ResNet 34 architecture diagram

Each ResNet block is either two layers deep (used in small networks like ResNet 18, 34) or 3 layers deep (ResNet 50, 101, 152). 50-layer ResNet: Each 2-layer block is replaced in the 34-layer net with this 3-layer bottleneck block, resulting in a 50-layer ResNet (see above table). They use option 2 for increasing dimensions ResNet-34 Pre-trained Model for PyTorch. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site Understanding and implementing ResNet Architecture [Part-1] (Used in small networks like ResNet 18, 34) or 3 layer deep( ResNet 50, 101, 152). ResNet 2 layer and 3 layer Block ResNet Architecture. Now, let us understand the architecture of the ResNet models. The following is the architecture of the 18,34,50,101 and 152 layered ResNet model. All of them work the same way as explained above. The following diagram compares ResNet and ResNeXt model and will help you understand how it works, Working of ResNext.

ResNet (34, 50, 101): Residual CNNs for Image

ResNet-34 Kaggl

Understanding and Implementing Architectures of ResNet and

  1. Resnet models were proposed in Deep Residual Learning for Image Recognition. Here we have the 5 versions of resnet models, which contains 18, 34, 50, 101, 152 layers respectively. Detailed model architectures can be found in Table 1
  2. ResNet Architecture. Compared to the conventional neural network architectures, ResNets are relatively easy to understand. Below is the image of a VGG network, a plain 34-layer neural network, and a 34-layer residual neural network. In the plain network, for the same output feature map, the layers have the same number of filters
  3. Figure 3. ResNet 34 Architecture Figure 4. Plain VS Residual Layer necessary when a residual has a different 2d spatial dimen-sion from the output that it is being added to. For example, The first residual used in ResNet 34 is of size 15x15x64, but it is being added to an output of size 8x8x128. To tak

3 - Building your first ResNet model (50 layers)¶ You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. ID BLOCK in the diagram stands for Identity block, and ID BLOCK x3 means you should stack 3 identity blocks together ResNet Encoder. A ResNet can be used for the encoder/down sampling section of the U-Net (the left half of the U). In my models, I have used a ResNet-34, a 34 layer ResNet architecture, as this has been found to be very effective by the Fastai researchers and is faster to train than ResNet-50 and uses less memory. Decode

Architecture. Logical scheme of base building block for ResNet: Architectural configurations for ImageNet. Building blocks are shown in brackets, with the numbers of blocks stacked Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question.Provide details and share your research! But avoid . Asking for help, clarification, or responding to other answers Introduction. In this article, we will go through the tutorial for the Keras implementation of ResNet-50 architecture from scratch. ResNet-50 (Residual Networks) is a deep neural network that is used as a backbone for many computer vision applications like object detection, image segmentation, etc. ResNet was created by the four researchers Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun and.

ResNet Understanding ResNet and Analyzing various Model

Residual Networks (ResNet) - Deep Learning - GeeksforGeek

The convolutional block is another type of block for ResNet block, and is used when the input and output dimension doesn't match up when we change the channel size. For example, to reduce the activation dimensions's height and width by a factor of 2, we can use a $1 \times 1$ convolution with a stride of 2 Released in 2015 by Microsoft Research Asia, the ResNet architecture (with its three realizations ResNet-50, ResNet-101 and ResNet-152) obtained very successful results in the ImageNet and MS-COCO competition. The core idea exploited in these models, residual connections, is found to greatly improve gradient flow, thus allowing training of much. ResNet has been trained with 34, 50, 101 and 152 layers. And if that was not enough, In the diagram, These networks are stacked together to arrive at a deep network architecture. For e.g., bellow is a ResNet arch with 34 layers. Fig. Therefore, this model is commonly known as ResNet-18. By configuring different numbers of channels and residual blocks in the module, we can create different ResNet models, such as the deeper 152-layer ResNet-152. Although the main architecture of ResNet is similar to that of GoogLeNet, ResNet's structure is simpler and easier to modify

