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Computed Tomography Organ and Disease Segmentation Using the NVIDIA VISTA-3D NIM Microservice | NVIDIA Technical Blog
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</span> <span class="post-rate-widget content-s"></span> <span class="post-lang-switcher"> <div class="posts-filter-form"><div class="filter-item form-type-select"><select class="msls_languages"><option value="https://developer.nvidia.com/blog/computed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim/" selected>English</option><option value="https://developer.nvidia.com/zh-cn/blog/computed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim/">中文</option></select></div></div> </span> </div> <h1 class="h--large txt-clr--blck mt-2 mb-0">Computed Tomography Organ and Disease Segmentation Using the NVIDIA VISTA-3D NIM Microservice</h1> <div class="post-info"> <div class="post-published-date"> Jul 26, 2024 </div> <div class="post-authors"> By <a href="https://developer.nvidia.com/blog/author/ahmedharouni/" title="Posts by Ahmed Harouni" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Ahmed Harouni</a> and <a href="https://developer.nvidia.com/blog/author/mzephyr/" title="Posts by Michael Zephyr" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Michael Zephyr</a> </div> <div class="card--post-attributes-secondary card--post-attributes-header"> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="85863" data-ulike-nonce="402226ddf3" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" data-ulike-likers-style="popover" class="wp_ulike_btn wp_ulike_put_text wp_post_btn_85863"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+10"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> <div class="post--comments-count secondary--attribute"> <a href="#entry-content-comments"> <i class="fad fa-comment-alt-lines"></i> Discuss (0) </a> </div> </div> </div> <img width="828" height="466" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured.jpg" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Three CT scan segments on a black background." decoding="async" fetchpriority="high" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured.jpg 828w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-300x169.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-625x352.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-179x101.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-768x432.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-645x363.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-500x281.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-160x90.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-362x204.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/monai-vista3d-nim-featured-195x110.jpg 195w" sizes="(max-width: 828px) 100vw, 828px" /> </div> </div> </div> </div> <div class="col-lg-3 col-md-3 mt-0 mb-0"></div> <div class="main-content col-lg-9 col-md-9 mt-0"> <div class="entry-meta-social"> <ul class="entry-meta-social-links-list"> <li><a data-wpel-link="external" href="https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fcomputed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim%2F" class="for-linkedin" target="_blank" rel="follow external noopener">L</a></li> <li><a data-wpel-link="external" href="https://twitter.com/intent/tweet?text=Computed+Tomography+Organ+and+Disease+Segmentation+Using+the+NVIDIA+VISTA-3D+NIM+Microservice+%7C+NVIDIA+Technical+Blog+https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fcomputed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim%2F" class="for-twitter" target="_blank" rel="follow external noopener">T</a></li> <li><a data-wpel-link="external" href="https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fcomputed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim%2F" class="for-facebook" target="_blank" rel="follow external noopener">F</a></li> <li><a data-wpel-link="external" href="https://www.reddit.com/submit?url=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fcomputed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim%2F&title=Computed+Tomography+Organ+and+Disease+Segmentation+Using+the+NVIDIA+VISTA-3D+NIM+Microservice+%7C+NVIDIA+Technical+Blog" class="for-reddit" target="_blank" rel="follow external noopener">R</a></li> <li><a href="mailto:?subject=I'd like to share a link with you&body=https%3A%2F%2Fdeveloper.nvidia.com%2Fblog%2Fcomputed-tomography-organ-and-disease-segmentation-using-the-nvidia-vista-3d-nim%2F" class="for-mail">E</a></li> </ul> </div> <div class="entry-content"> <p>Over 300M computed tomography (CT) scans are performed globally, 85M in the US alone. Radiologists are looking for ways to speed up their workflow and generate accurate reports, so having a foundation model to segment all organs and diseases would be helpful. Ideally, you’d have an optimized way to run this model in production at scale. </p> <p>NVIDIA Research has created a new foundation model to segment full-body CT images and has packaged it into a highly optimized container that can scale in deployment. In this post, we discuss the VISTA-3D foundation model, <a href="https://www.nvidia.com/en-us/ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA NIM</a>, and how to run the model on your data.</p> <h2 id="vista-3d " class="wp-block-heading">VISTA-3D <a href="#vista-3d " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>Vision foundation models have attracted increasing interest. In the context of medical image analysis, two essential features make these models particularly practical: </p> <ul class="wp-block-list"> <li>Fast and precise inference for common tasks</li> <li>Effective adaptation or zero-shot ability to novel tasks</li> </ul> <p>NVIDIA has been focusing on 3D CT segmentation and recently developed<a href="https://arxiv.org/abs/2406.05285" data-wpel-link="exclude"> VISTA-3D (Versatile Imaging SegmenTation and Annotation</a>). The model is trained systematically on more than 12K volumes encompassing 127 types of human anatomical structures and various lesions (lung nodules, liver tumors, pancreatic tumors, colon tumors, bone lesions, and kidney tumors). </p> <p>It provides accurate out-of-box segmentation as well as state-of-the-art, zero-shot interactive segmentation. The novel model design and training recipe represent a promising step toward developing a versatile medical image foundation model. </p> <p>VISTA-3D is a domain-specialized interactive foundation model that combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. It has the following core workflows: </p> <ul class="wp-block-list"> <li><strong>Segment everything:</strong> Enables whole body exploration, crucial for understanding complex diseases affecting multiple organs and for holistic treatment planning. </li> <li><strong>Segment using class:</strong> Provides detailed