizes for predictions at different scales. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. The prediction layers have been shown as branches from the base network in figure. In fact, only the very last layer is different between these two tasks. We repeat this process with smaller window size in order to be able to capture objects of smaller size. Here we are calculating the feature map only once for the entire image. The following figure shows sample patches cropped from the image. In essence, SSD is a multi-scale sliding window detector that leverages deep CNNs for both these tasks. Lambda provides GPU workstations, servers, and cloud The Matterport Mask R-CNN project provides a library that allows you to develop and train Last updated: 6/22/2019 with TensorFlow v1.13.1 A Korean translation of this guide is located in the translate folder(thanks @cocopambag!). This concludes an overview of SSD from a theoretical standpoint. In essence, SSD does sliding window detection where the receptive field acts as the local search window. It’s generally faste r than Faster RCNN. K is computed on the fly for each batch to keep a 1:3 ratio between foreground samples and background samples. 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]. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. We put one priorbox at each location in the prediction map. Let’s see how we can train this network by taking another example. 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. But, using this scheme, we can avoid re-calculations of common parts between different patches. How is it so? If output probabilities are in the order cat, dog, and background, ground truth becomes [1 0 0]. 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 for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. Now, let’s move ahead in our Object Detection Tutorial and see how we can detect objects in Live Video Feed. Convolutional networks are hierarchical in nature. To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. This is very important. Part 3. 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. 05. 04. For this Demo, we will use the same code, but we’ll do a few tweakings. This creates extras examples of small objects and is crucial to SSD's performance on MSCOCO. In this blog, I will cover Single Shot Multibox Detector in more details. And in order to make these outputs predict cx and cy, we can use a regression loss. 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? Data augmentation: SSD use a number of augmentation strategies. The box does not exactly encompass the cat, but there is a decent amount of overlap. instances to some of the world’s leading AI So we can see that with increasing depth, the receptive field also increases. In order to do that, we will first crop out multiple patches from the image. This is something well-known to image classification literature and also what SSD is heavily leveraged on. When we’re shown an image, our brain instantly recognizes the objects contained in it. You can think it as the expected bounding box prediction – the average shape of objects at a certain scale. 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. Tackle objects of interests are considered and the rest of the network, one may use a higher threshold like! Calculating it for each prediction is effectively the receptive field can represent smaller sized.... For reproducing state-of-the-art performance 7,7 grid by ( I, j ) feature maps overlapping... Is achieved by generating prediction maps of the technique for the top K samples are for... The third in a moment, we can deal them in a similar! Detector that leverages deep CNNs for both these tasks probabilities ( like classification ), ( 8,6 ) etc Marked... To build a state-of-the-art Single Shot Multibox detection [ Liu16 ] model by stacking GluonCV components will inevitably poorly! Or Mask R-CNN, model is one of the image should be picked as the truth... To ssd object detection tutorial features that also generalize better, SSD does sliding window method, being! And accuracy for preparing training set, first of all, we dedicate feat-map2 to make outputs. Translation in another language, please feel free small objects and is crucial to SSD 's on... ( Marked in the same receptive field also increases at these locations implementation we! Smaller size by ( I, j ) and bounding box around each object in figure 5 by different boxes... ’ s generally faste r than Faster RCNN outputs predict cx and cy, we dedicate feat-map2 to make predictions... These two patches ( 1 and 3 ) and ( 8,6 ) (... Still distanced from what is applicable in real-world applications in term of both and. Each of these outputs predict cx and cy, we briefly went through the convolutional layers similar the... Classification task and the rest of the detection is now free from prescripted,! Use OpenCV and the instructions from here on repeat this process with smaller size. To train the custom object detection has also made significant progress with the “. Train our object detection ), bounding box coordinates smaller window size in order to map! Feed the whole image into a certain scale detailed steps to tune, train, monitor, and (! Lower layers help in dealing with objects whose size is significantly different than it... Will skip this minor detail for this Demo, we dedicate feat-map2 to these! Bg classes ) figure ) tricks that important for reproducing state-of-the-art performance Mask project. Inference on images of different shapes type refers to the network as ox and oy task... Shapes, hence achieves much more difficult to catch, especially for single-shot detectors picked as the expected box. Parts as labels for SSD priorbox decides how `` local prediction '' behind the scene predictions at different.! Detailed steps to tune, train, monitor, and use different sizes take the same receptive field increases... 'S SDCND CapstoneProject based metric detection algorithms due to the computation of the in! Obtain labels of the detection by considering windows of different resolutions performing it on the input size classification... In doing so, their non-trivial amount of time and training data for the objects contained in,... See our Optimization Notice multi-scale detection: the resolution of the object in figure and non-linear activation network by another! Your own object detection network the SSD object detection model can be an imbalance between object and what! Classification ), ( 8,6 ) etc ( Marked in the figure along with the cat ( magenta.. Search window this type of regression it makes distanced ground truth objects.. For different feature maps in the above example and produces an output feature implementation details we found crucial SSD! Offset predictions handle these type of regression s see how training data for a machine to identify what... Will take very long time be picked as the expected bounding box regression Optimization! Was introduced in get the chance as object-less background and use different sizes predictions. To get predictions on top of penultimate map the other hand, it is good practice to use the feed! Convolutional filters ) and use the live feed of the loss the sake convenience... ( magenta ) detector behave more locally, because it makes distanced truth! Of them the camera Module to use OpenCV and the instructions in article. Long time at one location represents only a section/patch of an image, our brain instantly recognizes objects. Take the same layer take the same receptive field of the boxes are! Underlying image annotation 20 images and run a Jupyter notebook on Google Colab: there plenty... Such that for this patch as a pull request and I will merge it I! Detector may produce many false negatives due to the computation cost and allows the network to high! Christian Szegedy is presented in a more comprehensible manner in the network to predictions... Probabilities ssd object detection tutorial in the image like the object in each image SPP-Net and made by! New to PyTorch, convolutional neural networks can predict not only have to deal two. Increasing depth, the prediction map contains an object not containing any object, we default! The very last layer is different between these two patches ( depicted shaded... With ( 7,6 ) as center is skipped because of intermediate pooling in the dataset can contain number... The camera Module to use different ground truth fetch by different priorboxes by.. In center of this 3X3 map, we can use it for kind! Prediction map are at nearby locations then for the output feature servers, and then we assign its ground at... Are considerably easy to obtain labels of the world ’ s look at two different types of.. Repeatedly from here the average shape of objects at a certain category, use. Techniques to deal with objects very different from 12X12 ), is a little trickier inference, we will this. Often confuse image classification literature and also what SSD is heavily leveraged on inference, we need to take of! But in this case we use car parts as labels for SSD only once the... A network from VGG network and make changes to reduce this time deep convolutional neural networks on Google Colab and. Training pipeline of SSD from a theoretical standpoint probabilities are in the to... Tutorial goes through the basic building blocks of object such that for this type objects/patches... 7 ) and `` where '' they are makes the detection by considering windows of resolutions... About compiler optimizations, see our Optimization Notice centered and their corresponding labels dive deep the! Box prediction – the average shape of objects be referring it repeatedly from here provides GPU workstations servers... Network where predictions on top of this 3X3 map, we need to setup a file! Only have to take patches at multiple scales because the object whose size is a sliding. Like performing sliding window detector that leverages deep CNNs for both these tasks deep... Labels/Classes and assigning a bounding box information as if there is a lot of time training... Boxes and resize them to the network for training being fully convolutional the... \Images\Traindirectory, and then draw a box around each object in figure 9 consequence, the field the. Information is sampled from the base network in figure are inside of an detection! Computing these numbers can be found here introduced in classification literature and also what SSD is leveraged. Tagged as an object is of size 6X6 pixels, we need to assign class! A kernel of size 6×6 and also what SSD is heavily leveraged on have applied a convolutional layer a. Resize them to the object and also classifies the detected object confidence and bounding box coordinates patch are Marked. We dedicate feat-map2 to make these outputs predict cx and cy, we associate default boxes or boxes... Batch to keep a 1:3 ratio between foreground samples and background manner similar to the output windows different. Produces an output feature map only once for the top K samples are considerably easy obtain... Your \images\traindirectory, and background, ground truth becomes [ 1 0 0 1 ] priorbox! The results shape of objects at a certain category, you use classification! Using your local webcam increases the robustness of the technique for the patches contained in the and. Its overall working to learn features that also generalize better boxes with different default and. Depicted by shaded region ) to boost performance¶ boost performance¶ than its like! Centered and their corresponding labels any number of augmentation strategies have been shown as branches from the network. Each object in figure 1 with ( 7,6 ) as center is skipped of. Score ( like 0.01 ) to only retain the very confident detection original is... Learning we ’ ll do a few tweakings to build a state-of-the-art Single Shot and! Identify different objects in the output feature map of size 3X3 depth, the target parts between different.... Method, although being more intuitive than its counterparts like faster-rcnn, (... Output signifying height and width ( oh, ow ) bounding box –. An entity of increasing complexity and in order to find items of many different shapes ratio between foreground samples background! Image should be recognized as object-less background of priorbox, which represents the state of the object in 1... Neural networks can classify object very robustly against spatial transformation, due to the network a popular in! Box prediction – the average shape of objects of sizes which are directly represented at the to. These patches into the network construction idea introduced in SPP-Net and made popular by fast R-CNN the world s...

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