ResNet 18 11.174M ResNet 34 21.282M ResNet 50 23.521M ResNet 101 42.513M ResNet 152 58.157M Bibliography [1] K. He, X. Zhang, S. Ren and J. Sun, Deep Resifual Learning for Image Recognition, in CVPR, 2016 ResNet • Directly performing 3x3 convolutions with 256 feature maps at input and output: 256 x 256 x 3 x 3 ~ 600K operations Rethinking the inception architecture for computer vision, CVPR 2016 • K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, CVPR 2016 There are different versions of ResNet, including ResNet-18, ResNet-34, ResNet-50, and so on. The numbers denote layers, although the architecture is the same. To create a residual block, add a shortcut to the main path in the plain neural network, as shown in the figure below Instantiates the ResNet101 architecture. Reference. Deep Residual Learning for Image Recognition (CVPR 2015); For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning. Note: each Keras Application expects a specific kind of input preprocessing

Resnet 18 architecture. Tips, articles, resources on architect There is a lot of discussion about sustainable architecture and how it differs from what. we call eco-designers have taken this concept and applied it to the designing of home ResNet Architectures Each ResNet block is either 2 layer deep (Used in small networks like ResNet 18, 34) or 3 layer deep( ResNet 50, 101, 152) Params and helpers Loading of training/testing ids and depths Read images and masks Calculating the salt coverage and salt coverage classes Show some example images Create train/validation split stratified by salt coverage Build U-Net Model ResNet 34 U-Net with ResNet34 Encoder Define Loss Function Augmentation Training Predict the validation. Define model architecture as a sequence of layers. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - May 2, 2017 Today: CNN Architectures 7 Case Studies - AlexNet - VGG - GoogLeNet - ResNet Also.... - NiN (Network in Network) - Wide ResNet - ResNeXT - Stochastic Depth - DenseNet - FractalNet - SqueezeNet. Fei-Fei Li & Justin Johnson. Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack residual blocks ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks

Architecture of ResNet-50. The network contains five max-pooling layers with a kernel size of 2*2 with a two-pixel stride. 4, a 34 layer exemplary architecture diagram This paper proposes a gesture recognition method using convolutional neural networks. Hyperparameter : This architecture is the first use of Resnet50 diagram Resnet50 diagram 4. ResNet. ResNet is one of the monster architectures which truly define how deep a deep learning architecture can be. Residual Networks (ResNet in short) consists of multiple subsequent residual modules, which are the basic building block of ResNet architecture. A representation of residual module is as follow

Building your first ResNet model (50 layers) You now have the necessary blocks to build a very deep ResNet. The following figure describes in detail the architecture of this neural network. ID BLOCK in the diagram stands for Identity block, and ID BLOCK x3 means you should stack 3 identity blocks together Through the changes mentioned, ResNets were learned with network depth of as large as 152. It achieves better accuracy than VGGNet and GoogLeNet while being computationally more efficient than VGGNet. ResNet-152 achieves 95.51 top-5 accuracies. The architecture is similar to the VGGNet consisting mostly of 3X3 filters The model is the same as ResNet except for the bottleneck number of channels: which is twice larger in every block. The number of channels in outer 1x1: convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048: channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on. xi ABSTRACT M.S., Purdue University, May 2018. Deep Neural Network Architectures for Modu-lation Classi cation. Major Professor: Aly El Gamal. This thesis investigates the value of employing deep learning for the task of wire Resnet50 diagram

The Residual Network, or ResNet, architecture for convolutional neural networks was proposed by Kaiming He, et al. in their 2016 paper titled Deep Residual Learning for Image Recognition, which achieved success on the 2015 version of the ILSVRC challenge. A key innovation in the ResNet was the residual module function. This is the ResNet v2 scheme proposed in. # Expand channels of shortcut to match residual. # Should be int if network architecture is correctly configured. # 1 X 1 conv if shape is different. Else identity. print ( 'reshaping via a convolution...') Builds a residual block with repeating bottleneck blocks. stage and block Home Energy Rating Standards RESNET-ANSI American National Standards HERS H2O RESNET Committees. Quality Assurance. RESNET Advocacy. Raters. HERS Raters How to Become

