open-edge-platform · tbujewsk · Dec 31, 2025 · Dec 23, 2025 · Dec 23, 2025 · Dec 31, 2025
@@ -1,7 +1,7 @@
 
-# DL streamer Contributor Guide
+# DL Streamer Contributor Guide
 
-The following are guidelines for contributing to the Dlstreamer project, including the code of conduct, submitting issues, and contributing code.  
+The following are guidelines for contributing to the DL Streamer project, including the code of conduct, submitting issues, and contributing code.
 
 # Table of Contents
 
@@ -15,15 +15,15 @@ The following are guidelines for contributing to the Dlstreamer project, includi
 
 # Code of Conduct
 
-This project and everyone participating in it are governed by the [`CODE_OF_CONDUCT`](CODE_OF_CONDUCT.md) document. By participating, you are expected to adhere to this code.  
+This project and everyone participating in it are governed by the [`CODE_OF_CONDUCT`](CODE_OF_CONDUCT.md) document. By participating, you are expected to adhere to this code.
 
-# Security 
+# Security
 
-Read the [`Security Policy`](SECURITY.md).  
+Read the [`Security Policy`](SECURITY.md).
 
 # Get Started
 
-Clone the repository and follow the [`README`](README.md) to get started with the sample applications of interest.  
+Clone the repository and follow the [`README`](README.md) to get started with the sample applications of interest.
 
 ```
     git clone https://github.com/open-edge-platform/dlstreamer.git
@@ -34,18 +34,18 @@ Clone the repository and follow the [`README`](README.md) to get started with th
 
 ## Contribute Code Changes
 
-> If you want to help improve Dlstreamer, choose one of the issues reported in [`GitHub Issues`](issues) and create a [`Pull Request`](pulls) to address it.  
+> If you want to help improve DL Streamer, choose one of the issues reported in [`GitHub Issues`](issues) and create a [`Pull Request`](pulls) to address it.
 > Note: Please check that the change hasn't been implemented before you start working on it.
 
 ## Improve Documentation
 
 The easiest way to help with the `Developer Guide` and `User Guide` is to review it and provide feedback on the
-existing articles. Whether you notice a mistake, see the possibility of improving the text, or think more 
+existing articles. Whether you notice a mistake, see the possibility of improving the text, or think more
 information should be added, you can reach out to discuss the potential changes.
 
 ## Report Bugs
 
-If you encounter a bug, open an issue in [`Github Issues`](issues). Provide the following information to help us 
+If you encounter a bug, open an issue in [`Github Issues`](issues). Provide the following information to help us
 understand and resolve the issue quickly:
 
 - A clear and descriptive title
@@ -59,7 +59,7 @@ understand and resolve the issue quickly:
 
 Intel welcomes suggestions for new features and improvements. Follow these steps to make a suggestion:
 
-- Check if there's already a similar suggestion in [`Github Issues`](issues).  
+- Check if there's already a similar suggestion in [`Github Issues`](issues).
 - If not, open a new issue and provide the following information:
    - A clear and descriptive title
    - A detailed description of the enhancement
@@ -75,7 +75,7 @@ Before submitting a pull request, ensure you follow these guidelines:
 - Test your changes thoroughly.
 - Document your changes (in code, readme, etc.).
 - Submit your pull request, detailing the changes and linking to any relevant issues.
-- Wait for a review. Intel will review your pull request as soon as possible and provide you with feedback. 
+- Wait for a review. Intel will review your pull request as soon as possible and provide you with feedback.
 You can expect a merge once your changes are validated with automatic tests and approved by maintainers.
 
 # Development Guidelines

@@ -27,10 +27,10 @@ Please refer to [Install Guide](./docs/source/get_started/install/install_guide_
 3. [Compile from source code](./docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md)
 4. [Build Docker image from source code](./docs/source/dev_guide/advanced_install/advanced_build_docker_image.md)
 
-To see the full list of installed components check the [dockerfile content for Ubuntu24](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/libraries/dl-streamer/docker/ubuntu/ubuntu24.Dockerfile)
+To see the full list of installed components check the [dockerfile content for Ubuntu24](https://github.com/open-edge-platform/dlstreamer/blob/master/docker/ubuntu/ubuntu24.Dockerfile)
 
 ## Samples
-[Samples](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples) available for C/C++ and Python programming, and as gst-launch command lines and scripts.
+[Samples](https://github.com/open-edge-platform/dlstreamer/tree/master/samples) available for C/C++ and Python programming, and as gst-launch command lines and scripts.
 
