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PiperOrigin-RevId: 724441907

official/projects/waste_identification_ml/circularnet-docs/content/_index.md

@@ -1,3 +1,51 @@
+## Table of contents
+
+**[CircularNet overview](#circularnet-overview)**
+
+  * [Get started with CircularNet](#get-started-with-circularnet)
+
+**[Discover CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/_index.md)**
+
+* [Benefits of CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/benefits-of-cn.md)
+* [When to use CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/when-to-use-cn.md)
+* [How CircularNet works](/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/how-cn-works.md)
+
+**[Choose a deployment solution](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/_index.md)**
+
+* [Where to host the models](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/_index.md#where-to-host-the-models)
+    * [Cloud deployment](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/_index.md#cloud-deployment)
+    * [Edge device deployment](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/_index.md#edge-device-deployment)
+    * [In-house server deployment](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/_index.md#in-house-server-deployment)
+
+**[Set up the system requirements](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/_index.md)**
+
+* [Choose a camera](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/_index.md)
+    * [Recommendations for selecting a machine vision camera](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/camera-recommendations.md)
+    * [Factors based on vision system placement](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md)
+        * [Sensor size](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#sensor-size)
+        * [Focal length](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#focal-length)
+        * [Aperture size (f-number)](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#aperture-size-f-number)
+        * [Shutter speed](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/factors.md#shutter-speed)
+    * [Table of specifications](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/table-of-specs.md)
+* [Choose edge device hardware](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-edge-device.md)
+
+**[Deploy CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/_index.md)**
+
+* [Before you begin](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/before-you-begin.md)
+* [Clone the repository and install packages](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/clone-repo.md)
+* [Start the server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server.md)
+
+**[Prepare and analyze images](/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/_index.md)**
+
+* [Learn about the prediction pipeline](/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/learn-about-pipeline.md)
+* [Apply the prediction pipeline in Google Cloud](/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/prediction-pipeline-in-cloud.md)
+* [Apply the prediction pipeline in an edge device](/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/prediction-pipeline-in-edge.md)
+
+**[View data analysis and reporting](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/_index.md)**
+
+* [Before you begin](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/before-you-begin.md)
+* [Configure the dashboard](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/configure-dashboard.md)
+
 ## CircularNet overview
 
 CircularNet is a free computer vision model developed by Google that utilizes
@@ -20,8 +68,8 @@ deployment preferences so you can install CircularNet according to your needs.
 
 Start exploring CircularNet by reviewing the following documentation:
 
-1. Discover [the benefits, features, components, and use cases](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/) of CircularNet.
-1. Choose between [the different deployment options](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/solutions/) to install CircularNet models.
-1. Learn about [the recommendations for installing the camera](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/) you require to capture images.
-1. Follow a step-by-step solution example to [deploy CircularNet](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/) and [prepare your captured images](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/) for analysis and object tracking.
-1. Learn how to connect your data with [a dashboard for visualization and reporting](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).
+1. Discover [the benefits, features, components, and use cases](/official/projects/waste_identification_ml/circularnet-docs/content/discover-cn/) of CircularNet.
+1. Choose between [the different deployment options](/official/projects/waste_identification_ml/circularnet-docs/content/solutions/) to install CircularNet models.
+1. Learn about [the recommendations for installing the camera](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/) you require to capture images.
+1. Follow a step-by-step solution example to [deploy CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/) and [prepare your captured images](/official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/) for analysis and object tracking.
+1. Learn how to connect your data with [a dashboard for visualization and reporting](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).

official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/_index.md

@@ -17,14 +17,14 @@ computer vision techniques extract multiple properties of each object, including
 color detection. These properties facilitate object tracking and help eliminate
 duplicate object counts.
 
-If you [deploy the server](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) on Google Cloud, you can
+If you [deploy the server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) on Google Cloud, you can
 automate the entire image analysis workflow within your VM instance. Integration
 with BigQuery tables, storage buckets, and dashboards allows for seamless data
 flow and real-time updates. A [prediction pipeline](./learn-about-pipeline) for
 Google Cloud pushes the data directly to storage buckets and BigQuery tables,
-which you can connect to the dashboard for [visualization and analysis](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).
+which you can connect to the dashboard for [visualization and analysis](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).
 