What is Resnet or Residual Network How Resnet Helps

Scalable Architectures for CIFAR-10 and ImageNet. In NASNet, though the overall architecture is predefined as shown above, the blocks or cells are not predefined by authors. Instead, they are searched by reinforcement learning search method.; i.e. the number of motif repetitions N and the number of initial convolutional filters are as free parameters, and used for scaling ResNet [31, 35] is also called a residual network that introduces skip connection concept to deal vanishing gradient problem. This prevents the distortion which appears as the network becomes deeper and complex. ResNet variant ResNet-50 is used as one of the models. ResNet-50 architecture is shown in Fig. 9 The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance.

Hiddenlayer seems nice, but doesnt work, looks like i need to do it by hands. 1. level 1. timedacorn369. · 2y. Keras has a plot_model function which saves the model architecture. 3. level 2. hadaev Active Oldest Votes. 8. From your output, we can know that there are 20 convolution layers (one 7x7 conv, 16 3x3 conv, and plus 3 1x1 conv for downsample). Basically, if you ignore the 1x1 conv, and counting the FC (linear) layer, the number of layers are 18. And I've also made an example on how to visualize your architecture in pytorch via.

shallower architecture and its deeper counterpart that adds more layers onto it. There exists a solution by construction to the deeper model: the added layers are identity mapping, and the other layers are copied from the learned shallower model. The existence of this constructed solution indicate The following are 30 code examples for showing how to use torchvision.models.resnet18().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example Functions. ResNet50 (...): Instantiates the ResNet50 architecture. decode_predictions (...): Decodes the prediction of an ImageNet model. preprocess_input (...): Preprocesses a tensor or Numpy array encoding a batch of images. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and.

ResNet is a short name for Residual Network. As the name of the network indicates, the new terminology that this network introduces is residual learning. ResNet50 is a 50 layer Residual Network. There are other variants like ResNet101 and ResNet152 also. If playback doesn't begin shortly, try restarting your device There is nothing to stop you from adding any type of layer on top of the ResNet architecture, VGG architecture, DenseNet or any such network. How you do it depends on the framework you use. In Pytorch you could make a class that inherits from nn.M.. Wide ResNet-101-2 model from Wide Residual Networks. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Parameter Hi, this is probably a bit late, but I'm sharing what I found in case someone else stumbles upon this. So, based on the code in qubvel/segmentation-models and MrGiovanni/UNetPlusPlus, it looks like the backbone is the model to be used as the encoder part (the first half) of the UNet, and from which the decoder part (the second half) will be programmatically built up

Residual Neural Network (ResNet

Resnet architecture was evaluated on ImageNet 2012 classification dataset consisting of 1000 classes. The model was trained on the 1.28 million training images and evaluated on the 50k validation images. Moreover, 100k images were used for testing the model accuracy IN NVIDIA AMPERE ARCHITECTURE. 11 SPARSITY IN A100 GPU Fine-grained structured sparsity for Tensor Cores • 50% fine-grained sparsity • 2:4 pattern: 2 values out of each contiguous block of 4 must be 0 ResNet-34 73.7 73.9 0.2 73.7 - ResNet-50 76.6 76.8 0.2 76.8 0.2 ResNet-101 77.7 78.0 0.3 77.9 DoDAF v2.02, Chg 1 31 January 2015 ii 2.11 Locations 2-48 2.11.1 Data group description 2-49 2.11.2 Use in DoD core processes 2-51 2.12 Pedigrees 2-5

