Object detection reduces the human efforts in many fields. YOLO vs RetinaNet performance on COCO 50 Benchmark. It achieved 91.2% top-5 accuracy on ImageNet which is better than VGG (90%) and YOLO network(88%). The architecture of the Darknet 19 has been shown below. It then guesses an objectness score for each bounding box using logistic regression. “1(obj)ij=1 only if the box contain an object and responsible for detect this object (higher IOU)“. Despite adding 369 additional concepts Darknet-19 achieves 71.9% top-1 accuracy and 90.4% top-5 accuracy. The documentation indicates that it is tested only with Intel’s GPUs, so the code would switch you back to CPU, if you do not have an Intel GPU. Each object still only assigned to one grid cell in one detection tensor. Here we will have look what are the so called Incremental improvements in YOLO v3. This gives the network a time to adjust its filters to work better on higher resolution input. Then they trained the network for 160 epochs on detection datasets (VOC and COCO datasets). For example, if the input image contains a dog, the tree of probabilities will be like this tree below: Instead of assuming every image has an object, we use YOLOv2’s objectness predictor to give us the value of Pr(physical object), which is the root of the tree. Let the black dotted boxes represent the 2 anchor boxes for that cell . Feature Pyramid Networks (FPN): YOLO v3 makes predictions similar to the FPN where 3 predictions are made for every location the input image and features are extracted from each prediction. The model next predicts boxes at three different scales, extracting features from these scales using a similar concept to feature pyramid networks. To predict k bounding boxes YOLOv2 used the idea of Anchor boxes. It is based on regression where object detection and localization and classification the object for the input image will take place in a single go. Using only convolutional layers(without fully connected layers) Faster R-CNN predicts offsets and confidences for anchor boxes. This type of algorithms is commonly used real-time object detection. (2018). When we plot accuracy vs. speed on the AP50 (IOU 0.5 metric), we see that YOLOv3 has significant benefits over other detection systems. Doing upsampling from previous layers allows getting meaning full semantic information and finer-grained information from earlier feature map. For every boundary box has fiver elements (x, y, w, h, confidence score). YOLO v3 has DARKNET-53, with … For example, an object can be labeled as a woman and as a person. YOLOv3: A Huge Improvement — Anand Sonawane — Medium. Using independent logistic classifiers, an object can be detected as a woman an as a person at the same time. Each of the 7x7 grid cells predicts B bounding boxes(YOLO chose B=2), and for each box, the model outputs a confidence score ©. 1-Since each grid cell predicts only two boxes and can only have one class, this limits the number of nearby objects that YOLO can predict, specially for small objects that appear in groups, such as flocks of birds. This has been resolved to a great extent in YOLO v2 where it is trained with random images with different dimensions range between 320*320 to 608*608 [5]. As we see, all the classes are under the root (physical object). Using a softmax for class prediction imposes the assumption that each box has exactly one class, which is often not the case(as in Open Image Dataset). 2-Detection datasets have only common objects and general labels, like “dog” or “boat”, while Classification datasets have a much wider and deeper range of labels. Then they removed the 1x1000 fully connected layer and added four convolutional layers and two fully connected layers with randomly initialized weights and increased the input resolution of the network from 224×224 to 448×448. They trained the Darknet-19 model on WordTree .They extracted the 1000 classes of ImageNet dataset from WordTree and added to it all the intermediate nodes, which expands the label space from 1000 to 1369 and called it WordTree1k.Now the size of the output layer of darknet-19 became 1369 instead of 1000. Confidence score is the probability that box contains an object and how accurate is the boundary box. Since YOLO uses 7x7 grid then if an object occupies more than one grid this object may be detected in more than one grid . Object detection reduces the human efforts in many fields. For example (prior 1) overlaps the first ground truth object more than any other bounding box prior (has the highest IOU) and prior 2 overlaps the second ground truth object by more than any other bounding box prior. (2016). The network predicts 5 bounding boxes for each cell. The width and height are predicted relative to the whole image, so 0<(x,y,w,h)<1. For these 1369 predictions, we don’t compute one softmax, but we compute a separate softmax overall synsets that are hyponyms of the same concept. Since the ground truth box is drawn by hand we are 100% sure that there is an object inside the ground truth box; accordingly, any box with a high IOU with the truth box will also surround the same object, then the higher the IOU, the higher the possibility that an object occurs inside the predicted box. [5]. After training on classification the fully connected layer is removed from Darknet-53. It supports CPU and GPU computation. we can consider the prediction as incorrect if the IOU between the predicted box and the ground truth box is less than the threshold value(0.5,0.75,…). The predictions are encoded as S ×S ×(B ∗5 + Classes) tensor. YOLOv3: An Incremental Improvement. For pretraining they used the first 20 convolutional layers from the network we talked about previously followed by a average-pooling layer and a 1x1000 fully connected layer with input size of 224×224 .This network achieve a top-5 accuracy of 88%. 2- Sort the predictions starting form the highest confidence C. 3-Choose the box with the highest C and output it as a prediction . YOLOv3: A Huge Improvement — Anand Sonawane — Medium. 5-Start again from step (3) until all remaining predictions are checked. Sometimes we need a model that can detect more than 20 classes, and that is what YOLO9000 does. After that they trained the model for detection. (2018). I’m going to quickly to compare yolo on a cpu versus yolo on the gpu explaining advantages and disadvantages for both of them. Our error metric should reflect that small deviations in large boxes matter less than in small boxes. Higher Resolution Classifier: the input size in YOLO v2 has been increased from 224*224 to 448*448. I used the pre-trained Yolov3 weight and used Opencv’s dnn module and only selected detections classified as ‘person’. It only needs to look once at the image to detect all the objects and that is why they chose the name (You Only Look Once) and that is actually the reason why YOLO is a very fast model. For real-life applications, we make choices to balance accuracy and speed. Note: We ran into problems using OpenCV’s GPU implementation of the DNN. If you don't already have Darknet installed, you should do that first. [online] Available at: https://towardsdatascience.com/batch-normalization-in-neural-networks-1ac91516821c [Accessed 5 Dec. 2018]. However, with YOLOv3 we see better performance for small objects, and that because of using short cut connections. Additionally to the confidence score C the model outputs 4 numbers ( (x, y), w , h) to represent the location and the dimensions of the predicted bounding box. It is much deeper than the YOL v2 and also had shortcut connections. I’m not going to explain how the COCO benchmark works as it’s beyond the scope of the work, but the 50 in COCO 50 benchmark is a measure of how well do the predicted bounding boxes align the the ground truth boxes of the object. X and y are the coordinates of the object in the input image, w and h are the width and height of the object respectively. YOLO used sum-squared error SSE for the loss function because it is easy to optimize. This has been resolved in the YOLO v2 divides the image into 13*13 grid cells which is smaller when compared to its previous version. This comprehensive and easy three-step tutorial lets you train your own custom object detector using YOLOv3. YOLOv2 predicts location coordinates relative to the location of the grid cell. Towards Data Science. In this image we have a grid cell(red) and 5 anchor boxes(yellow) with different shapes. In our case, we are using YOLO v3 to detect an object. For each class (cars, pedestrians, cats,….) During training, they mix images from both detection and classification datasets. Darknet-53: the predecessor YOLO v2 used Darknet-19 as feature extractor and YOLO v3 uses the Darknet-53 network for feature extractor which has 53 convolutional layers. R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms. To achieve better performance they used some ideas: 1-BatchNormalization: By adding batch normalization on all of the convolutional layers in YOLO they get more than 2% improvement in mAP. It is very hard to have a fair comparison among different object detectors. filename graph_object_SSD. For windows, you can also use darkflow which is a tensorflow implementation of darknet, but Darkflow doesn’t offer an implementation for YOLOv3 yet. The model outputs a softmax for each branch level. [8]. Looks like the pre-trained model is doing quite okay. Darknet-53 composes of the mainly with 3x3 and 1x1 filters with shortcut connections. At each scale YOLOv3 uses 3 anchor boxes and predicts 3 boxes for any grid cell. Someone may ask how and why they chose these 5 boxes ?They run k-means clustering on the training set bounding boxes for various values of k and plot the average IOU with closest centroid, but instead of using Euclidean distance they used IOU between the bounding box and the centroid . They chose k = 5 as a good trade off between model complexity and high recall. The (x,y) coordinates represent the center of the box relative to the bounds of the grid cell. YOLO runs a classification and localization problem to each of the 7x7=49 grid cells simultaneously. It tries to optimize the following, multi-part loss: The first two terms represent the localization loss, Terms 3 & 4 represent the confidence loss, The last term represents the classification loss. Instead of fixing the input image size they changed the network every few iterations. To calculate the precision of this model, we need to check the 100 boxes the model had drawn, and if we found that 20 of them are incorrect, then the precision will be =80/100=0.8. However, YOLOv3 performance drops significantly as the IOU threshold increases (IOU =0.75), indicating that YOLOv3 struggles to get the boxes perfectly aligned with the object, but it still faster than other methods. 2-High Resolution Classifier: The original YOLO was trained as follow: i-They trains the classifier network at 224×224 . When it sees a classification image we only backpropagate loss from the classification specific parts of the architecture. YOLO v3 is able to identify more than 80 different objects in one image. For example if the network is trained for person and a man it would give the probability of 0.85 to person and 0.8 for the man and label the object in the picture as both man and person. Since the classification and localization network can detect only one object, that means any grid cell can detect only one object. YOLO v3 has all we need for object detection in real-time with accurately and classifying the objects. The idea of mixing detection and classification data faces a few challenges: 1-Detection datasets are small comparing to classification datasets. You only look once, or YOLO, is one of the faster object detection algorithms out there. 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