diff --git a/CMakeLists.txt b/CMakeLists.txt index 7b985bc5..3eeb4558 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -44,7 +44,7 @@ set(PLUGIN_VERSION ${PRODUCT_VERSION}) add_definitions(-DPLUGIN_VERSION="${PLUGIN_VERSION}") add_definitions(-DPACKAGE="dlstreamer") add_definitions(-DPACKAGE_NAME="Deep Learning Streamer elements") -add_definitions(-DGST_PACKAGE_ORIGIN="https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer") +add_definitions(-DGST_PACKAGE_ORIGIN="https://github.com/open-edge-platform/dlstreamer/tree/master") add_definitions(-DPLUGIN_LICENSE="MIT/X11") add_definitions(-DPRODUCT_FULL_NAME="Deep Learning Streamer") if(WIN32) diff --git a/docker/fedora41/intel-dlstreamer.spec b/docker/fedora41/intel-dlstreamer.spec index c891dfa9..67cbbd8d 100644 --- a/docker/fedora41/intel-dlstreamer.spec +++ b/docker/fedora41/intel-dlstreamer.spec @@ -5,7 +5,7 @@ Summary: Deep Learning Streamer License: Proprietary Source0: %{name}-%{version}.tar.gz -URL: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer +URL: https://github.com/open-edge-platform/dlstreamer/tree/master Packager: DL Streamer Team ExclusiveArch: x86_64 AutoReqProv: no diff --git a/docker/ubuntu/debian/control b/docker/ubuntu/debian/control index 66bc2eeb..56ce1bb0 100644 --- a/docker/ubuntu/debian/control +++ b/docker/ubuntu/debian/control @@ -8,6 +8,6 @@ Package: intel-dlstreamer Architecture: amd64 Replaces: intel-dlstreamer Multi-Arch: same -Homepage: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer +Homepage: https://github.com/open-edge-platform/dlstreamer/tree/master Description: Intel(R) Deep Learning Streamer Depends: ${misc:Depends}, ${shlibs:Depends}, libglib2.0-0t64, libjpeg-turbo8, libdrm2, libwayland-bin, libwayland-client0, libx11-6, libpng16-16t64, libva-drm2, libcurl4t64, libde265-0, libxext6, libva-x11-2, libgl1, libglx-mesa0, libva-wayland2, openexr, libgudev-1.0-0, vainfo, libpython3.12t64, python3, python3-gi, python3-gi-cairo, libcairo2, libvpx9, libopus0, libsrtp2-1, libxv1, libtbb12, libpaho-mqtt1.3, ffmpeg, libgirepository-1.0-1, libsoup-3.0-0, openvino-2025.3.0 diff --git a/docker/ubuntu/debian/control-ubuntu22 b/docker/ubuntu/debian/control-ubuntu22 index 4ef5f3c9..3a7e5e15 100644 --- a/docker/ubuntu/debian/control-ubuntu22 +++ b/docker/ubuntu/debian/control-ubuntu22 @@ -8,6 +8,6 @@ Package: intel-dlstreamer Architecture: amd64 Replaces: intel-dlstreamer Multi-Arch: same -Homepage: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer +Homepage: https://github.com/open-edge-platform/dlstreamer/tree/master Description: Intel(R) Deep Learning Streamer Depends: ${misc:Depends}, ${shlibs:Depends}, libglib2.0-dev, libjpeg-turbo8, libdrm2, libwayland-bin, libwayland-client0, libx11-6, libpng16-16, libva-drm2, libcurl4, libde265-0, libxext6, libva-x11-2, libgl1, libglx-mesa0, libva-wayland2, openexr, libgudev-1.0-0, vainfo, python3-pip, python3-gi, python3-gi-cairo, libcairo2, libvpx7, libopus0, libsrtp2-1, libxv1, libtbb12, libpaho-mqtt1.3, ffmpeg, libgirepository-1.0-1, libsoup-3.0-0, openvino-2025.3.0 diff --git a/docs/scripts/all_models.yaml b/docs/scripts/all_models.yaml index c05d8a31..5dda4bef 100644 --- a/docs/scripts/all_models.yaml +++ b/docs/scripts/all_models.yaml @@ -29,8 +29,8 @@ YOLOv5: task_type: detection readme: https://dlstreamer.github.io/dev_guide/yolo_models.html source_readme: https://github.com/ultralytics/yolov5/releases/tag/v7.0 - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v5.json + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v5.json files: - source: https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt YOLOv7: @@ -39,8 +39,8 @@ YOLOv7: task_type: detection readme: https://dlstreamer.github.io/dev_guide/yolo_models.html source_readme: https://github.com/WongKinYiu/yolov7 - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v7.json + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v7.json YOLOv8: source: public format: pytorch @@ -96,8 +96,8 @@ YOLOX: task_type: detection readme: https://dlstreamer.github.io/dev_guide/yolo_models.html source_readme: https://github.com/Megvii-BaseDetection/YOLOX - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-x.json + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-x.json ch_PP-OCRv4_rec_infer: source: public format: paddle @@ -139,7 +139,7 @@ mask_rcnn_inception_resnet_v2_atrous_coco: source: public task_type: instance_segmentation tf_devices: CPU - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/mask-rcnn.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/mask-rcnn.json mask_rcnn_resnet50_atrous_coco: Source framework: TensorFlow\* Type: Instance segmentation @@ -157,7 +157,7 @@ mask_rcnn_resnet50_atrous_coco: source: public task_type: instance_segmentation tf_devices: CPU - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/mask-rcnn.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/mask-rcnn.json efficientnet-v2-b0: Source framework: PyTorch\* @@ -170,9 +170,9 @@ efficientnet-v2-b0: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_demo/python - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp_gapi - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: efficientnet-v2-b0 openvino_devices: CPU pytorch_devices: CPU @@ -190,9 +190,9 @@ efficientnet-v2-s: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_demo/python - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp_gapi - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: efficientnet-v2-s openvino_devices: CPU pytorch_devices: CPU @@ -246,7 +246,7 @@ aclnet: The model output for "AclNet" is the sound classifier output for the 53 different environmental sound classes from the internal sound database.' license: https://raw.githubusercontent.com/opencv/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/aclnet.