Understanding ResNet50 architectur

Aug 10, 2015 - Image 34 of 36 from gallery of Gap House / Archihood WXY. Diagram 05-jul-2016 - Explora el tablero de Sofia Garay Architecture - Program Diagrams en Pinterest. Ver más ideas sobre diagramas de arquitectura, organigrama, disenos de unas architecture (Whitney et al., 2004; Fixson and Park, 2008). For example, in a definitive white paper, the Engineering Systems Division at MIT defined the architecture (of any complex system) as an abstract description of the entities of a system and how they are related (Whitney et al., 2004, p. 2; emphasis added) Now I want to draw the network architecture diagram for my research paper. Example is shown below: machine-learning neural-network deep-learning svm software-recommendation. Share. Improve this question. Follow edited Oct 25 '18 at 7:14. Franck Dernoncourt

Instead of simply assembling capsule modules, the capsule block in our proposed architecture is completely re-designed in order to suit time series problems and achieve state-of-the-art performance. Download : Download high-res image (271KB) Download : Download full-size image; Fig. 1. Schematic diagram of the proposed TSCaps approach たとえば、18層と34層を比較した結果が図3である。従来方式だと34層のほうが18層よりエラーが増えてしまっている。ResNetでは34層の方が良い結果となっている。 図3 学習エラーの比較。左:従来方式、右:ResNet. さらに多階層もO Oct 20, 2016 - Explore Miren Urena's board RedBluff on Pinterest. See more ideas about architecture, architecture design, diagram architecture Description. Edit. ResNet-50 is a deep convolutional network for classification. ResNet is a short form for Residual network and residual learning's aim was to solve image classifications. Residual Network learn from residuals instead of features. Paper. Deep Residual Learning for Image Recognition. Dataset The proposed model-1 in this paper employs a pre-trained ResNet-50 residual network architecture that has 177 layers in total. The dataset is divided into a 30:70 ratio with 30% of data used for training and 70% used for testing. The block diagram of the proposed ensemble of ResNet-50 and ECOC classifier is given in Fig. 2. For the first.

Original ResNet (left) — RoR approach (right) As can be seen from the classic ResNet model architecture, each blue block has a skip connection. In the RoR approach, new connections are added from the input to the output via the previous connections. There are different versions of RoR as in ResNet Because depending on the parameters like pruning threshold the architecture is getting changed, which might affect the accuracy. Does anyone has any ideas please share the pruning parameters used or Is this protected by Nvidia? Morganh. September 1, 2020, 7:02am #3. It is not protected..

How to Train a Custom Resnet34 Model for Image

E.g. ResNet-18 and ResNet-200 are both based on the ResNet architecture, but ResNet-200 is much deeper than ResNet-18 and is, therefore, more accurate. On the other hand, ResNet-18 is smaller and faster to run. There are a few problems with using very deep networks. They are difficult to train due to the vanishing gradient problem In a ResNet we're going to make a change to this we're gonna take a [l] and just fast forward it copies it much further into the neural network to before a [l+2]. just add al before applying the non-linearity and this the shortcut.. Shortcut Connections. Shortcut connection or Skip connections which allows you to take the activation from one layer and suddenly feed it to another layer Feb 13, 2015 - Explore Franshelys Hernandez's board parti diagrams, followed by 109 people on Pinterest. See more ideas about parti diagram, diagram architecture, concept architecture

Detailed Guide to Understand and Implement ResNets - CV

architecture view (aligned to the work of BIAN) of the vertical lines of the banking business and aligns those to a logical banking technology architecture leveraging platform and infrastructure services for on-premise and cloud deployments of banking application services. Figure 3: MIRA-B Document Flo Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers. 03/27/2019 ∙ by Jiahui Yu, et al. ∙ 2 ∙ share . We study how to set channel numbers in a neural network to achieve better accuracy under constrained resources (e.g., FLOPs, latency, memory footprint or model size) Here are a variety of pre-trained models for ImageNet classification. Accuracy is measured as single-crop validation accuracy on ImageNet. GPU timing is measured on a Titan X, CPU timing on an Intel i7-4790K (4 GHz) run on a single core. Using multi-threading with OPENMP should scale linearly with # of CPUs ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152 の5種類が提案されている。 いずれも上記の構成になっており、conv2_x, conv3_x, conv4_x, conv5_x の部分は residual block を以下で示すパラメータに従い、重ねたものとなっている