 ## NN models
 DL Streamer supports NN models in OpenVINO™ IR and ONNX* formats.

@@ -27,7 +27,7 @@ The complete solution leverages:
   | [gvamotiondetect](./docs/source/elements/gvamotiondetect.md) | Performs lightweight motion detection on NV12 video frames and emits motion regions of interest (ROIs) as analytics metadata. |
   | [gvapython](./docs/source/elements/gvapython.md) | Provides a callback to execute user-defined Python functions on every   frame. Can be used for metadata conversion, inference post-processing, and other tasks. |
   | [gvarealsense](./docs/source/elements/gvarealsense.md) | Provides integration with Intel RealSense cameras, enabling video and depth stream capture for use in GStreamer pipelines. |
-  | [gvatrack](./docs/source/elements/gvatrack.md) | Performs object tracking using zero-term, or imageless tracking algorithms.   Assigns unique object IDs to the tracked objects. | 
+  | [gvatrack](./docs/source/elements/gvatrack.md) | Performs object tracking using zero-term, or imageless tracking algorithms.   Assigns unique object IDs to the tracked objects. |
   | [gvawatermark](./docs/source/elements/gvawatermark.md) | Overlays the metadata on the video frame to visualize the inference   results. |
 
 For the details on supported platforms, please refer to [System Requirements](./docs/source/get_started/system_requirements.md).
@@ -236,7 +236,7 @@ For installing Pipeline Framework with the prebuilt binaries or Docker\* or to b
 |[preview] Enabled Intel® Arc™ B-Series Graphics [products formerly Battlemage] | Validated with Ubuntu 24.04, 6.12.3-061203-generic and the latest Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver v24.52.32224.5 + the latest public Intel Graphics Media Driver version + pre-rerelease Intel® Graphics Memory Management Library version |
 | OpenVINO 2024.6 support | Update to the latest version of OpenVINO |
 | Updated NPU driver | Updated NPU driver to 1.10.1 version. |
-| Bug fixing | Running multiple gstreamer pipeline objects in the same process on dGPU leads to error; DLStreamer docker image build is failing (2024.2.2 and 2024.3.0 versions); Fixed installation scripts: minor fixes of GPU, NPU installation section; Updated documentation: cleanup, added missed parts, added DLS system requirements |
+| Bug fixing | Running multiple gstreamer pipeline objects in the same process on dGPU leads to error; DL Streamer docker image build is failing (2024.2.2 and 2024.3.0 versions); Fixed installation scripts: minor fixes of GPU, NPU installation section; Updated documentation: cleanup, added missed parts, added DLS system requirements |
 
 
 # Deep Learning Streamer (DL Streamer) Pipeline Framework Release 2024.3.0
@@ -322,7 +322,7 @@ For installing Pipeline Framework with the prebuilt binaries or Docker\* or to b
 |----------------|------------------------|
 | [#425](https://github.com/dlstreamer/dlstreamer/issues/425) | when using inference-region=roi-list vs full-frame in my classification pipeline, classification data does not get published |
 | [#432](https://github.com/dlstreamer/dlstreamer/issues/432) | Installation issues with gst-ugly plugins |
-| [#397](https://github.com/dlstreamer/dlstreamer/issues/397) | Installation Error DLStreamer - Both Debian Packages and Compile from Sources |
+| [#397](https://github.com/dlstreamer/dlstreamer/issues/397) | Installation Error DL Streamer - Both Debian Packages and Compile from Sources |
 | Internal findings | custom efficientnetb0 fix, issue with selection region before inference, Geti classification model fix, dGPU vah264enc element not found error fix, sample: face_detection_and_classifiation fix|
 
 ## Deep Learning Streamer (DL Streamer) Pipeline Framework Release 2024.1.1
@@ -331,8 +331,8 @@ For installing Pipeline Framework with the prebuilt binaries or Docker\* or to b
 
 | **Title**      | **High-level description**      |
 |----------------|---------------------------------|
-| Missing git package | Git package added to DLStreamer docker runtime image |
-| VTune when running DLStreamer | Publish instructions to install and run VTune to analyze media + gpu when running DLStreamer  |
+| Missing git package | Git package added to DL Streamer docker runtime image |
+| VTune when running DL Streamer | Publish instructions to install and run VTune to analyze media + gpu when running DL Streamer  |
 | Update NPU drivers to version 1.5.0 | Update NPU driver version inside docker images|
 | Instance_segmentation sample | Add new Mask-RCNN segmentation sample |
 | Documentation updates | Enhance Performance Guide and Model Preparation section |
@@ -355,7 +355,7 @@ For installing Pipeline Framework with the prebuilt binaries or Docker\* or to b
 | Update OpenVINO version to latest one (2024.2.0) | Update OpenVINO version to latest one (2024.2.0) |