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/aclnet.json name: aclnet openvino_devices: CPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/aclnet @@ -290,7 +290,7 @@ action-recognition-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/action_recognition_demo/python description: General-purpose action recognition model for Kinetics-400 dataset based on Video Transformer Network approach. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/kinetics_400.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/kinetics_400.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE module: custom_evaluators.sequential_action_recognition_evaluator.SequentialActionRecognitionEvaluator name: action-recognition-0001 @@ -319,7 +319,7 @@ age-gender-recognition-retail-0013: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/interactive_face_detection_demo/cpp description: Age & gender classification. Used in Audience Analytics. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/age-gender-recognition-retail-0013.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/age-gender-recognition-retail-0013.json name: age-gender-recognition-retail-0013 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/age-gender-recognition-retail-0013 @@ -336,7 +336,7 @@ anti-spoof-mn3: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/interactive_face_detection_demo/cpp_gapi - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/interactive_face_detection_demo/cpp license: https://raw.githubusercontent.com/kirillProkofiev/training_extensions/kp/antispoofing/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/anti-spoof-mn3.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/anti-spoof-mn3.json name: anti-spoof-mn3 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/anti-spoof-mn3 @@ -743,9 +743,9 @@ densenet-121-tf: group of models designed to perform image classification. For details, see TensorFlow* API docs , repository and paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/tensorflow/tensorflow/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: densenet-121-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/densenet-121-tf @@ -797,9 +797,9 @@ dla-34: PyTorch*. All DLA (Deep Layer Aggregation) classification models have been pre-trained on the ImageNet dataset. For details about this family of models, check out the Code for the CVPR Paper "Deep Layer Aggregation" . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/ucbdrive/dla/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: dla-34 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -815,7 +815,7 @@ driver-action-recognition-adas-0002: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/action_recognition_demo/python - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/action_recognition_demo/python description: Video Transformer Network for driver action recognition. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/driver_actions.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/driver_actions.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE module: custom_evaluators.sequential_action_recognition_evaluator.SequentialActionRecognitionEvaluator name: driver-action-recognition-adas-0002 @@ -873,7 +873,7 @@ efficientdet-d0-tf: All the EfficientDet models have been pre-trained on the Common Objects in Context (COCO) image database. For details about this family of models, check out the Google AutoML repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl.txt license: https://raw.githubusercontent.com/google/automl/master/LICENSE name: efficientdet-d0-tf openvino_devices: CPU, GPU @@ -898,7 +898,7 @@ efficientdet-d1-tf: All the EfficientDet models have been pre-trained on the Common Objects in Context (COCO) image database. For details about this family of models, check out the Google AutoML repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl.txt license: https://raw.githubusercontent.com/google/automl/master/LICENSE name: efficientdet-d1-tf openvino_devices: CPU, GPU @@ -923,9 +923,9 @@ efficientnet-b0: TensorFlow*. All the EfficientNet models have been pre-trained on the ImageNet image database. For details about this family of models, check out the TensorFlow Cloud TPU repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/tensorflow/tpu/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: efficientnet-b0 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/efficientnet-b0 @@ -963,9 +963,9 @@ efficientnet-b0-pytorch: The model output for "efficientnet-b0-pytorch" is the typical object classifier output for 1000 different classifications matching those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/rwightman/gen-efficientnet-pytorch/a36e2b2cd1bd122a508a6fffeaa7606890f8c882/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: efficientnet-b0-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -988,7 +988,7 @@ emotions-recognition-retail-0003: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/interactive_face_detection_demo/cpp description: Recognizes 5 emotions for a face. Targeted for Retail Audience Analytics. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/emotions-recognition-retail-0003.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/emotions-recognition-retail-0003.json name: emotions-recognition-retail-0003 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/emotions-recognition-retail-0003 @@ -1044,7 +1044,7 @@ face-detection-0200: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Face Detection based on MobileNetV2 (SSD). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-0200.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-0200.json name: face-detection-0200 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-0200 @@ -1061,7 +1061,7 @@ face-detection-0202: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Face Detection based on MobileNetV2 (SSD). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-0202.