Location diagram • System Use-Case diagram Phase E. Opportunities & Solutions • Project Context diagram • Benefits diagram Phase C, Data Architecture • Data Entity/Data Component catalog • Data Entity/Business Function matrix • System/Data matrix • Class diagram • Data Dissemination diagram Phase D, Technology Architecture The graph is then rendered direct from gephi into an image. This is a directed graph of microsoft research ResNet-50 network used for image recognition. There are 4.36 million nodes and 9.52 million edges in the graph. If this is interesting then happy to post more. 7

http://www.gumroad.com/qreativehome for some free stuffhttp://www.instagram.com/qreativehomehttp://www.facebook.com/qreativehomehttp://www.pinterest.com/qrea.. This extremely deep ResNet-110 has 54 two-layer Residual Units (consisting of 3 \(\times \) 3 convolutional layers) and is challenging for optimization. Our implementation details (see appendix) are the same as . Throughout this paper we report the median accuracy of 5 runs for each architecture on CIFAR, reducing the impacts of random variations

ResNet may refer to: Residential network, a computer network provided by a university to serve residence halls. Residual flow network, in graph theory. Residual neural network, a type of artificial neural network. Residential Energy Services Network (RESNET), an organization responsible for home energy ratings. Topics referred to by the same term NVIDIA's complete solution stack, from hardware to software, allows data scientists to deliver unprecedented acceleration at every scale. Visit NVIDIA GPU Cloud (NGC) to pull containers and quickly get up and running with deep learning. Single GPU Training Performance of NVIDIA A100, A40, A30, A10, V100 and T4 5. Proposed Method 5.1. The Structure of the Proposed Network. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification for software-defined radio [].The architecture of the proposed network consists of stem network module, three Inception-ResNet modules, and. 29,263 recent views. In the Software Design and Architecture Specialization, you will learn how to apply design principles, patterns, and architectures to create reusable and flexible software applications and systems. You will learn how to express and document the design and architecture of a software system using a visual notation CRM System Architecture ( Entity Relationship Diagram) Use Creately's easy online diagram editor to edit this diagram, collaborate with others and export results to multiple image formats. We were unable to load the diagram. You can edit this template on Creately's Visual Workspace to get started quickly. Adapt it to suit your needs by.

Fast ResNet-34 architecture

Cloud TPU VM Architecture. Each TPU board is physically connected to a host machine (TPU Host). In a TPU Pod, there is a TPU host for each TPU board. How you interact with the TPU host (and the TPU board) depends upon the TPU VM architecture you are using: TPU Nodes or TPU VMs. TPU Nodes. TPU Nodes are the original TPU experience System architecture defines the structure of a software system. This is usually a series of diagrams that illustrate services, components, layers and interactions. A systems architecture document may also cover other elements of a solution including business architecture, technology architecture, security architecture and data architecture May 8, 2019 - Image 34 of 35 from gallery of Microsoft Milan / Flores & Prats. Photograph by Flores Prats. Pinterest. Today. Explore. Architecture Study Architecture Architecture Graphics Environmental Design Facade Architecture Sustainable Architecture Architecture Sketches Architecture Diagrams Residential Architecture

Introduction to ResNet in TensorFlow 2 - Adventures in

Publication-ready NN-architecture schematics. Download SVG. FCNN style LeNet style AlexNet style. Style: Edge width proportional to edge weights. Edge Width. Edge opacity proportional to edge weights. Edge Opacity. Edge color proportional to edge weights. Negative Edge Color. Positive Edge Color. Default Edge Color. Node Diameter PHY Interface for PCI Express, SATA, USB 3.1, DisplayPort, and Converged IO Architectures, ver 5.

