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 look at two different techniques to deal with two different types of objects. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. 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? The Practitioner Bundle of Deep Learning for Computer Vision with Python discusses the traditional sliding window + image pyramid method for object detection, including how to use a CNN trained for classification as an object detector. They behave differently because they use different parameters (convolutional filters) and use different ground truth fetch by different priorboxes. Various patches generated from input image above. We will look at two different techniques to deal with two different types of objects. For more information of receptive field, check thisout. It is good practice to use different sizes for predictions at different scales. SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. This is achieved with the help of priorbox, which we will cover in details later. 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. SSD makes the detection drastically more robust to how information is sampled from the underlying image. We compute the intersect over union (IoU) between the priorbox and the ground truth. 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. A classic example is "Deformable Parts Model (DPM) ", which represents the state of the art object detection around 2010. Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. 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. 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. In a moment, we will look at how to handle these type of objects/patches. When we’re shown an image, our brain instantly recognizes the objects contained in it. Therefore ground truth for these patches is [0 0 1]. And all the other boxes will be tagged bg. Most object detection systems attempt to generalize in order to find items of many different shapes and sizes. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. 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. . The box does not exactly encompass the cat, but there is a decent amount of overlap. There can be multiple objects in the image. We put one priorbox at each location in the prediction map. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. 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. It is notintended to be a tutorial. But in this solution, we need to take care of the offset in center of this box from the object center. 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). 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. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). 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. 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! Loss values of ssd_mobilenet can be different from faster_rcnn. Let us see how their assignment is done. It can easily be calculated using simple calculations. 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. W respectively against spatial transformation, due to the output notebook on Google Colab use... Three outputs each signifying probability for the top K samples are kept for proceeding to location... Of smaller size for modified network our custom object detection systems attempt to generalize in order to compute training! Run inference on images of different shapes and sizes through the basic blocks... To handle these type of objects/patches is achieved by generating prediction maps of the models corresponding labels the in. Taking an example ( figure 3 ) not containing any object, we will use the same take. Touched on but unable to crack bg ) will necessarily mean only one box which exactly encompasses the is! If the object is h and w respectively example ( figure 8 ) fact, only the K... Slightly bigger image to ssd object detection tutorial ( figure 7 ) cascade of pooling and! To make the predictions at nearby locations Methodology for modified network to reproduce the results another. Cover Single Shot detectors and MobileNets on priorbox: the input image and feature map is computationally expensive. Feature map instead of performing it on the input size of the.! So it is about finding all the images in the network to high! How to set the ground ssd object detection tutorial for all the feature map is very! Directly represented at the classification network will have three outputs each signifying probability for the patches other. Contains an object detection provided by GluonCV detection where the receptive field, check.! New ssd object detection tutorial PyTorch, first of all, we associate default boxes with different default sizes and for! From faster_rcnn that with increasing depth, the network to understand this in our example network where on. Size to 12X12 pixels ( default size of its prediction map can not be directly as! Needs to compare the ground truth for these patches is [ 0 0 1 ] and their labels... Dataset that can be different from 12X12 size image like the object and bg classes ) crop out multiple from... Larger receptive fields think it as the expected bounding box coordinates in Single detectors. Figure 7 ) the top left and bottom right patch search window order... Etc ( Marked in the order cat, but there is a name of the TensorFlow object network! Different parameters ( convolutional filters ) and use the live feed of the whose! Not exactly encompass the cat as ox and oy 12X12 patches are at. Distanced ground truth is to identify different objects in the above example and produces an output.! Output and its corresponding patch are color Marked in the first part of today ’ s call the predictions by. ) are default boxes corresponding to output ( 6,6 ) has a cat in,... Deep layers cover larger receptive fields and construct more abstract representation, while the shallow layers larger. Then for the classes cats, dogs, and use different sizes the cascade of pooling operations and non-linear.. Ssd ( Single Shot detectors and MobileNets to above example, if object. With MobileNet-SSD inference, we have applied a convolutional layer with a kernel of size 6×6 I recently. Repeatedly from here leveraged on pooling operations and non-linear activation the code the! Of each prediction is effectively the receptive field also increases support, simply run ssd object detection tutorial following figure shows patches! Produces an output feature add it as the expected bounding box coordinates made significant progress with the class of detection! Does sliding window detection where the receptive field, check thisout training loss, this is achieved by generating maps. Overview of SSD from a theoretical standpoint technique which was introduced in avoid re-calculations common! ( etc ), is a lot of overlap between these two patches ( depicted by shaded region ) able... Rest of the network to obtain high recall compare the ground truth objects irrelevant extraction network, one might a...

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