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-0202.json name: face-detection-0202 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-0202 @@ -1078,7 +1078,7 @@ face-detection-0204: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Face Detection based on MobileNetV2 (SSD). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-0204.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-0204.json name: face-detection-0204 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-0204 @@ -1095,7 +1095,7 @@ face-detection-0205: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Face Detection based on MobileNetV2 (FCOS). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-0205.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-0205.json name: face-detection-0205 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-0205 @@ -1112,7 +1112,7 @@ face-detection-0206: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Face Detection based on ResNet152 (ATSS). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-0206.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-0206.json name: face-detection-0206 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-0206 @@ -1154,7 +1154,7 @@ face-detection-adas-0001: description: Face Detection (MobileNet with reduced channels + SSD with weights sharing) license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-adas-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-adas-0001.json name: face-detection-adas-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-adas-0001 @@ -1188,7 +1188,7 @@ face-detection-retail-0004: description: Face Detection (SqNet1.0modif+single scale) without BatchNormalization trained with negatives. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-retail-0004.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-retail-0004.json name: face-detection-retail-0004 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-retail-0004 @@ -1221,7 +1221,7 @@ face-detection-retail-0005: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/interactive_face_detection_demo/cpp description: Face Detection based on MobileNetV2. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/face-detection-retail-0005.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/face-detection-retail-0005.json name: face-detection-retail-0005 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/face-detection-retail-0005 @@ -1329,7 +1329,7 @@ facial-landmarks-35-adas-0002: description: Custom-architecture convolutional neural network for 35 facial landmarks estimation. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/facial-landmarks-35-adas-0002.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/facial-landmarks-35-adas-0002.json name: facial-landmarks-35-adas-0002 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/facial-landmarks-35-adas-0002 @@ -1344,7 +1344,7 @@ facial-landmarks-98-detection-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/gaze_estimation_demo/cpp description: Landmark's detection. 98 points. Trained on the internal dataset license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/facial-landmarks-98-detection-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/facial-landmarks-98-detection-0001.json name: facial-landmarks-98-detection-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/facial-landmarks-98-detection-0001 @@ -1402,9 +1402,9 @@ faster_rcnn_inception_resnet_v2_atrous_coco: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Faster R-CNN with Inception ResNet v2 Atrous version. Used for object detection. For details see the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-image-info.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-image-info.json name: faster_rcnn_inception_resnet_v2_atrous_coco openvino_devices: CPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/faster_rcnn_inception_resnet_v2_atrous_coco @@ -1423,9 +1423,9 @@ faster_rcnn_resnet50_coco: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Faster R-CNN ResNet-50 model. Used for object detection. For details, see the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-image-info.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-image-info.json name: faster_rcnn_resnet50_coco openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/faster_rcnn_resnet50_coco @@ -1641,9 +1641,9 @@ googlenet-v1-tf: model has been pre-trained on the ImageNet image database. For details about this family of models, check out the paper , repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://github.com/tensorflow/models/blob/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: googlenet-v1-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/googlenet-v1-tf @@ -1667,9 +1667,9 @@ googlenet-v2-tf: model has been pre-trained on the ImageNet image database. For details about this family of models, check out the paper , repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://github.com/tensorflow/models/blob/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: googlenet-v2-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/googlenet-v2-tf @@ -1689,9 +1689,9 @@ googlenet-v3: description: The "googlenet-v3" model is the first of the Inception family of models designed to perform image classification. For details about this family of models, check out the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: googlenet-v3 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/googlenet-v3 @@ -1714,9 +1714,9 @@ googlenet-v3-pytorch: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp_gapi - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: googlenet-v3-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -1740,9 +1740,9 @@ googlenet-v4-tf: the "googlenet-v4-tf" model has been pre-trained on the ImageNet image database. For details about this family of models, check out the paper , repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://github.com/tensorflow/models/blob/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: googlenet-v4-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/googlenet-v4-tf @@ -1850,9 +1850,9 @@ hbonet-0.25: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp description: The "hbonet-0.25" model is one of the classification models from repository with "width_mult=0.25" - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://github.com/d-li14/HBONet/blob/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: hbonet-0.25 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -1873,9 +1873,9 @@ hbonet-1.0: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp description: The "hbonet-1.0" model is one of the classification models from repository with "width_mult=1.0" - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://github.com/d-li14/HBONet/blob/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: hbonet-1.0 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -1945,7 +1945,7 @@ horizontal-text-detection-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/text_detection_demo/cpp description: Horizontal text detector based on FCOS with light MobileNetV2 backbone license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/horizontal-text-detection-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/horizontal-text-detection-0001.json name: horizontal-text-detection-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/horizontal-text-detection-0001 @@ -1993,7 +1993,7 @@ human-pose-estimation-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/human_pose_estimation_demo/python description: 2D human pose estimation with tuned mobilenet backbone (based on OpenPose). license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/human-pose-estimation-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/human-pose-estimation-0001.json name: human-pose-estimation-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/human-pose-estimation-0001 @@ -2183,9 +2183,9 @@ inception-resnet-v2-tf: description: The "inception-resnet-v2" model is one of the Inception family of models designed to perform image classification. For details about this family of models, check out the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: inception-resnet-v2-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/inception-resnet-v2-tf @@ -2234,7 +2234,7 @@ instance-segmentation-security-0002: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/whiteboard_inpainting_demo/python description: General purpose instance segmentation model. Mask R-CNN with ResNet50 backbone - FPN - RPN - detection and segmentation heads. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE name: instance-segmentation-security-0002 openvino_devices: CPU @@ -2260,7 +2260,7 @@ instance-segmentation-security-0091: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/whiteboard_inpainting_demo/python description: General purpose instance segmentation model. Cascade Mask R-CNN with ResNet101 backbone and deformable convolution V2. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE name: instance-segmentation-security-0091 openvino_devices: CPU @@ -2284,7 +2284,7 @@ instance-segmentation-security-0228: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/whiteboard_inpainting_demo/python description: General purpose instance segmentation model. Mask R-CNN with ResNet101 backbone and light FPN - RPN - detection and segmentation heads. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE name: instance-segmentation-security-0228 openvino_devices: CPU @@ -2308,7 +2308,7 @@ instance-segmentation-security-1039: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/whiteboard_inpainting_demo/python description: General purpose instance segmentation model. Mask R-CNN with EfficientNet-B2 backbone and light FPN - RPN - detection and segmentation heads. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE name: instance-segmentation-security-1039 openvino_devices: CPU @@ -2332,7 +2332,7 @@ instance-segmentation-security-1040: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/whiteboard_inpainting_demo/python description: General purpose instance segmentation model. Mask R-CNN with EfficientNet-B2 backbone and light FPN - RPN - detection and segmentation heads. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE name: instance-segmentation-security-1040 openvino_devices: CPU @@ -2354,7 +2354,7 @@ landmarks-regression-retail-0009: description: Landmark's detection. Used in Smart Classroom. The model is identical to 0002 but trained on internal dataset with improved regression loss. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/landmarks-regression-retail-0009.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/landmarks-regression-retail-0009.json name: landmarks-regression-retail-0009 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/landmarks-regression-retail-0009 @@ -2423,7 +2423,7 @@ license-plate-recognition-barrier-0007: description: This model uses a small-footprint network trained end-to-end to recognize Chinese license plates in traffic. license: https://raw.githubusercontent.com/opencv/training_toolbox_tensorflow/develop/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/license-plate-recognition-barrier-0007.