sectional views based on specific classes, essential for targeted disease analysis or organ mapping, such as tumor identification in critical organs. </li> <li><strong>Segment point prompts:</strong> Enhances segmentation precision through user-directed, click-based selection. This interactive approach accelerates the creation of accurate ground-truth data, essential in medical imaging analysis. </li> </ul> <p>Figure 1 shows a high-level diagram of the VISTA-3D architecture, with an encoder layer followed by two decoder layers in parallel. One decoder is for the automatic segmentation while the other decoder is for the point prompt. </p> <p>Each head takes in corresponding input as class prompts or point clicks by the user to guide the segmentation. Each head also results in its own segmentation results, which are then combined into the final segmentation result using a merging algorithm. </p> <p>For more information about the architecture, see the <a href="https://arxiv.org/abs/2406.05285" data-wpel-link="exclude">VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography</a> research paper.</p> <div class="wp-block-image"> <figure class="aligncenter size-full-page-width"><a href="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture.png" data-wpel-link="internal" target="_blank" rel="follow noopener"><img decoding="async" width="1024" height="351" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-1024x351.png" alt="Diagram shows that the input image goes through an encoder and then two different decoders, one for automatic segmentation and the other for point segmentation." class="wp-image-85868" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-1024x351.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-300x103.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-625x214.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-179x61.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-768x263.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-645x221.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-500x172.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-160x55.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-362x124.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture-321x110.png 321w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/vista-3d-architecture.png 1271w" sizes="(max-width: 1024px) 100vw, 1024px" /></a><figcaption class="wp-element-caption"><em>Figure 1. High-level network architecture for VISTA-3D</em></figcaption></figure></div> <h2 id="vista-3d_nim microservice" class="wp-block-heading">VISTA-3D NIM microservice<a href="#vista-3d_nim microservice" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>All NVIDIA NIM microservices are hosted on the <a href="https://build.nvidia.com" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA API Catalog</a> for you to test out different microservices and find out about their capabilities. </p> <p>Find VISTA-3D under <strong>Healthcare</strong> and test it out with sample data. View the test dataset in either axial and coronal or sagittal view. VISTA-3D can segment over 100 organs, or you can select specific classes of interest. </p> <h2 id="using_nim_microservices_hosted_by_nvidia " class="wp-block-heading">Using NIM microservices hosted by NVIDIA <a href="#using_nim_microservices_hosted_by_nvidia " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>You can run VISTA-3D on your data using the NIM microservice hosted by NVIDIA. Sign up to <a href="https://build.nvidia.com/explore/healthcare?signin=true" data-wpel-link="internal" target="_blank" rel="follow noopener">get a personal key</a>. NVIDIA gives users 1000 free credits to try out any of the NIM microservices. </p> <p>Give your personal key a name, and an expiration date. Include <strong>AI Foundation Models and Endpoints</strong> in the services included. You use this key for all API calls. For more information, see <a href="https://docs.nvidia.com/launchpad/ai/base-command-coe/latest/bc-coe-docker-basics-step-02.html#optionally-generate-an-ngc-key" data-wpel-link="internal" target="_blank" rel="follow noopener">Optionally Generate an NGC Key</a>. </p> <p>To check on your credits, log in to the <a href="https://build.nvidia.com" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA API Catalog</a> and check your profile. Your credits are listed at the upper right.</p> <p>To test your first VISTA-3D NIM microservice call, copy the code of your favorite language (shell script, Python, or Node.JS) and use the recently generated <code>API_KEY</code> value to run inference on the same sample that you used on the NVIDIA AI solution page, similar to the following example in Python. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> import requests invoke_url = "https://health.api.nvidia.com/v1/medicalimaging/nvidia/vista-3d" headers = {"Authorization": "Bearer <place you key here it should start with nvapi-> ",} filename="example-1" payload = { "image": f"https://assets.ngc.nvidia.com/products/api-catalog/vista3d/{filename}.nii.gz", "output": {"extension": ".nii.gz","dtype": "uint8"}} session = requests.Session() response = session.post(invoke_url, headers=headers, json=payload) response.raise_for_status() file_name=f"{filename}_seg.nii.gz" with open(file_name, "wb") as file: file.write(response.content) print("File downloaded successfully!") </pre></div> <p>Congratulations, you just ran your first VISTA-3D NIM microservice call on the sample data. This also confirms that your <code>API_KEY</code> value works and you have enough credits, so you are now ready to test with your own data. </p> <p>Each inference call uses one credit and your credits are shared across all NIM microservices. </p> <h2 id="running_vista-3d_with_your_data " class="wp-block-heading">Running VISTA-3D with your data <a href="#running_vista-3d_with_your_data " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>To see VISTA-3D inference on your own data, you must set up an FTP server to serve your medical images. </p> <p>Unlike LLM models that take a compressible small text payload, medical images are typically large. Instead of sending large images in the API payload, you send the image URL to the FTP server. The VISTA-3D NIM microservice then downloads the images from the FTP server and runs inference before sending back the inference result (Figure 2). </p> <div class="wp-block-image"> <figure class="aligncenter size-full"><img decoding="async" width="904" height="398" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1.png" alt="Diagram shows that the LLM NIM takes the text directly in the payload, while the VISTA-3D NIM takes the URL to the image so it is downloaded from the HTTP server." class="wp-image-86120" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1.png 904w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-300x132.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-625x275.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-179x79.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-768x338.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-645x284.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-500x220.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-160x70.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-362x159.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/07/comparison-llm-and-vista-3d-nims-1-250x110.png 250w" sizes="(max-width: 904px) 100vw, 904px" /><figcaption class="wp-element-caption"><em>Figure 2. Difference between LLM and VISTA-3D NIM microservice</em></figcaption></figure></div> <h3 id="share_files_on_github " class="wp-block-heading">Share files on GitHub <a href="#share_files_on_github " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>The simplest and fastest way to share sample data is to use GitHub:</p> <ol class="wp-block-list"> <li>Log into your GitHub account.</li> <li>Create a new project and make sure that it is public. </li> <li>Choose <strong>Upload file</strong> and upload some NIfTI files with .nii or .nii.gz. Make sure that the size of each is <25 MB. </li> <li>Select your NIfTI file, right-click <strong>Raw</strong>, and then choose <strong>Copy link address</strong>. </li> </ol> <p>The resulting URL should look something like the following example:</p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: plain; title: ; notranslate" title=""> https://github.com/<your_user_name>/Nifti_samples/raw/main/filename.nii.gz </pre></div> <p>You can now change the URL in the earlier code example to point to your file and VISTA-3D downloads it. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> payload = { "image": f"https://github.com/<your_user_name>/Nifti_samples/raw/main/filename.nii.gz", "output": {"extension": ".nii.gz","dtype": "uint8"}} </pre></div> <h3 id="share_your_sample_data_in_the_cloud " class="wp-block-heading">Share your sample data in the cloud <a href="#share_your_sample_data_in_the_cloud " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>If you have some data already in the cloud, you can expose a small number of NIfTI images to test VISTA-3D. In that case, start a simple NGINX server or a simple Python HTTP server from your local file system directory. This must be a publicly accessible server or port so that the VISTA-3D microservice hosted by NVIDIA can download the data. </p> <p>First, connect to the server using SSH, move some sample data, and then start a simple Python HTTP file server:</p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> python -m http.server <port> </pre></div> <p>You should be able to access the files from a browser using <code>htttp://serverIP:port/</code>. </p> <p>Connect to the FTP server created earlier and change the lines to point to your HTTP server. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> filename="<file_on_http" payload = { "image": f"http://<your server ip:port /{filename}.nii.gz", </pre></div> <p>You should see the HTTP file server responding to the NIM request to download the image file and then you will get the inference result. </p> <h2 id="running_nim_microservices_locally " class="wp-block-heading">Running NIM microservices locally <a href="#running_nim_microservices_locally " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>To get started running NIM microservices locally, <a href="https://www.nvidia.com/en-us/ai/nim-notifyme/" data-wpel-link="internal" target="_blank" rel="follow noopener">apply for NVIDIA NIM access</a>. After you’ve applied, the NVIDIA team will contact you to schedule an onboarding meeting. You’ll need an NVAIE license or a trial license, but it’s important to wait for instructions before applying for a license. </p> <p>After completing these steps and receiving approval, you’ll gain access to the VISTA-3D NIM microservice Docker container. This enables you to run the microservice on your preferred hardware, whether locally or in the cloud. </p> <p>In the following sections, we show you an example setup using Docker Compose to help you get up and running quickly.</p> <h3 id="prerequisites " class="wp-block-heading">Prerequisites <a href="#prerequisites " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>You should have the Docker, Docker Compose, and NVIDIA drivers installed. To check, run the following command, which pulls a small image of the latest CUDA release, starts the Docker container, and then checks that you can access the GPU from within the container. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> docker run --rm --gpus all nvidia/cuda: 12.5.0-base-ubuntu20.04 nvidia-smi </pre></div> <p> You should see an output showing the GPUs that you have on your system. </p> <h3 id="docker_compose_file " class="wp-block-heading">Docker Compose file <a href="#docker_compose_file " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>The Docker Compose code example starts two containers: </p> <ul class="wp-block-list"> <li><code>nim-vista</code> to run inference </li> <li><code>nim-nginx</code> to serve your images</li> </ul> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> # docker-compose.yml version: "3.9" services: nim: container_name: nim-vista image: nvcr.io/nvidia/nim/medical_imaging_vista3d:24.03 ports: - 8008:8008 ############################## nginx: container_name: nim-nginx image: nginx:1.19-alpine-perl volumes: - ./nginx.conf:/etc/nginx/nginx.conf - <local/folder/containing/nifti/files>:/files ports: - 8009:8009 </pre></div> <p>Change the directory to <code><local/folder/containing/nifti/files></code>. </p> <h3 id="nginx_basic_configuration " class="wp-block-heading">Nginx basic configuration <a href="#nginx_basic_configuration " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>In the same folder, create the <code>nginx.conf</code> file: </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> worker_processes auto; pid /etc/nginx/.nginx.pid; events { worker_connections 768; } http { server { listen 8009; # internal port root /files; # Static file directory autoindex on; # Enable directory listing location / { try_files $uri $uri/ = 404; } } } </pre></div> <h3 id="run_the_inference_call " class="wp-block-heading">Run the inference call <a href="#run_the_inference_call " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>To confirm that your setup is correct, run the following curl command: </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> curl http://localhost:8008/health/ready </pre></div> <p>This command should return true, indicating that the VISTA-3D NIM microservice is up and running. </p> <p>To confirm that <code>nginx</code> is serving your data correctly, on your browser, check<a href="http://localhost:8009/" data-wpel-link="external" target="_blank" rel="follow external noopener"> http://localhost:8009</a> to see the folders and files that you are sharing.</p> <p>Now you can reuse the same code from earlier to do inference, with the minor change of pointing to <code>nginx</code> for serving your data, instead of pointing to NVIDIA sample data or your sample files on GitHub. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; highlight: [3,6,8]; title: ; notranslate" title=""> import requests invoke_url = “http://localhost:8008/vista3d/inference” headers = {} filename="test_w_nginx" payload = { "image": f"https://localhost:8809/<file Path as you it shows in nginx webpage>.nii.gz", "output": {"extension": ".nii.gz","dtype": "uint8"}} session = requests.Session() response = session.post(invoke_url, headers=headers, json=payload) response.raise_for_status() file_name=f"{filename}_seg.nii.gz" with open(file_name, "wb") as file: file.write(response.content) print("File downloaded successfully!") </pre></div> <p>The <code>invoke_url</code> value now points to your host and the headers are empty as you don’t need to authenticate using the NGC key. </p> <p>After this runs, you should have the organs segmentation file with all the labels. </p> <h3 id="security_tip " class="wp-block-heading">Security tip <a href="#security_tip " class="heading-anchor-link"><i class="fas fa-link"></i></a></h3> <p>When you get this running, you probably don’t want to have the NGINX port open serving your data to anyone with access to the host. </p> <p>Instead, limit access to VISTA-3D NIM microservice. Edit the <code>docker-compose.yml</code> file and remove the port mapping of <code>nginx</code> by deleting the lines for <code>ports: - 8009:8009</code>. </p> <p>Next, change the infer call to use <code>nim-nginx</code> instead of <code>localhost</code>, which is not accessible anymore. </p> <div class="wp-block-syntaxhighlighter-code "><pre class="brush: python; title: ; notranslate" title=""> "image":fhttps://nim-nginx:8809/{filename}.nii.gz </pre></div> <p>The internal name <code>nim-nginx</code> is accessible to NIM as Docker compose places all services in the same network. </p> <h2 id="conclusion " class="wp-block-heading">Conclusion <a href="#conclusion " class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>In this post, we introduced the new NVIDIA AI Foundation model, VISTA-3D, which can segment over 100 organs and multiple diseases in a CT image. We also showed you how NVIDIA NIM simplifies using this model.</p> <p>Interested in enhancing your CT image analysis? <a href="https://www.nvidia.com/en-us/ai/nim-notifyme/" data-wpel-link="internal" target="_blank" rel="follow noopener">Apply for access to the VISTA-3D NIM microservice</a>. After being approved, you can run this powerful model on your own hardware, improving your segmentation accuracy and streamlining your workflow.</p> </div> <div class="block--prospero-assets"> <h2 class="h--smaller txt-clr--blck">Related resources</h2> <ul><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S62320&ncid=em-even-124008-vt33-23spring#/'>Learn how to Streamline Medical Imaging Annotation and Training workflows with MONAI Cloud APIs</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S62504&ncid=em-even-124008-vt33-23spring#/'>Optimizing Your AI Strategy to Develop and Deploy Novel Deep Learning Models in the Cloud for Medical Image Analysis</a></li><li>NGC Containers: <a class='wpel-ignore' target='_blank' href='https://catalog.ngc.nvidia.com/orgs/nim/teams/deepmind/containers/alphafold2?ncid=em-nurt-245273-vt33'>AlphaFold2</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://build.nvidia.com/nvidia/vista-3d?ncid=em-nurt-245273-vt33'>VISTA-3D NIM</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://build.nvidia.com/nvidia/vista-3d?ncid=em-nurt-245273-vt33'>Embed QA-4 NIM</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://docs.nvidia.com/clara-viz/?ncid=em-nurt-245273-vt33'>Clara Viz</a></li></ul> </div> <div class="card--post-attributes-secondary"> <div class="post--comments-count secondary--attribute"> <a href="#entry-content-comments"> <i class="fad fa-comment-alt-lines"></i> Discuss (0) </a> </div> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="85863" data-ulike-nonce="402226ddf3" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" data-ulike-likers-style="popover" class="wp_ulike_btn wp_ulike_put_text wp_post_btn_85863"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+10"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> </div> <div class="card--post-attributes-secondary tags"> <div class="caption"> <h2 class="h--smaller txt-clr--blck">Tags</h2> </div> <div class="content-s post-tags--list mt-0"> <a href="https://developer.nvidia.com/blog/category/computer-vision/" data-wpel-link="internal" target="_blank" rel="follow noopener">Computer Vision / Video Analytics</a> | <a href="https://developer.nvidia.com/blog/category/data-center-cloud/" data-wpel-link="internal" target="_blank" rel="follow noopener">Data Center / Cloud</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?industry=Healthcare+%26+Life+Sciences" data-wpel-link="internal" target="_blank" rel="follow noopener">Healthcare & Life Sciences</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=NIM" data-wpel-link="internal" target="_blank" rel="follow noopener">NIM</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?learning_levels=Intermediate+Technical" data-wpel-link="internal" target="_blank" rel="follow noopener">Intermediate Technical</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?content_types=Deep+dive" data-wpel-link="internal" target="_blank" rel="follow noopener">Deep dive</a> | <a href="https://developer.nvidia.com/blog/tag/featured/" data-wpel-link="internal" target="_blank" rel="follow noopener">featured</a> | <a href="https://developer.nvidia.com/blog/tag/image-segmentation/" data-wpel-link="internal" target="_blank" rel="follow noopener">Instance / Semantic Segmentation</a> </div> </div> <div class="post-authors-list"> <div class="entry-content-author"> <div class="caption"> <h2 class="h--smaller txt-clr--blck"> About the Authors </h2> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2021/06/ahmed-Harouni_headshot-square-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2021/06/ahmed-Harouni_headshot-square-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Ahmed Harouni </b> <br/> Ahmed Harouni is a Deep Learning Solutions Architect for medical imaging at NVIDIA. Ahmed received his PhD in Electrical and Computer Engineering from Johns Hopkins University focusing on MR image acquisition and image processing. Recently he has been using deep-learning for image segmentation, registration, disease classification and localization. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/ahmedharouni/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Ahmed Harouni<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2021/04/Michael-Zephyr-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2021/04/Michael-Zephyr-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Michael Zephyr </b> <br/> Michael Zephyr is a developer evangelist on the NVIDIA Clara team and is responsible for helping design the developer experience for all NVIDIA Clara products. He develops and presents content through blog posts, webinars, and conferences that help developers quickly get started. Michael received his master's in computer science specializing in machine learning and interactive intelligence from Georgia Tech University. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/mzephyr/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Michael Zephyr<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> </div> </div> <div class="entry-content-comments" id="entry-content-comments"> <div class="container" id="pf-disqus-thread"> <div class="row" id="respond"> <div class="col-md-12 related-posts-comments mb-0"><h2 class="h--smaller txt-clr--blck mb-0">Comments</h2></div> </div> <div class="row"> <div class="col-lg-12 col-md-12 col-sm-12 col-xs-12"> <div class="wpdc-comments-loading" 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T-I9105|GT-I8510|GT-S6790N|SM-G7105|SM-N9005|GT-S5301|GT-I9295|GT-I9195|SM-C101|GT-S7392|GT-S7560|GT-B7610|GT-I5510|GT-S7582|GT-S7530E|GT-I8750|SM-G9006V|SM-G9008V|SM-G9009D|SM-G900A|SM-G900D|SM-G900F|SM-G900H|SM-G900I|SM-G900J|SM-G900K|SM-G900L|SM-G900M|SM-G900P|SM-G900R4|SM-G900S|SM-G900T|SM-G900V|SM-G900W8|SHV-E160K|SCH-P709|SCH-P729|SM-T2558|GT-I9205|SM-G9350|SM-J120F|SM-G920F|SM-G920V|SM-G930F|SM-N910C|SM-A310F|GT-I9190|SM-J500FN|SM-G903F|SM-J330F|SM-G610F|SM-G981B|SM-G892A|SM-A530F",LG:"\\bLG\\b;|LG[- ]?(C800|C900|E400|E610|E900|E-900|F160|F180K|F180L|F180S|730|855|L160|LS740|LS840|LS970|LU6200|MS690|MS695|MS770|MS840|MS870|MS910|P500|P700|P705|VM696|AS680|AS695|AX840|C729|E970|GS505|272|C395|E739BK|E960|L55C|L75C|LS696|LS860|P769BK|P350|P500|P509|P870|UN272|US730|VS840|VS950|LN272|LN510|LS670|LS855|LW690|MN270|MN510|P509|P769|P930|UN200|UN270|UN510|UN610|US670|US740|US760|UX265|UX840|VN271|VN530|VS660|VS700|VS740|VS750|VS910|VS920|VS930|VX9200|VX11000|AX840A|LW770|P506|P925|P999|E612|D955|D802|MS323|M257)|LM-G710",Sony:"SonyST|SonyLT|SonyEricsson|SonyEricssonLT15iv|LT18i|E10i|LT28h|LT26w|SonyEricssonMT27i|C5303|C6902|C6903|C6906|C6943|D2533|SOV34|601SO|F8332",Asus:"Asus.*Galaxy|PadFone.*Mobile",Xiaomi:"^(?!.*\\bx11\\b).*xiaomi.*$|POCOPHONE F1|MI 8|Redmi Note 9S|Redmi Note 5A Prime|N2G47H|M2001J2G|M2001J2I|M1805E10A|M2004J11G|M1902F1G|M2002J9G|M2004J19G|M2003J6A1G",NokiaLumia:"Lumia [0-9]{3,4}",Micromax:"Micromax.*\\b(A210|A92|A88|A72|A111|A110Q|A115|A116|A110|A90S|A26|A51|A35|A54|A25|A27|A89|A68|A65|A57|A90)\\b",Palm:"PalmSource|Palm",Vertu:"Vertu|Vertu.*Ltd|Vertu.*Ascent|Vertu.*Ayxta|Vertu.*Constellation(F|Quest)?|Vertu.*Monika|Vertu.*Signature",Pantech:"PANTECH|IM-A850S|IM-A840S|IM-A830L|IM-A830K|IM-A830S|IM-A820L|IM-A810K|IM-A810S|IM-A800S|IM-T100K|IM-A725L|IM-A780L|IM-A775C|IM-A770K|IM-A760S|IM-A750K|IM-A740S|IM-A730S|IM-A720L|IM-A710K|IM-A690L|IM-A690S|IM-A650S|IM-A630K|IM-A600S|VEGA PTL21|PT003|P8010|ADR910L|P6030|P6020|P9070|P4100|P9060|P5000|CDM8992|TXT8045|ADR8995|IS11PT|P2030|P6010|P8000|PT002|IS06|CDM8999|P9050|PT001|TXT8040|P2020|P9020|P2000|P7040|P7000|C790",Fly:"IQ230|IQ444|IQ450|IQ440|IQ442|IQ441|IQ245|IQ256|IQ236|IQ255|IQ235|IQ245|IQ275|IQ240|IQ285|IQ280|IQ270|IQ260|IQ250",Wiko:"KITE 4G|HIGHWAY|GETAWAY|STAIRWAY|DARKSIDE|DARKFULL|DARKNIGHT|DARKMOON|SLIDE|WAX 4G|RAINBOW|BLOOM|SUNSET|GOA(?!nna)|LENNY|BARRY|IGGY|OZZY|CINK FIVE|CINK PEAX|CINK PEAX 2|CINK SLIM|CINK SLIM 2|CINK +|CINK KING|CINK PEAX|CINK SLIM|SUBLIM",iMobile:"i-mobile (IQ|i-STYLE|idea|ZAA|Hitz)",SimValley:"\\b(SP-80|XT-930|SX-340|XT-930|SX-310|SP-360|SP60|SPT-800|SP-120|SPT-800|SP-140|SPX-5|SPX-8|SP-100|SPX-8|SPX-12)\\b",Wolfgang:"AT-B24D|AT-AS50HD|AT-AS40W|AT-AS55HD|AT-AS45q2|AT-B26D|AT-AS50Q",Alcatel:"Alcatel",Nintendo:"Nintendo (3DS|Switch)",Amoi:"Amoi",INQ:"INQ",OnePlus:"ONEPLUS",GenericPhone:"Tapatalk|PDA;|SAGEM|\\bmmp\\b|pocket|\\bpsp\\b|symbian|Smartphone|smartfon|treo|up.browser|up.link|vodafone|\\bwap\\b|nokia|Series40|Series60|S60|SonyEricsson|N900|MAUI.*WAP.*Browser"},tablets:{iPad:"iPad|iPad.*Mobile",NexusTablet:"Android.*Nexus[\\s]+(7|9|10)",GoogleTablet:"Android.*Pixel C",SamsungTablet:"SAMSUNG.*Tablet|Galaxy.*Tab|SC-01C|GT-P1000|GT-P1003|GT-P1010|GT-P3105|GT-P6210|GT-P6800|GT-P6810|GT-P7100|GT-P7300|GT-P7310|GT-P7500|GT-P7510|SCH-I800|SCH-I815|SCH-I905|SGH-I957|SGH-I987|SGH-T849|SGH-T859|SGH-T869|SPH-P100|GT-P3100|GT-P3108|GT-P3110|GT-P5100|GT-P5110|GT-P6200|GT-P7320|GT-P7511|GT-N8000|GT-P8510|SGH-I497|SPH-P500|SGH-T779|SCH-I705|SCH-I915|GT-N8013|GT-P3113|GT-P5113|GT-P8110|GT-N8010|GT-N8005|GT-N8020|GT-P1013|GT-P6201|GT-P7501|GT-N5100|GT-N5105|GT-N5110|SHV-E140K|SHV-E140L|SHV-E140S|SHV-E150S|SHV-E230K|SHV-E230L|SHV-E230S|SHW-M180K|SHW-M180L|SHW-M180S|SHW-M180W|SHW-M300W|SHW-M305W|SHW-M380K|SHW-M380S|SHW-M380W|SHW-M430W|SHW-M480K|SHW-M480S|SHW-M480W|SHW-M485W|SHW-M486W|SHW-M500W|GT-I9228|SCH-P739|SCH-I925|GT-I9200|GT-P5200|GT-P5210|GT-P5210X|SM-T311|SM-T310|SM-T310X|SM-T210|SM-T210R|SM-T211|SM-P600|SM-P601|SM-P605|SM-P900|SM-P901|SM-T217|SM-T217A|SM-T217S|SM-P6000|SM-T3100|SGH-I467|XE500|SM-T110|GT-P5220|GT-I9200X|GT-N5110X|GT-N5120|SM-P905|SM-T111|SM-T2105|SM-T315|SM-T320|SM-T320X|SM-T321|SM-T520|SM-T525|SM-T530NU|SM-T230NU|SM-T330NU|SM-T900|XE500T1C|SM-P605V|SM-P905V|SM-T337V|SM-T537V|SM-T707V|SM-T807V|SM-P600X|SM-P900X|SM-T210X|SM-T230|SM-T230X|SM-T325|GT-P7503|SM-T531|SM-T330|SM-T530|SM-T705|SM-T705C|SM-T535|SM-T331|SM-T800|SM-T700|SM-T537|SM-T807|SM-P907A|SM-T337A|SM-T537A|SM-T707A|SM-T807A|SM-T237|SM-T807P|SM-P607T|SM-T217T|SM-T337T|SM-T807T|SM-T116NQ|SM-T116BU|SM-P550|SM-T350|SM-T550|SM-T9000|SM-P9000|SM-T705Y|SM-T805|GT-P3113|SM-T710|SM-T810|SM-T815|SM-T360|SM-T533|SM-T113|SM-T335|SM-T715|SM-T560|SM-T670|SM-T677|SM-T377|SM-T567|SM-T357T|SM-T555|SM-T561|SM-T713|SM-T719|SM-T813|SM-T819|SM-T580|SM-T355Y?|SM-T280|SM-T817A|SM-T820|SM-W700|SM-P580|SM-T587|SM-P350|SM-P555M|SM-P355M|SM-T113NU|SM-T815Y|SM-T585|SM-T285|SM-T825|SM-W708|SM-T835|SM-T830|SM-T837V|SM-T720|SM-T510|SM-T387V|SM-P610|SM-T290|SM-T515|SM-T590|SM-T595|SM-T725|SM-T817P|SM-P585N0|SM-T395|SM-T295|SM-T865|SM-P610N|SM-P615|SM-T970|SM-T380|SM-T5950|SM-T905|SM-T231|SM-T500|SM-T860",Kindle:"Kindle|Silk.*Accelerated|Android.*\\b(KFOT|KFTT|KFJWI|KFJWA|KFOTE|KFSOWI|KFTHWI|KFTHWA|KFAPWI|KFAPWA|WFJWAE|KFSAWA|KFSAWI|KFASWI|KFARWI|KFFOWI|KFGIWI|KFMEWI)\\b|Android.*Silk/[0-9.]+ like Chrome/[0-9.]+ (?!Mobile)",SurfaceTablet:"Windows NT [0-9.]+; ARM;.*(Tablet|ARMBJS)",HPTablet:"HP Slate (7|8|10)|HP ElitePad 900|hp-tablet|EliteBook.*Touch|HP 8|Slate 21|HP SlateBook 10",AsusTablet:"^.*PadFone((?!Mobile).)