-On the other hand, direct data transfer to the cloud for edge device implementations needs a client-side configuration. A [prediction pipeline](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/learn-about-pipeline) for devices lets you load models sequentially and store image analysis results locally.
+On the other hand, direct data transfer to the cloud for edge device implementations needs a client-side configuration. A [prediction pipeline](/official/projects/waste_identification_ml/circularnet-docs/content/learn-about-pipeline) for devices lets you load models sequentially and store image analysis results locally.
 
 This section describes how to apply the two specialized CircularNet models using
 a prediction pipeline on the client side to prepare and analyze the images you

official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/learn-about-pipeline.md

@@ -26,4 +26,4 @@ object-tracking algorithm to identify and eliminate duplicate occurrences of
 objects across sequential frames, enhancing the accuracy of object detection and
 analysis.
 
-Moreover, [applying a prediction pipeline in Google Cloud](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/prediction-pipeline-in-cloud) automatically uploads raw images and prediction results into [BigQuery](https://cloud.google.com/bigquery) tables. This seamless integration allows you to combine [visualization dashboards with analytical reports](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/) effortlessly.
+Moreover, [applying a prediction pipeline in Google Cloud](/official/projects/waste_identification_ml/circularnet-docs/content/prediction-pipeline-in-cloud) automatically uploads raw images and prediction results into [BigQuery](https://cloud.google.com/bigquery) tables. This seamless integration allows you to combine [visualization dashboards with analytical reports](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/) effortlessly.

official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/prediction-pipeline-in-cloud.md

@@ -1,6 +1,6 @@
 Apart from applying the prediction models to analyze images, the script that runs [the prediction pipeline](./learn-about-pipeline) on Google Cloud ingests data into [BigQuery](https://cloud.google.com/bigquery) to store all the image analysis details.
 
-After [setting up a server](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) in a Google Cloud account, you can start recording videos of objects passing on your conveyor belt to gather data for analysis. The next step is transferring those video or image files to a [Cloud Storage](https://cloud.google.com/storage) bucket, where the prediction pipeline processes the images.
+After [setting up a server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) in a Google Cloud account, you can start recording videos of objects passing on your conveyor belt to gather data for analysis. The next step is transferring those video or image files to a [Cloud Storage](https://cloud.google.com/storage) bucket, where the prediction pipeline processes the images.
 
 The results of each video or image prediction are stored and appended to two
 BigQuery tables, ensuring efficient data management. After systematically
@@ -10,15 +10,15 @@ stores the results in another bucket for further use and analysis.
 This page explains how to create and manage Cloud Storage buckets on Google
 Cloud for the videos you record, run the prediction pipeline to apply the
 models, and process images for further analysis. You can then connect a
-visualization dashboard to the BigQuery tables to [display results as charts and reports](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).<br/><br/>
+visualization dashboard to the BigQuery tables to [display results as charts and reports](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).<br/><br/>
 
 {{< table_of_contents >}}
 
 ---
 
 ## Store videos in Cloud Storage buckets
 
-To effectively manage and process the videos or images recorded by the [machine vision camera](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/) capturing objects on the conveyor belt, you need the following storage devices:
+To effectively manage and process the videos or images recorded by the [machine vision camera](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/) capturing objects on the conveyor belt, you need the following storage devices:
 
 -  **Local disk storage**: Temporarily cache or store files locally from the camera.
 -  **Cloud Storage input bucket**: Store your recorded videos or images. You can automate uploads using a storage transfer agent.
@@ -55,7 +55,7 @@ from your NVIDIA T4 GPU virtual machine (VM) instance must have access to Cloud
 Storage buckets and BigQuery tables to let the VM instance run the prediction
 pipeline and upload the results to BigQuery.
 
-The VM you created when [deploying CircularNet](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/) has a service
+The VM you created when [deploying CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/) has a service
 account that must act as a _principal_ with the following two roles:
 
 -  Grant the Storage Admin role to access your Cloud Storage input and output buckets. [Add the VM service account as a principal to a bucket-level policy](https://cloud.google.com/storage/docs/access-control/using-iam-permissions#bucket-add) for both buckets.
@@ -73,7 +73,7 @@ on Google Cloud:
 1. [Grant the required permissions to the VM service account](#grant-required-permissions).
 1. From the **Navigation menu** on the Google Cloud console, select **Compute Engine** > **VM instances**.
 1. On the **VM instances** page, find the VM instance you created with the
-   NVIDIA T4 GPU when [deploying CircularNet](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/).
+   NVIDIA T4 GPU when [deploying CircularNet](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/).
 1. If you stopped your VM instance, restart it by clicking **More actions** >
    **Start / Resume** in the row of the instance that you want to restart.
 