 | Release docker images on DockerHUB: runtime and dev | Release docker images on DockerHUB: runtime and dev |
 | Bugs fixing | Bug fixed: GPU not detected in Docker container Dlstreamer - MTL platform; Updated docker images with proper GPU and NPU packages; yolo5 model failed with batch-size >1; Remove excessive ‘mbind failed:...’ warning logs |
-| Documentation updates | Added sample applications for Mask-RCNN instance segmentation. Added list of supported models from Open Model Zoo and public repos. Added scripts to generate DLStreamer-consumable models from public repos. Document usage of ModelAPI properties in OpenVINO IR (model.xml) instead of creating custom model_proc files. Updated installation instructions for docker images. |
+| Documentation updates | Added sample applications for Mask-RCNN instance segmentation. Added list of supported models from Open Model Zoo and public repos. Added scripts to generate DL Streamer-consumable models from public repos. Document usage of ModelAPI properties in OpenVINO IR (model.xml) instead of creating custom model_proc files. Updated installation instructions for docker images. |
 
 ### Fixed issues
 
@@ -407,8 +407,8 @@ For installing Pipeline Framework with the prebuilt binaries or Docker\* or to b
 |----------------|------------------------|-----------------|-------------------------|
 | 390 | [How to install packages with sudo inside the docker container intel/dlstreamer:latest](https://github.com/dlstreamer/dlstreamer/issues/390) | start the container as mentioned above with root-user `(-u 0) docker run -it -u 0 --rm`... and then are able to update binaries | All |
 | 392 | [installation error dlstreamer with openvino 2023.2](https://github.com/dlstreamer/dlstreamer/issues/392) | 2024.0 version supports API 2.0 so I highly recommend to check it and in case if this problem is still valid please raise new issue | All |
-| 393 | [Debian file location for DL streamer 2022.3](https://github.com/dlstreamer/dlstreamer/issues/393) | Error no longer occurring for user | All |
-| 394 | [Custom YoloV5m Accuracy Drop in dlstreamer with model proc](https://github.com/dlstreamer/dlstreamer/issues/394) | Procedure to transform crowdhuman_yolov5m.pt model to the openvino version that can be used directly in DLstreamer with Yolo_v7 converter (no layer cutting required) * `git clone https://github.com/ultralytics/yolov5 * cd yolov5 * pip install -r requirements.txt openvino-dev * python export.py --weights crowdhuman_yolov5m.pt --include openvino` | All |
+| 393 | [Debian file location for DL Streamer 2022.3](https://github.com/dlstreamer/dlstreamer/issues/393) | Error no longer occurring for user | All |
+| 394 | [Custom YoloV5m Accuracy Drop in dlstreamer with model proc](https://github.com/dlstreamer/dlstreamer/issues/394) | Procedure to transform crowdhuman_yolov5m.pt model to the openvino version that can be used directly in DL Streamer with Yolo_v7 converter (no layer cutting required) * `git clone https://github.com/ultralytics/yolov5 * cd yolov5 * pip install -r requirements.txt openvino-dev * python export.py --weights crowdhuman_yolov5m.pt --include openvino` | All |
 | 396 | [Segfault when reuse same model with same model-instance-id.](https://github.com/dlstreamer/dlstreamer/issues/396) | 2024.0 version supports API 2.0 so I highly recommend to check it and in case if this problem is still valid please raise new issue | All |
 | 404 | [How to generate model proc file for yolov8?](https://github.com/dlstreamer/dlstreamer/issues/404) | Added as a feature in this release | All |
 | 406 | [yolox support](https://github.com/dlstreamer/dlstreamer/issues/406) | Added as a feature in this release | All |
@@ -445,7 +445,7 @@ For more detailed instructions please refer to [DL Streamer Pipeline Framework i
 
 ## Samples
 
-The [samples](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples) folder in DL Streamer Pipeline Framework repository contains command line, C++ and Python examples.
+The [samples](https://github.com/open-edge-platform/dlstreamer/tree/master/samples) folder in DL Streamer Pipeline Framework repository contains command line, C++ and Python examples.
 
 ## Legal Information
 

@@ -24,9 +24,8 @@ interfaces:
 
 ![c++-interfaces-and-base-classes](../_images/c++-interfaces-and-base-classes.svg)
 
-Many examples how to create C++ elements can be found on github
-repository in [folder
-src](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/src)
+Many examples how to create C++ elements can be found on the github
+repository in the [src folder](https://github.com/open-edge-platform/dlstreamer/tree/master/src)
 and sub-folders.
 
 ## Element description
@@ -86,7 +85,7 @@ auto ffmpeg_source = create_source(ffmpeg_multi_source, {{"inputs", inputs}}, ff
 ```
 
 See direct programming samples
-[ffmpeg_openvino](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/ffmpeg_openvino)
+[ffmpeg_openvino](https://github.com/open-edge-platform/dlstreamer/tree/master/samples/ffmpeg_openvino)
 and
-[ffmpeg_dpcpp](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/ffmpeg_dpcpp)
+[ffmpeg_dpcpp](https://github.com/open-edge-platform/dlstreamer/tree/master/samples/ffmpeg_dpcpp)
 for examples.
@@ -1,8 +1,8 @@
-# ① Memory Interop and C++ abstract interfaces
+# Memory Interop and C++ abstract interfaces
 
-Deep Learning Streamer provides independent sub-component for zero-copy
+Deep Learning Streamer (DL Streamer) provides an independent sub-component for zero-copy
 buffer sharing and memory interop between various frameworks and memory
-handles on CPU and GPU
+handles on the CPU and GPU.
 
 - CPU memory `void*`
 - FFmpeg `AVFrame`
@@ -17,10 +17,10 @@ handles on CPU and GPU
 
 The memory interop sub-component is available via APT installation
 `sudo apt install intel-dlstreamer-cpp` and on
-[github](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/include/dlstreamer).
+[github](https://github.com/open-edge-platform/dlstreamer/tree/master/include/dlstreamer).
 
-> **Note:** This sub-component implemented as C++ header-only library. Python
-> bindings for this library coming in next releases.
+> **Note:** This sub-component is implemented as a C++ header-only library. Python
+> bindings for this library will be coming in future releases.
 
 ## Why memory interop library?
 
@@ -97,14 +97,11 @@ pre-allocated native memory object to C++ constructor (wrap already
 allocated object) or passing allocation parameters to C++ constructor
 (allocate new memory).
 
-Many examples how to allocate memory and create and use memory mappers
-can be found by searching word `mapper` in [samples
-https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples\>]{.title-ref}\_\_
-and [src
-https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/src\>]{.title-ref}\_\_
-folders on github source code, for example FFmpeg+DPCPP sample
-[rgb_to_grayscale
-https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/ffmpeg_dpcpp/rgb_to_grayscale\>]()
+Many examples on how to allocate memory and create and use memory mappers
+can be found by searching for the word `mapper` in [samples](https://github.com/open-edge-platform/dlstreamer/tree/master/samples)
+and [src](https://github.com/open-edge-platform/dlstreamer/tree/master/src)
+folders on github source code, like for example in the FFmpeg+DPCPP sample
+[rgb_to_grayscale](https://github.com/open-edge-platform/dlstreamer/tree/master/samples/ffmpeg_dpcpp/rgb_to_grayscale)
 and almost every C++ element.
 
 There is special mapper

@@ -1,19 +1,19 @@
 # PyTorch tensor inference
 
-DLStreamer supports PyTorch inference backend via the
+Deep Learning Streamer (DL Streamer) supports PyTorch inference backend via the
 `pytorch_tensor_inference` GStreamer element implemented in Python.
-Element description can be found in
-[Elements 2.0 reference](elements_list) .
+Element description can be found in [Elements 2.0 reference](elements_list).
 
 ## Prior to the first run
 
 Before using `pytorch_tensor_inference`, make sure that all of the
 following requirements are met. Visit
-[Install Guide](../get_started/install/install_guide_ubuntu.md) for more information about installing DLStreamer.
+[Install Guide](../get_started/install/install_guide_ubuntu.md) for more information about
+installing DL Streamer.
 
 1. `intel-dlstreamer-gst-python3-plugin-loader` and
-   `intel-dlstreamer-gst-python3` packages are installed. If not, add
-   DLStreamer apt repository and install the following packages:
+   `intel-dlstreamer-gst-python3` packages are installed. If not, add a DL Streamer apt
+   repository and install the following packages:
 
    ```bash
    apt-get update
@@ -28,7 +28,7 @@ following requirements are met. Visit
    python3 -m pip install -r requirements.txt
    ```
 
-3. DLStreamer environment has been configured. If not:
+3. DL Streamer environment has been configured. If not:
 
    ```bash
    source /opt/intel/dlstreamer/setupvars.sh
@@ -70,7 +70,7 @@ model. To obtain the size of the output tensors during caps negotiations
 phase, inference is performed on an random tensor, the size of which
 will be set in accordance with the capabilities.
 
-## DLStreamer pipelines with pytorch_tensor_inference
+## DL Streamer pipelines with pytorch_tensor_inference
 
 Below is an example using the `pytorch_tensor_inference` element in
 pipeline to classify objects. This example uses the `resnet50` model

@@ -14,7 +14,7 @@ Follow the instructions in
 ## Step 2: Download Dockerfiles
 
 All Dockerfiles are in
-[DLStreamer GitHub repository](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/docker).
+[DLStreamer GitHub repository](https://github.com/open-edge-platform/dlstreamer/tree/master/docker).
 
 <!--hide_directive::::{tab-set}
 :::{tab-item}hide_directive--> Ubuntu24 debian/dev Dockerfile