*$|Transformer|TF101|TF101G|TF300T|TF300TG|TF300TL|TF700T|TF700KL|TF701T|TF810C|ME171|ME301T|ME302C|ME371MG|ME370T|ME372MG|ME172V|ME173X|ME400C|Slider SL101|\\bK00F\\b|\\bK00C\\b|\\bK00E\\b|\\bK00L\\b|TX201LA|ME176C|ME102A|\\bM80TA\\b|ME372CL|ME560CG|ME372CG|ME302KL| K010 | K011 | K017 | K01E |ME572C|ME103K|ME170C|ME171C|\\bME70C\\b|ME581C|ME581CL|ME8510C|ME181C|P01Y|PO1MA|P01Z|\\bP027\\b|\\bP024\\b|\\bP00C\\b",BlackBerryTablet:"PlayBook|RIM Tablet",HTCtablet:"HTC_Flyer_P512|HTC Flyer|HTC Jetstream|HTC-P715a|HTC EVO View 4G|PG41200|PG09410",MotorolaTablet:"xoom|sholest|MZ615|MZ605|MZ505|MZ601|MZ602|MZ603|MZ604|MZ606|MZ607|MZ608|MZ609|MZ615|MZ616|MZ617",NookTablet:"Android.*Nook|NookColor|nook browser|BNRV200|BNRV200A|BNTV250|BNTV250A|BNTV400|BNTV600|LogicPD Zoom2",AcerTablet:"Android.*; \\b(A100|A101|A110|A200|A210|A211|A500|A501|A510|A511|A700|A701|W500|W500P|W501|W501P|W510|W511|W700|G100|G100W|B1-A71|B1-710|B1-711|A1-810|A1-811|A1-830)\\b|W3-810|\\bA3-A10\\b|\\bA3-A11\\b|\\bA3-A20\\b|\\bA3-A30|A3-A40",ToshibaTablet:"Android.*(AT100|AT105|AT200|AT205|AT270|AT275|AT300|AT305|AT1S5|AT500|AT570|AT700|AT830)|TOSHIBA.*FOLIO",LGTablet:"\\bL-06C|LG-V909|LG-V900|LG-V700|LG-V510|LG-V500|LG-V410|LG-V400|LG-VK810\\b",FujitsuTablet:"Android.*\\b(F-01D|F-02F|F-05E|F-10D|M532|Q572)\\b",PrestigioTablet:"PMP3170B|PMP3270B|PMP3470B|PMP7170B|PMP3370B|PMP3570C|PMP5870C|PMP3670B|PMP5570C|PMP5770D|PMP3970B|PMP3870C|PMP5580C|PMP5880D|PMP5780D|PMP5588C|PMP7280C|PMP7280C3G|PMP7280|PMP7880D|PMP5597D|PMP5597|PMP7100D|PER3464|PER3274|PER3574|PER3884|PER5274|PER5474|PMP5097CPRO|PMP5097|PMP7380D|PMP5297C|PMP5297C_QUAD|PMP812E|PMP812E3G|PMP812F|PMP810E|PMP880TD|PMT3017|PMT3037|PMT3047|PMT3057|PMT7008|PMT5887|PMT5001|PMT5002",LenovoTablet:"Lenovo TAB|Idea(Tab|Pad)( A1|A10| K1|)|ThinkPad([ ]+)?Tablet|YT3-850M|YT3-X90L|YT3-X90F|YT3-X90X|Lenovo.*(S2109|S2110|S5000|S6000|K3011|A3000|A3500|A1000|A2107|A2109|A1107|A5500|A7600|B6000|B8000|B8080)(-|)(FL|F|HV|H|)|TB-X103F|TB-X304X|TB-X304F|TB-X304L|TB-X505F|TB-X505L|TB-X505X|TB-X605F|TB-X605L|TB-8703F|TB-8703X|TB-8703N|TB-8704N|TB-8704F|TB-8704X|TB-8704V|TB-7304F|TB-7304I|TB-7304X|Tab2A7-10F|Tab2A7-20F|TB2-X30L|YT3-X50L|YT3-X50F|YT3-X50M|YT-X705F|YT-X703F|YT-X703L|YT-X705L|YT-X705X|TB2-X30F|TB2-X30L|TB2-X30M|A2107A-F|A2107A-H|TB3-730F|TB3-730M|TB3-730X|TB-7504F|TB-7504X|TB-X704F|TB-X104F|TB3-X70F|TB-X705F|TB-8504F|TB3-X70L|TB3-710F|TB-X704L",DellTablet:"Venue 11|Venue 8|Venue 7|Dell Streak 10|Dell Streak 7",YarvikTablet:"Android.*\\b(TAB210|TAB211|TAB224|TAB250|TAB260|TAB264|TAB310|TAB360|TAB364|TAB410|TAB411|TAB420|TAB424|TAB450|TAB460|TAB461|TAB464|TAB465|TAB467|TAB468|TAB07-100|TAB07-101|TAB07-150|TAB07-151|TAB07-152|TAB07-200|TAB07-201-3G|TAB07-210|TAB07-211|TAB07-212|TAB07-214|TAB07-220|TAB07-400|TAB07-485|TAB08-150|TAB08-200|TAB08-201-3G|TAB08-201-30|TAB09-100|TAB09-211|TAB09-410|TAB10-150|TAB10-201|TAB10-211|TAB10-400|TAB10-410|TAB13-201|TAB274EUK|TAB275EUK|TAB374EUK|TAB462EUK|TAB474EUK|TAB9-200)\\b",MedionTablet:"Android.*\\bOYO\\b|LIFE.*(P9212|P9514|P9516|S9512)|LIFETAB",ArnovaTablet:"97G4|AN10G2|AN7bG3|AN7fG3|AN8G3|AN8cG3|AN7G3|AN9G3|AN7dG3|AN7dG3ST|AN7dG3ChildPad|AN10bG3|AN10bG3DT|AN9G2",IntensoTablet:"INM8002KP|INM1010FP|INM805ND|Intenso Tab|TAB1004",IRUTablet:"M702pro",MegafonTablet:"MegaFon V9|\\bZTE V9\\b|Android.*\\bMT7A\\b",EbodaTablet:"E-Boda (Supreme|Impresspeed|Izzycomm|Essential)",AllViewTablet:"Allview.*(Viva|Alldro|City|Speed|All TV|Frenzy|Quasar|Shine|TX1|AX1|AX2)",ArchosTablet:"\\b(101G9|80G9|A101IT)\\b|Qilive 97R|Archos5|\\bARCHOS (70|79|80|90|97|101|FAMILYPAD|)(b|c|)(G10| Cobalt| TITANIUM(HD|)| Xenon| Neon|XSK| 2| XS 2| PLATINUM| CARBON|GAMEPAD)\\b",AinolTablet:"NOVO7|NOVO8|NOVO10|Novo7Aurora|Novo7Basic|NOVO7PALADIN|novo9-Spark",NokiaLumiaTablet:"Lumia 2520",SonyTablet:"Sony.*Tablet|Xperia Tablet|Sony Tablet S|SO-03E|SGPT12|SGPT13|SGPT114|SGPT121|SGPT122|SGPT123|SGPT111|SGPT112|SGPT113|SGPT131|SGPT132|SGPT133|SGPT211|SGPT212|SGPT213|SGP311|SGP312|SGP321|EBRD1101|EBRD1102|EBRD1201|SGP351|SGP341|SGP511|SGP512|SGP521|SGP541|SGP551|SGP621|SGP641|SGP612|SOT31|SGP771|SGP611|SGP612|SGP712",PhilipsTablet:"\\b(PI2010|PI3000|PI3100|PI3105|PI3110|PI3205|PI3210|PI3900|PI4010|PI7000|PI7100)\\b",CubeTablet:"Android.*(K8GT|U9GT|U10GT|U16GT|U17GT|U18GT|U19GT|U20GT|U23GT|U30GT)|CUBE U8GT",CobyTablet:"MID1042|MID1045|MID1125|MID1126|MID7012|MID7014|MID7015|MID7034|MID7035|MID7036|MID7042|MID7048|MID7127|MID8042|MID8048|MID8127|MID9042|MID9740|MID9742|MID7022|MID7010",MIDTablet:"M9701|M9000|M9100|M806|M1052|M806|T703|MID701|MID713|MID710|MID727|MID760|MID830|MID728|MID933|MID125|MID810|MID732|MID120|MID930|MID800|MID731|MID900|MID100|MID820|MID735|MID980|MID130|MID833|MID737|MID960|MID135|MID860|MID736|MID140|MID930|MID835|MID733|MID4X10",MSITablet:"MSI \\b(Primo 73K|Primo 73L|Primo 81L|Primo 77|Primo 93|Primo 75|Primo 76|Primo 73|Primo 81|Primo 91|Primo 90|Enjoy 71|Enjoy 7|Enjoy 10)\\b",SMiTTablet:"Android.*(\\bMID\\b|MID-560|MTV-T1200|MTV-PND531|MTV-P1101|MTV-PND530)",RockChipTablet:"Android.*(RK2818|RK2808A|RK2918|RK3066)|RK2738|RK2808A",FlyTablet:"IQ310|Fly Vision",bqTablet:"Android.*(bq)?.*\\b(Elcano|Curie|Edison|Maxwell|Kepler|Pascal|Tesla|Hypatia|Platon|Newton|Livingstone|Cervantes|Avant|Aquaris ([E|M]10|M8))\\b|Maxwell.*Lite|Maxwell.*Plus",HuaweiTablet:"MediaPad|MediaPad 7 Youth|IDEOS S7|S7-201c|S7-202u|S7-101|S7-103|S7-104|S7-105|S7-106|S7-201|S7-Slim|M2-A01L|BAH-L09|BAH-W09|AGS-L09|CMR-AL19",NecTablet:"\\bN-06D|\\bN-08D",PantechTablet:"Pantech.*P4100",BronchoTablet:"Broncho.*(N701|N708|N802|a710)",VersusTablet:"TOUCHPAD.*[78910]|\\bTOUCHTAB\\b",ZyncTablet:"z1000|Z99 2G|z930|z990|z909|Z919|z900",PositivoTablet:"TB07STA|TB10STA|TB07FTA|TB10FTA",NabiTablet:"Android.*\\bNabi",KoboTablet:"Kobo Touch|\\bK080\\b|\\bVox\\b Build|\\bArc\\b Build",DanewTablet:"DSlide.*\\b(700|701R|702|703R|704|802|970|971|972|973|974|1010|1012)\\b",TexetTablet:"NaviPad|TB-772A|TM-7045|TM-7055|TM-9750|TM-7016|TM-7024|TM-7026|TM-7041|TM-7043|TM-7047|TM-8041|TM-9741|TM-9747|TM-9748|TM-9751|TM-7022|TM-7021|TM-7020|TM-7011|TM-7010|TM-7023|TM-7025|TM-7037W|TM-7038W|TM-7027W|TM-9720|TM-9725|TM-9737W|TM-1020|TM-9738W|TM-9740|TM-9743W|TB-807A|TB-771A|TB-727A|TB-725A|TB-719A|TB-823A|TB-805A|TB-723A|TB-715A|TB-707A|TB-705A|TB-709A|TB-711A|TB-890HD|TB-880HD|TB-790HD|TB-780HD|TB-770HD|TB-721HD|TB-710HD|TB-434HD|TB-860HD|TB-840HD|TB-760HD|TB-750HD|TB-740HD|TB-730HD|TB-722HD|TB-720HD|TB-700HD|TB-500HD|TB-470HD|TB-431HD|TB-430HD|TB-506|TB-504|TB-446|TB-436|TB-416|TB-146SE|TB-126SE",PlaystationTablet:"Playstation.*(Portable|Vita)",TrekstorTablet:"ST10416-1|VT10416-1|ST70408-1|ST702xx-1|ST702xx-2|ST80208|ST97216|ST70104-2|VT10416-2|ST10216-2A|SurfTab",PyleAudioTablet:"\\b(PTBL10CEU|PTBL10C|PTBL72BC|PTBL72BCEU|PTBL7CEU|PTBL7C|PTBL92BC|PTBL92BCEU|PTBL9CEU|PTBL9CUK|PTBL9C)\\b",AdvanTablet:"Android.* \\b(E3A|T3X|T5C|T5B|T3E|T3C|T3B|T1J|T1F|T2A|T1H|T1i|E1C|T1-E|T5-A|T4|E1-B|T2Ci|T1-B|T1-D|O1-A|E1-A|T1-A|T3A|T4i)\\b ",DanyTechTablet:"Genius Tab G3|Genius Tab S2|Genius Tab Q3|Genius Tab G4|Genius Tab Q4|Genius Tab G-II|Genius TAB GII|Genius TAB GIII|Genius Tab S1",GalapadTablet:"Android [0-9.]+; [a-z-]+; \\bG1\\b",MicromaxTablet:"Funbook|Micromax.*\\b(P250|P560|P360|P362|P600|P300|P350|P500|P275)\\b",KarbonnTablet:"Android.*\\b(A39|A37|A34|ST8|ST10|ST7|Smart Tab3|Smart Tab2)\\b",AllFineTablet:"Fine7 Genius|Fine7 Shine|Fine7 Air|Fine8 Style|Fine9 More|Fine10 Joy|Fine11 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1027",HCLTablet:"HCL.*Tablet|Connect-3G-2.0|Connect-2G-2.0|ME Tablet U1|ME Tablet U2|ME Tablet G1|ME Tablet X1|ME Tablet Y2|ME Tablet Sync",DPSTablet:"DPS Dream 9|DPS Dual 7",VistureTablet:"V97 HD|i75 3G|Visture V4( HD)?|Visture V5( HD)?|Visture V10",CrestaTablet:"CTP(-)?810|CTP(-)?818|CTP(-)?828|CTP(-)?838|CTP(-)?888|CTP(-)?978|CTP(-)?980|CTP(-)?987|CTP(-)?988|CTP(-)?989",MediatekTablet:"\\bMT8125|MT8389|MT8135|MT8377\\b",ConcordeTablet:"Concorde([ ]+)?Tab|ConCorde ReadMan",GoCleverTablet:"GOCLEVER TAB|A7GOCLEVER|M1042|M7841|M742|R1042BK|R1041|TAB A975|TAB A7842|TAB A741|TAB A741L|TAB M723G|TAB M721|TAB A1021|TAB I921|TAB R721|TAB I720|TAB T76|TAB R70|TAB R76.2|TAB R106|TAB R83.2|TAB M813G|TAB I721|GCTA722|TAB I70|TAB I71|TAB S73|TAB R73|TAB R74|TAB R93|TAB R75|TAB R76.1|TAB A73|TAB A93|TAB A93.2|TAB T72|TAB R83|TAB R974|TAB R973|TAB A101|TAB A103|TAB A104|TAB A104.2|R105BK|M713G|A972BK|TAB A971|TAB R974.2|TAB R104|TAB R83.3|TAB A1042",ModecomTablet:"FreeTAB 9000|FreeTAB 7.4|FreeTAB 7004|FreeTAB 7800|FreeTAB 2096|FreeTAB 7.5|FreeTAB 1014|FreeTAB 1001 |FreeTAB 8001|FreeTAB 9706|FreeTAB 9702|FreeTAB 7003|FreeTAB 7002|FreeTAB 1002|FreeTAB 7801|FreeTAB 1331|FreeTAB 1004|FreeTAB 8002|FreeTAB 8014|FreeTAB 9704|FreeTAB 1003",VoninoTablet:"\\b(Argus[ _]?S|Diamond[ _]?79HD|Emerald[ _]?78E|Luna[ _]?70C|Onyx[ _]?S|Onyx[ _]?Z|Orin[ _]?HD|Orin[ _]?S|Otis[ _]?S|SpeedStar[ _]?S|Magnet[ _]?M9|Primus[ _]?94[ _]?3G|Primus[ _]?94HD|Primus[ _]?QS|Android.*\\bQ8\\b|Sirius[ _]?EVO[ _]?QS|Sirius[ _]?QS|Spirit[ _]?S)\\b",ECSTablet:"V07OT2|TM105A|S10OT1|TR10CS1",StorexTablet:"eZee[_']?(Tab|Go)[0-9]+|TabLC7|Looney Tunes Tab",VodafoneTablet:"SmartTab([ ]+)?[0-9]+|SmartTabII10|SmartTabII7|VF-1497|VFD 1400",EssentielBTablet:"Smart[ ']?TAB[ ]+?[0-9]+|Family[ ']?TAB2",RossMoorTablet:"RM-790|RM-997|RMD-878G|RMD-974R|RMT-705A|RMT-701|RME-601|RMT-501|RMT-711",iMobileTablet:"i-mobile i-note",TolinoTablet:"tolino tab [0-9.]+|tolino shine",AudioSonicTablet:"\\bC-22Q|T7-QC|T-17B|T-17P\\b",AMPETablet:"Android.* A78 ",SkkTablet:"Android.* (SKYPAD|PHOENIX|CYCLOPS)",TecnoTablet:"TECNO P9|TECNO DP8D",JXDTablet:"Android.* \\b(F3000|A3300|JXD5000|JXD3000|JXD2000|JXD300B|JXD300|S5800|S7800|S602b|S5110b|S7300|S5300|S602|S603|S5100|S5110|S601|S7100a|P3000F|P3000s|P101|P200s|P1000m|P200m|P9100|P1000s|S6600b|S908|P1000|P300|S18|S6600|S9100)\\b",iJoyTablet:"Tablet (Spirit 7|Essentia|Galatea|Fusion|Onix 7|Landa|Titan|Scooby|Deox|Stella|Themis|Argon|Unique 7|Sygnus|Hexen|Finity 7|Cream|Cream X2|Jade|Neon 7|Neron 7|Kandy|Scape|Saphyr 7|Rebel|Biox|Rebel|Rebel 8GB|Myst|Draco 7|Myst|Tab7-004|Myst|Tadeo Jones|Tablet Boing|Arrow|Draco Dual Cam|Aurix|Mint|Amity|Revolution|Finity 9|Neon 9|T9w|Amity 4GB Dual Cam|Stone 4GB|Stone 8GB|Andromeda|Silken|X2|Andromeda II|Halley|Flame|Saphyr 9,7|Touch 8|Planet|Triton|Unique 10|Hexen 10|Memphis 4GB|Memphis 8GB|Onix 10)",FX2Tablet:"FX2 PAD7|FX2 PAD10",XoroTablet:"KidsPAD 701|PAD[ ]?712|PAD[ ]?714|PAD[ ]?716|PAD[ ]?717|PAD[ ]?718|PAD[ ]?720|PAD[ ]?721|PAD[ ]?722|PAD[ ]?790|PAD[ ]?792|PAD[ ]?900|PAD[ ]?9715D|PAD[ ]?9716DR|PAD[ ]?9718DR|PAD[ ]?9719QR|PAD[ ]?9720QR|TelePAD1030|Telepad1032|TelePAD730|TelePAD731|TelePAD732|TelePAD735Q|TelePAD830|TelePAD9730|TelePAD795|MegaPAD 1331|MegaPAD 1851|MegaPAD 2151",ViewsonicTablet:"ViewPad 10pi|ViewPad 10e|ViewPad 10s|ViewPad E72|ViewPad7|ViewPad E100|ViewPad 7e|ViewSonic VB733|VB100a",VerizonTablet:"QTAQZ3|QTAIR7|QTAQTZ3|QTASUN1|QTASUN2|QTAXIA1",OdysTablet:"LOOX|XENO10|ODYS[ -](Space|EVO|Xpress|NOON)|\\bXELIO\\b|Xelio10Pro|XELIO7PHONETAB|XELIO10EXTREME|XELIOPT2|NEO_QUAD10",CaptivaTablet:"CAPTIVA PAD",IconbitTablet:"NetTAB|NT-3702|NT-3702S|NT-3702S|NT-3603P|NT-3603P|NT-0704S|NT-0704S|NT-3805C|NT-3805C|NT-0806C|NT-0806C|NT-0909T|NT-0909T|NT-0907S|NT-0907S|NT-0902S|NT-0902S",TeclastTablet:"T98 4G|\\bP80\\b|\\bX90HD\\b|X98 Air|X98 Air 3G|\\bX89\\b|P80 3G|\\bX80h\\b|P98 Air|\\bX89HD\\b|P98 3G|\\bP90HD\\b|P89 3G|X98 3G|\\bP70h\\b|P79HD 3G|G18d 3G|\\bP79HD\\b|\\bP89s\\b|\\bA88\\b|\\bP10HD\\b|\\bP19HD\\b|G18 3G|\\bP78HD\\b|\\bA78\\b|\\bP75\\b|G17s 3G|G17h 3G|\\bP85t\\b|\\bP90\\b|\\bP11\\b|\\bP98t\\b|\\bP98HD\\b|\\bG18d\\b|\\bP85s\\b|\\bP11HD\\b|\\bP88s\\b|\\bA80HD\\b|\\bA80se\\b|\\bA10h\\b|\\bP89\\b|\\bP78s\\b|\\bG18\\b|\\bP85\\b|\\bA70h\\b|\\bA70\\b|\\bG17\\b|\\bP18\\b|\\bA80s\\b|\\bA11s\\b|\\bP88HD\\b|\\bA80h\\b|\\bP76s\\b|\\bP76h\\b|\\bP98\\b|\\bA10HD\\b|\\bP78\\b|\\bP88\\b|\\bA11\\b|\\bA10t\\b|\\bP76a\\b|\\bP76t\\b|\\bP76e\\b|\\bP85HD\\b|\\bP85a\\b|\\bP86\\b|\\bP75HD\\b|\\bP76v\\b|\\bA12\\b|\\bP75a\\b|\\bA15\\b|\\bP76Ti\\b|\\bP81HD\\b|\\bA10\\b|\\bT760VE\\b|\\bT720HD\\b|\\bP76\\b|\\bP73\\b|\\bP71\\b|\\bP72\\b|\\bT720SE\\b|\\bC520Ti\\b|\\bT760\\b|\\bT720VE\\b|T720-3GE|T720-WiFi",OndaTablet:"\\b(V975i|Vi30|VX530|V701|Vi60|V701s|Vi50|V801s|V719|Vx610w|VX610W|V819i|Vi10|VX580W|Vi10|V711s|V813|V811|V820w|V820|Vi20|V711|VI30W|V712|V891w|V972|V819w|V820w|Vi60|V820w|V711|V813s|V801|V819|V975s|V801|V819|V819|V818|V811|V712|V975m|V101w|V961w|V812|V818|V971|V971s|V919|V989|V116w|V102w|V973|Vi40)\\b[\\s]+|V10 \\b4G\\b",JaytechTablet:"TPC-PA762",BlaupunktTablet:"Endeavour 800NG|Endeavour 1010",DigmaTablet:"\\b(iDx10|iDx9|iDx8|iDx7|iDxD7|iDxD8|iDsQ8|iDsQ7|iDsQ8|iDsD10|iDnD7|3TS804H|iDsQ11|iDj7|iDs10)\\b",EvolioTablet:"ARIA_Mini_wifi|Aria[ _]Mini|Evolio X10|Evolio X7|Evolio X8|\\bEvotab\\b|\\bNeura\\b",LavaTablet:"QPAD E704|\\bIvoryS\\b|E-TAB IVORY|\\bE-TAB\\b",AocTablet:"MW0811|MW0812|MW0922|MTK8382|MW1031|MW0831|MW0821|MW0931|MW0712",MpmanTablet:"MP11 OCTA|MP10 OCTA|MPQC1114|MPQC1004|MPQC994|MPQC974|MPQC973|MPQC804|MPQC784|MPQC780|\\bMPG7\\b|MPDCG75|MPDCG71|MPDC1006|MP101DC|MPDC9000|MPDC905|MPDC706HD|MPDC706|MPDC705|MPDC110|MPDC100|MPDC99|MPDC97|MPDC88|MPDC8|MPDC77|MP709|MID701|MID711|MID170|MPDC703|MPQC1010",CelkonTablet:"CT695|CT888|CT[\\s]?910|CT7 Tab|CT9 Tab|CT3 Tab|CT2 Tab|CT1 Tab|C820|C720|\\bCT-1\\b",WolderTablet:"miTab \\b(DIAMOND|SPACE|BROOKLYN|NEO|FLY|MANHATTAN|FUNK|EVOLUTION|SKY|GOCAR|IRON|GENIUS|POP|MINT|EPSILON|BROADWAY|JUMP|HOP|LEGEND|NEW AGE|LINE|ADVANCE|FEEL|FOLLOW|LIKE|LINK|LIVE|THINK|FREEDOM|CHICAGO|CLEVELAND|BALTIMORE-GH|IOWA|BOSTON|SEATTLE|PHOENIX|DALLAS|IN 101|MasterChef)\\b",MediacomTablet:"M-MPI10C3G|M-SP10EG|M-SP10EGP|M-SP10HXAH|M-SP7HXAH|M-SP10HXBH|M-SP8HXAH|M-SP8MXA",MiTablet:"\\bMI PAD\\b|\\bHM NOTE 1W\\b",NibiruTablet:"Nibiru M1|Nibiru Jupiter One",NexoTablet:"NEXO NOVA|NEXO 10|NEXO AVIO|NEXO FREE|NEXO GO|NEXO EVO|NEXO 3G|NEXO SMART|NEXO KIDDO|NEXO MOBI",LeaderTablet:"TBLT10Q|TBLT10I|TBL-10WDKB|TBL-10WDKBO2013|TBL-W230V2|TBL-W450|TBL-W500|SV572|TBLT7I|TBA-AC7-8G|TBLT79|TBL-8W16|TBL-10W32|TBL-10WKB|TBL-W100",UbislateTablet:"UbiSlate[\\s]?7C",PocketBookTablet:"Pocketbook",KocasoTablet:"\\b(TB-1207)\\b",HisenseTablet:"\\b(F5281|E2371)\\b",Hudl:"Hudl HT7S3|Hudl 2",TelstraTablet:"T-Hub2",GenericTablet:"Android.