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/license-plate-recognition-barrier-0007.json name: license-plate-recognition-barrier-0007 openvino_devices: CPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/license-plate-recognition-barrier-0007 @@ -2522,9 +2522,9 @@ mixnet-l: All the MixNet models have been pretrained on the ImageNet* image database. For details about this family of models, check out the TensorFlow Cloud TPU repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/tensorflow/tpu/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mixnet-l openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mixnet-l @@ -2545,9 +2545,9 @@ mobilenet-v1-0.25-128: use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used. For details, see paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v1-0.25-128 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v1-0.25-128 @@ -2568,9 +2568,9 @@ mobilenet-v1-1.0-224-tf: use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used. For details, see the paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v1-1.0-224-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v1-1.0-224-tf @@ -2591,9 +2591,9 @@ mobilenet-v2-1.0-224: a variety of use cases. They can be used for classification, detection, embeddings, and segmentation like other popular large-scale models. For details, see the paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v2-1.0-224 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v2-1.0-224 @@ -2614,9 +2614,9 @@ mobilenet-v2-1.4-224: use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models are used. For details, see the paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v2-1.4-224 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v2-1.4-224 @@ -2645,9 +2645,9 @@ mobilenet-v2-pytorch: The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v2-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -2667,9 +2667,9 @@ mobilenet-v3-large-1.0-224-tf: of MobileNets, based on a combination of complementary search techniques as well as a novel architecture design. "mobilenet-v3-large-1.0-224-tf" is targeted for high resource use cases. For details see paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v3-large-1.0-224-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v3-large-1.0-224-tf @@ -2689,9 +2689,9 @@ mobilenet-v3-small-1.0-224-tf: of MobileNets, based on a combination of complementary search techniques as well as a novel architecture design. "mobilenet-v3-small-1.0-224-tf" is targeted for low resource use cases. For details see paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: mobilenet-v3-small-1.0-224-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-v3-small-1.0-224-tf @@ -2714,7 +2714,7 @@ mobilenet-yolo-v4-syg: paper of MobileNetV2 and YOLOv4 license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE mAP: 86.35% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/blob/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/mobilenet-yolo-v4-syg.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/mobilenet-yolo-v4-syg.json name: mobilenet-yolo-v4-syg openvino_devices: CPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/mobilenet-yolo-v4-syg @@ -2922,9 +2922,9 @@ nfnet-f0: For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: nfnet-f0 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/nfnet-f0 @@ -2981,7 +2981,7 @@ open-closed-eye-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/gaze_estimation_demo/cpp description: Fully convolutional network for recognition of eye state ('open', 'closed'). license: https://raw.githubusercontent.com/opencv/openvino_training_extensions/develop/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/open-closed-eye-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/open-closed-eye-0001.json name: open-closed-eye-0001 openvino_devices: CPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/open-closed-eye-0001 @@ -3007,7 +3007,7 @@ pedestrian-and-vehicle-detector-adas-0001: description: Pedestrian and Vehicle detector based on ssd + mobilenet with reduced channels number. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/pedestrian-and-vehicle-detector-adas-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/pedestrian-and-vehicle-detector-adas-0001.json name: pedestrian-and-vehicle-detector-adas-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/pedestrian-and-vehicle-detector-adas-0001 @@ -3031,7 +3031,7 @@ pedestrian-detection-adas-0002: description: Pedestrian detector based on ssd + mobilenet with reduced channels number. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/pedestrian-detection-adas-0002.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/pedestrian-detection-adas-0002.json name: pedestrian-detection-adas-0002 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/pedestrian-detection-adas-0002 @@ -3060,7 +3060,7 @@ person-attributes-recognition-crossroad-0230: description: Pedestrian attributes recognition based on a PVANet with hyperfeatures backbone + classification head license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0230.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0230.json name: person-attributes-recognition-crossroad-0230 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-attributes-recognition-crossroad-0230 @@ -3087,7 +3087,7 @@ person-attributes-recognition-crossroad-0234: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/crossroad_camera_demo/cpp description: Pedestrian attributes recognition based on ResNet-50 backbone license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0234.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0234.json name: person-attributes-recognition-crossroad-0234 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-attributes-recognition-crossroad-0234 @@ -3114,7 +3114,7 @@ person-attributes-recognition-crossroad-0238: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/crossroad_camera_demo/cpp description: Pedestrian attributes recognition based on Inception V3 backbone license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0238.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-attributes-recognition-crossroad-0238.json name: person-attributes-recognition-crossroad-0238 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-attributes-recognition-crossroad-0238 @@ -3153,7 +3153,7 @@ person-detection-0200: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/social_distance_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 256x256 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-0200.