Understanding and Implementing Architectures of ResNet and

The architecture is the most fundamental aspect of software. You will learn how development teams describe architectures, plan successful architectures based on quality attributes, and evaluate the resulting architecture. You will also learn how architecture relates to organization structure and even product planning Korbyt Architecture Diagrams. A high level overview of the Korbyt Cloud architecture is available below: Failed to fetch Error: URL to the PDF file must be on exactly the same domain as the current web page. Click here for more info C++ Java and C# class headers are synchronized between diagrams and code in real-time Programmer's workbenches, documentation tools, version control systems Supports following UML diagrams: Use case diagram, Sequence diagram, Collaboration diagram, Class diagram, Statechart diagram, Activity diagram, Component diagram, Deployment diagram and.

Structure of ResNet50 [12] | Download Scientific Diagram

Common architectures in convolutional neural networks

Inception v3 architecture (Source). Convolutional neural networks are a type of deep learning neural network. These types of neural nets are widely used in computer vision and have pushed the capabilities of computer vision over the last few years, performing exceptionally better than older, more traditional neural networks; however, studies. A Deep Dive Into the Transformer Architecture - The Development of Transformer Models. Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to.

Figure 10. Block diagram of Jetson Xavier CPU complex with NVIDIA Carmel clusters. The Carmel CPU cores feature NVIDIA's Dynamic Code Optimization, a 10-way superscalar architecture, and a full implementation of ARMv8.2 including full Advanced SIMD, VFP (Vector Floating Point), and ARMv8.2-FP16 use on premise. Please visit IBM Architecture Center for the architecture for these offerings. The VMware HCX on IBM Cloud service takes this hybridity to the next step, blending instances of either VCS or VCF with existing on-premises virtualized datacenters by enabling the creation of seamles A superb visual reference to the principles of architecture. Now including interactive CD-ROM! For more than thirty years, the beautifully illustrated Architecture: Form, Space, and Order has been the classic introduction to the basic vocabulary of architectural design. The updated Third Edition features expanded sections on circulation, light, views, and site context, along with new. Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs. Bestseller. 11 total hoursUpdated 7/2021. Rating: 4.5 out of 1. 4.5 41,117. PyTorch for Deep Learning with Python Bootcamp

Inception-v3 Architecture (Batch Norm and ReLU are used after Conv) With 42 layers deep, the computation cost is only about 2.5 higher than that of GoogLeNet [4], and much more efficient than that of VGGNet [3]. The links I use for reference about the architecture Office Floor Plan 15x17. Edit this example. Office Floor Plan 23x20. Edit this example. Office Floor Plan 20x11. Edit this example. Office Floor Plan 14x13. Edit this example. Office Floor Plan 11x13 Visio is a diagraming tool that makes it easy and intuitive to create flowcharts, diagrams, org charts, floor plans, engineering designs, and more, using modern templates with the familiar Office experience. On this page, you can access some of the top templates and sample diagrams available in Visio, or request ones that you want. To see the hundreds of templates and sample diagrams available. We notice that the LDS variant achieved 0.7% higher top-n accuracy and 0.9% higher top-(n + 2) accuracy than bottleneck ResNet-34, making it preferable as a lightweight architecture. Comparing now to the baseline ResNet-18 variant, although bottleneck LDS-ResNet-18 has more than 10 times fewer parameters, it achieves similar, if not better, CV. Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress

ResNet PyTorc

This study uses 4 Mask R-CNN models, which is influenced by the lighting on polarizing microscope and using ResNet-50 architecture (Model A) or ResNet-101 (Model B), and the models that is not affected by the lighting on polarizing microscope and using ResNet-50 architecture (Model C) or ResNet-101 (Model D)

top-1 accuracy for ResNet-18/34/50The performance of AlexNet, Vgg-16, ResNet-18, Resnet-34Deep High Resolution Net (HRNet) code analysis and networkStarNEt architecture, using ResNet (in our implementation