*\\b97D\\b|Tablet(?!.*PC)|BNTV250A|MID-WCDMA|LogicPD Zoom2|\\bA7EB\\b|CatNova8|A1_07|CT704|CT1002|\\bM721\\b|rk30sdk|\\bEVOTAB\\b|M758A|ET904|ALUMIUM10|Smartfren Tab|Endeavour 1010|Tablet-PC-4|Tagi Tab|\\bM6pro\\b|CT1020W|arc 10HD|\\bTP750\\b|\\bQTAQZ3\\b|WVT101|TM1088|KT107"},oss:{AndroidOS:"Android",BlackBerryOS:"blackberry|\\bBB10\\b|rim tablet os",PalmOS:"PalmOS|avantgo|blazer|elaine|hiptop|palm|plucker|xiino",SymbianOS:"Symbian|SymbOS|Series60|Series40|SYB-[0-9]+|\\bS60\\b",WindowsMobileOS:"Windows CE.*(PPC|Smartphone|Mobile|[0-9]{3}x[0-9]{3})|Windows Mobile|Windows Phone [0-9.]+|WCE;",WindowsPhoneOS:"Windows Phone 10.0|Windows Phone 8.1|Windows Phone 8.0|Windows Phone OS|XBLWP7|ZuneWP7|Windows NT 6.[23]; ARM;",iOS:"\\biPhone.*Mobile|\\biPod|\\biPad|AppleCoreMedia",iPadOS:"CPU OS 13",SailfishOS:"Sailfish",MeeGoOS:"MeeGo",MaemoOS:"Maemo",JavaOS:"J2ME/|\\bMIDP\\b|\\bCLDC\\b",webOS:"webOS|hpwOS",badaOS:"\\bBada\\b",BREWOS:"BREW"},uas:{Chrome:"\\bCrMo\\b|CriOS|Android.*Chrome/[.0-9]* (Mobile)?",Dolfin:"\\bDolfin\\b",Opera:"Opera.*Mini|Opera.*Mobi|Android.*Opera|Mobile.*OPR/[0-9.]+$|Coast/[0-9.]+",Skyfire:"Skyfire",Edge:"\\bEdgiOS\\b|Mobile Safari/[.0-9]* Edge",IE:"IEMobile|MSIEMobile",Firefox:"fennec|firefox.*maemo|(Mobile|Tablet).*Firefox|Firefox.*Mobile|FxiOS",Bolt:"bolt",TeaShark:"teashark",Blazer:"Blazer",Safari:"Version((?!\\bEdgiOS\\b).)*Mobile.*Safari|Safari.*Mobile|MobileSafari",WeChat:"\\bMicroMessenger\\b",UCBrowser:"UC.*Browser|UCWEB",baiduboxapp:"baiduboxapp",baidubrowser:"baidubrowser",DiigoBrowser:"DiigoBrowser",Mercury:"\\bMercury\\b",ObigoBrowser:"Obigo",NetFront:"NF-Browser",GenericBrowser:"NokiaBrowser|OviBrowser|OneBrowser|TwonkyBeamBrowser|SEMC.*Browser|FlyFlow|Minimo|NetFront|Novarra-Vision|MQQBrowser|MicroMessenger",PaleMoon:"Android.*PaleMoon|Mobile.*PaleMoon"},props:{Mobile:"Mobile/[VER]",Build:"Build/[VER]",Version:"Version/[VER]",VendorID:"VendorID/[VER]",iPad:"iPad.*CPU[a-z ]+[VER]",iPhone:"iPhone.*CPU[a-z ]+[VER]",iPod:"iPod.*CPU[a-z ]+[VER]",Kindle:"Kindle/[VER]",Chrome:["Chrome/[VER]","CriOS/[VER]","CrMo/[VER]"],Coast:["Coast/[VER]"],Dolfin:"Dolfin/[VER]",Firefox:["Firefox/[VER]","FxiOS/[VER]"],Fennec:"Fennec/[VER]",Edge:"Edge/[VER]",IE:["IEMobile/[VER];","IEMobile [VER]","MSIE [VER];","Trident/[0-9.]+;.*rv:[VER]"],NetFront:"NetFront/[VER]",NokiaBrowser:"NokiaBrowser/[VER]",Opera:[" OPR/[VER]","Opera Mini/[VER]","Version/[VER]"],"Opera Mini":"Opera Mini/[VER]","Opera Mobi":"Version/[VER]",UCBrowser:["UCWEB[VER]","UC.*Browser/[VER]"],MQQBrowser:"MQQBrowser/[VER]",MicroMessenger:"MicroMessenger/[VER]",baiduboxapp:"baiduboxapp/[VER]",baidubrowser:"baidubrowser/[VER]",SamsungBrowser:"SamsungBrowser/[VER]",Iron:"Iron/[VER]",Safari:["Version/[VER]","Safari/[VER]"],Skyfire:"Skyfire/[VER]",Tizen:"Tizen/[VER]",Webkit:"webkit[ /][VER]",PaleMoon:"PaleMoon/[VER]",SailfishBrowser:"SailfishBrowser/[VER]",Gecko:"Gecko/[VER]",Trident:"Trident/[VER]",Presto:"Presto/[VER]",Goanna:"Goanna/[VER]",iOS:" \\bi?OS\\b [VER][ ;]{1}",Android:"Android [VER]",Sailfish:"Sailfish [VER]",BlackBerry:["BlackBerry[\\w]+/[VER]","BlackBerry.*Version/[VER]","Version/[VER]"],BREW:"BREW [VER]",Java:"Java/[VER]","Windows Phone OS":["Windows Phone OS [VER]","Windows Phone [VER]"],"Windows Phone":"Windows Phone [VER]","Windows CE":"Windows CE/[VER]","Windows NT":"Windows NT [VER]",Symbian:["SymbianOS/[VER]","Symbian/[VER]"],webOS:["webOS/[VER]","hpwOS/[VER];"]},utils:{Bot:"Googlebot|facebookexternalhit|Google-AMPHTML|s~amp-validator|AdsBot-Google|Google Keyword Suggestion|Facebot|YandexBot|YandexMobileBot|bingbot|ia_archiver|AhrefsBot|Ezooms|GSLFbot|WBSearchBot|Twitterbot|TweetmemeBot|Twikle|PaperLiBot|Wotbox|UnwindFetchor|Exabot|MJ12bot|YandexImages|TurnitinBot|Pingdom|contentkingapp|AspiegelBot",MobileBot:"Googlebot-Mobile|AdsBot-Google-Mobile|YahooSeeker/M1A1-R2D2",DesktopMode:"WPDesktop",TV:"SonyDTV|HbbTV",WebKit:"(webkit)[ /]([\\w.]+)",Console:"\\b(Nintendo|Nintendo WiiU|Nintendo 3DS|Nintendo Switch|PLAYSTATION|Xbox)\\b",Watch:"SM-V700"}},g.detectMobileBrowsers={fullPattern:/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i, shortPattern:/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i,tabletPattern:/android|ipad|playbook|silk/i};var h,i=Object.prototype.hasOwnProperty;return g.FALLBACK_PHONE="UnknownPhone",g.FALLBACK_TABLET="UnknownTablet",g.FALLBACK_MOBILE="UnknownMobile",h="isArray"in Array?Array.isArray:function(a){return"[object Array]"===Object.prototype.toString.call(a)},function(){var a,b,c,e,f,j,k=g.mobileDetectRules;for(a in k.props)if(i.call(k.props,a)){for(b=k.props[a],h(b)||(b=[b]),f=b.length,e=0;e<f;++e)c=b[e],j=c.indexOf("[VER]"),j>=0&&(c=c.substring(0,j)+"([\\w._\\+]+)"+c.substring(j+5)),b[e]=new RegExp(c,"i");k.props[a]=b}d(k.oss),d(k.phones),d(k.tablets),d(k.uas),d(k.utils),k.oss0={WindowsPhoneOS:k.oss.WindowsPhoneOS,WindowsMobileOS:k.oss.WindowsMobileOS}}(),g.findMatch=function(a,b){for(var c in a)if(i.call(a,c)&&a[c].test(b))return c;return null},g.findMatches=function(a,b){var c=[];for(var d in a)i.call(a,d)&&a[d].test(b)&&c.push(d);return c},g.getVersionStr=function(a,b){var c,d,e,f,h=g.mobileDetectRules.props;if(i.call(h,a))for(c=h[a],e=c.length,d=0;d<e;++d)if(f=c[d].exec(b),null!==f)return f[1];return null},g.getVersion=function(a,b){var c=g.getVersionStr(a,b);return c?g.prepareVersionNo(c):NaN},g.prepareVersionNo=function(a){var b;return b=a.split(/[a-z._ \/\-]/i),1===b.length&&(a=b[0]),b.length>1&&(a=b[0]+".",b.shift(),a+=b.join("")),Number(a)},g.isMobileFallback=function(a){return g.detectMobileBrowsers.fullPattern.test(a)||g.detectMobileBrowsers.shortPattern.test(a.substr(0,4))},g.isTabletFallback=function(a){return g.detectMobileBrowsers.tabletPattern.test(a)},g.prepareDetectionCache=function(a,c,d){if(a.mobile===b){var e,h,i;return(h=g.findMatch(g.mobileDetectRules.tablets,c))?(a.mobile=a.tablet=h,void(a.phone=null)):(e=g.findMatch(g.mobileDetectRules.phones,c))?(a.mobile=a.phone=e,void(a.tablet=null)):void(g.isMobileFallback(c)?(i=f.isPhoneSized(d),i===b?(a.mobile=g.FALLBACK_MOBILE,a.tablet=a.phone=null):i?(a.mobile=a.phone=g.FALLBACK_PHONE,a.tablet=null):(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null)):g.isTabletFallback(c)?(a.mobile=a.tablet=g.FALLBACK_TABLET,a.phone=null):a.mobile=a.tablet=a.phone=null)}},g.mobileGrade=function(a){var b=null!==a.mobile();return a.os("iOS")&&a.version("iPad")>=4.3||a.os("iOS")&&a.version("iPhone")>=3.1||a.os("iOS")&&a.version("iPod")>=3.1||a.version("Android")>2.1&&a.is("Webkit")||a.version("Windows Phone 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