@@ -82,7 +82,7 @@ on Google Cloud:
 
 1. Click **SSH** in the row of the instance that you want to connect to. The
    **SSH-in-Browser** tool opens. For more information, see [Connect to VMs](https://cloud.google.com/compute/docs/connect/standard-ssh#connect_to_vms).
-1. On the **SSH-in-browser** window, [start the server](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server).
+1. On the **SSH-in-browser** window, [start the server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server).
 1. Display the names of the models you loaded to the Triton inference server:
 
     ```

official/projects/waste_identification_ml/circularnet-docs/content/analyze-data/prediction-pipeline-in-edge.md

@@ -1,9 +1,9 @@
-The script that runs [the prediction pipeline](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/learn-about-pipeline) on an
+The script that runs [the prediction pipeline](/official/projects/waste_identification_ml/circularnet-docs/content/learn-about-pipeline) on an
 edge device applies the prediction models to analyze images.
 
-After [setting up a server](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) in your edge device, you
+After [setting up a server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server) in your edge device, you
 can start recording videos of objects passing on your conveyor belt to gather
-data for analysis and storing files locally from [the camera](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/). The next step is transferring those
+data for analysis and storing files locally from [the camera](/official/projects/waste_identification_ml/circularnet-docs/content/system-req/choose-camera/). The next step is transferring those
 videos or image files to a folder in the edge device, where the prediction
 pipeline processes the images.
 
@@ -15,7 +15,7 @@ creates an output directory with results for further use and analysis.
 This page explains how to run the prediction pipeline to apply the models to
 images stored locally in the edge device. You can then manage data according to
 your needs, such as exporting the results to BigQuery tables and connecting a
-visualization dashboard to [display results as charts and reports](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).<br/><br/>
+visualization dashboard to [display results as charts and reports](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).<br/><br/>
 
 {{< table_of_contents >}}
 
@@ -28,7 +28,7 @@ in an edge device:
 
 1. Open the terminal of your edge device to interact with the operating system
    through the command line.
-1. [Start the server](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server).
+1. [Start the server](/official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/start-server).
 1. Display the names of the models you loaded to the Triton inference server:
 
     ```
@@ -109,12 +109,12 @@ You have finished running the prediction pipeline and applying the prediction
 models to your files for further analysis. You can find the image results with
 the applied masks in your output folder in the edge device. You can also export
 your results manually to a [BigQuery](https://cloud.google.com/bigquery) table
-to connect it with a visualization dashboard for [data analysis and reporting](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).
+to connect it with a visualization dashboard for [data analysis and reporting](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/).
 
 **Important:** If you rerun the prediction pipeline on the same file, you must
 delete the results created the first time you ran the script from the output
 folder to avoid conflicting issues.
 
 ## What's next
 
--  [View data analysis and reporting](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/view-data/)
+-  [View data analysis and reporting](/official/projects/waste_identification_ml/circularnet-docs/content/view-data/)

official/projects/waste_identification_ml/circularnet-docs/content/deploy-cn/clone-repo.md

@@ -1,4 +1,4 @@
-After meeting the [prerequisites](/third_party/tensorflow_models/official/projects/waste_identification_ml/circularnet-docs/content/before-you-begin), follow these steps to clone the project from the [GitHub repository](https://github.com/tensorflow/models/tree/master/official/projects/waste_identification_ml) and install all the required packages.
+After meeting the [prerequisites](/official/projects/waste_identification_ml/circularnet-docs/content/before-you-begin), follow these steps to clone the project from the [GitHub repository](https://github.com/tensorflow/models/tree/master/official/projects/waste_identification_ml) and install all the required packages.
 
 Run the following commands on the **SSH-in-browser** window of your VM instance
 in Google Cloud or the terminal of your edge device: