resnet

accelerate convergence. Using K-means Clustering method to automatically select the best initial boxesdirect location prediction: The method of guaranteeing and accelerating anchor box position convergence: predicting position matching fine-grained Features: More precise features (finer grained Features) can improve detection of small targets. The authors add Passtrough layers to the network to add features. Passthrough is similar to ResNet, combinin

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered receptive Field. On the basis of feeling wild, 1980 Fukushima proposed a theoretical model Neocognitron is the first application of the field of artificial neural network. In 1998, the LENET-5 model proposed by LeCun was successful

->relu->aAnd ResNet is a step.Input–>linear->rule->linear->? ->relu->aOperation Process:Next is:?No, the larger the network, the higher the probability of the error, so the fidelity:Take the a[l] and add it in to get the activation function so that it can be guaranteed. Look at one more picture:The overall processThis is not a real jump, but rather the original a value for the activation function.Inception Network AlgorithmIn fact, Google is a convolu

networks, but soon people find that a good initialization strategy is much more effective than the laborious level-by-layer pre-training, which appeared in the 2014 batch Normalization technology is also a deeper network can be effectively trained, by the end of 15, through the residual (ResNet) we can basically train any depth of neural network.So now the main application of automatic encoder has two aspects, the first is data denoising, the second

project is very strong, but there is also a consequence, that is the details you have no way to control, the training process is highly encapsulated, resulting in you have no way to know the details inside, as well as the specific details of each parameter, making debugging and research becomes very difficult.[Pytorch] An underlying framework similar to Theano TensorFlow. Its underlying optimizations are still on the C, but all of its basic frameworks are written in Python.Se-resnet50_33epoch:

bottleneck after concatenate. The front concatenate after a bottleneck regression to the key point response graph. However, unlike the previous L2 loss, this calculation loss use the online hard mining method, the training only dynamically return loss a large number of channel. It can be understood that the loss of the front is the key point of the real visible response, and the following loss use the global information to return to the Occlusion key point response. The network takes RESNET50

global average pool (averaging POOLING,AGP) replaces FC to fuse the depth characteristics. Finally, the method of Softmax and other loss functions as the network objective function to guide the learning process has obtained very good prediction results on ResNet and googlenet.On the other hand, Wei Xiushing (see Reference) recent studies have found that FC can act as a "firewall" in the Model representation capability migration process. Classical con

method, squeezenet can ensure accuracy without loss (even slightly improved), the maximum compression rate, the original alexnet from 240MB to 4.8MB, and combined deep The compression can reach 0.47MB, fully meet the mobile end of the deployment and low bandwidth network transmission. In addition, the author also draws on the idea of ResNet, modifies the original network structure, increases the bypass branch, and improves the classification accurac

Series, RCNN,SPP-NET,FAST-RCNN,FASTER-RCNN Yolo Series, YOLO and YOLO9000 Ssds Later, there is a deep residual network resnet, and then appeared RFCN, and the recent mask-rcnn and so on, the detection effect is getting better and higher precision. Detection of characteristic +adaboost features of Haar As the first installment of this series, let's start with a simple, Haar feature +adaboost algorithm. The principle is simple. There are a lot of tuto

the detection precision is not very high. Main Ideas YOLO V2: Represents the current level of the industry's most advanced object detection, its speed faster than other detection systems (FASTERR-CNN,RESNET,SSD), users can be in its speed and accuracy between the tradeoff.YOLO9000: This network structure can detect more than 9000 kinds of objects in real time, thanks to its use of wordtree, through Wordtree to mix the detection dataset and identify t

using the activation function, Ability to gain performance improvements with smaller network parameters Article address Https://arxiv.org/pdf/1603.05201.pdf Introduction to more comprehensive activation functions: https://zhuanlan.zhihu.com/p/22142013 Web sites that can be used for reference: Https://kanghsi.gitbooks.io/deep-learning/cnnzhong_de_diao_can_zhi_dao.html Http://www.cnblogs.com/liujshi/p/5646102.html http://blog.csdn.net/qq_20259459/article/details/70316511 A summary of the networ

, sample collection methods and requirements. The algorithm is able to achieve high detection accuracy, bottom error rate and high efficiency in video real time system. Other requirements: 1. Bachelor degree or above in image processing, pattern recognition, machine learning or equivalent; 2. Proficient in C + + programming, with strong development capabilities, familiar with Python and other development tools; 3. Proficient in image processing, machine learning, in-depth learning and other rela

the first. Since it is too difficult to control the study of Gan, we would like to disassemble, do not let Gan one time to learn all the data, but let Gan step by step to complete the learning process. In the case of a picture generation, don't let the build model in GAN (G) generate an entire picture each time, but let it generate part of the picture. This thought can be regarded as a variant of DeepMind's famous work DRAW. DRAW's paper [3] begins by saying that we humans rarely do a single pi

(1) speed/accuracy trade-offs for modern convolutional object detectors Its main consideration is three kinds of detectors (Faster RCNN,R-FCN,SSD) as the meta structure, three kinds of CNN Network (vgg,inception,resnet) as feature extractor, change other parameters such as image resolution, proposals quantity, etc. The tradeoff between accuracy rate and speed of target detection system is studied. (2) Yolo9000:better, Faster, stronger It is an upgrade

and channel 0; Learn the arbitrary shape of the filter: Learning through 0 in 2D space, to achieve the requirements of learning arbitrary shape; Shorten the number of layers in the DNN: Completely remove the entire layer, by increasing the shortcut method to achieve the situation without fault. The article does not provide the learning algorithm in the case of SSL and DNN combination. The experimental part is very detailed, with lenet in Mnist, Convnet and