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-0200.json name: person-detection-0200 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-0200 @@ -3174,7 +3174,7 @@ person-detection-0201: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/social_distance_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 384x384 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-0201.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-0201.json name: person-detection-0201 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-0201 @@ -3195,7 +3195,7 @@ person-detection-0202: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/social_distance_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 512x512 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-0202.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-0202.json name: person-detection-0202 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-0202 @@ -3214,7 +3214,7 @@ person-detection-0203: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/pedestrian_tracker_demo/cpp description: Person Detection based on MobileNetV2 (ATSS) on 864x480 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-0203.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-0203.json name: person-detection-0203 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-0203 @@ -3381,7 +3381,7 @@ person-detection-asl-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/gesture_recognition_demo/python description: Person detector (ShuffleNetv2 backbone and FCOS head) for ASL scenario license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-0203.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-0203.json name: person-detection-asl-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-asl-0001 @@ -3470,7 +3470,7 @@ person-detection-retail-0013: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/social_distance_demo/cpp description: Pedestrian detection (RMNet with lrelu + SSD) license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-detection-retail-0013.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-detection-retail-0013.json name: person-detection-retail-0013 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-detection-retail-0013 @@ -3585,7 +3585,7 @@ person-vehicle-bike-detection-2000: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/pedestrian_tracker_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 256x256 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2000.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2000.json name: person-vehicle-bike-detection-2000 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-2000 @@ -3604,7 +3604,7 @@ person-vehicle-bike-detection-2001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/pedestrian_tracker_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 384x484 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2001.json name: person-vehicle-bike-detection-2001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-2001 @@ -3623,7 +3623,7 @@ person-vehicle-bike-detection-2002: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/pedestrian_tracker_demo/cpp description: MobileNetV2-SSD with two heads and clustered priors for 512x512 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2002.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2002.json name: person-vehicle-bike-detection-2002 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-2002 @@ -3643,7 +3643,7 @@ person-vehicle-bike-detection-2003: description: Person Vehicle Bike Detection based on MobileNetV2 (ATSS) on 864x480 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2003.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2003.json name: person-vehicle-bike-detection-2003 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-2003 @@ -3663,7 +3663,7 @@ person-vehicle-bike-detection-2004: description: Person Vehicle Bike Detection based on MobileNetV2 (ATSS) on 448x256 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2004.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-2004.json name: person-vehicle-bike-detection-2004 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-2004 @@ -3696,7 +3696,7 @@ person-vehicle-bike-detection-crossroad-0078: detection architecture - RMNet backbone and learnable image downscale block (person-vehicle-bike-detection-crossroad-0066 with extra pooling) license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-0078.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-0078.json name: person-vehicle-bike-detection-crossroad-0078 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-crossroad-0078 @@ -3728,7 +3728,7 @@ person-vehicle-bike-detection-crossroad-1016: description: Multiclass (person - vehicle - non-vehicle) detector based on SSD detection architecture - MobileNetV2 backbone) license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-1016.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-1016.json name: person-vehicle-bike-detection-crossroad-1016 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-crossroad-1016 @@ -3766,7 +3766,7 @@ person-vehicle-bike-detection-crossroad-yolov3-1020: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/single_human_pose_estimation_demo/python description: YOLO v3 finetuned for person-vehicle-bike detection task license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-yolov3-1020.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/person-vehicle-bike-detection-crossroad-yolov3-1020.json name: person-vehicle-bike-detection-crossroad-yolov3-1020 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/person-vehicle-bike-detection-crossroad-yolov3-1020 @@ -3783,7 +3783,7 @@ product-detection-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Product detection based on MobileNetV2. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/product-detection-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/product-detection-0001.json name: product-detection-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/product-detection-0001 @@ -3845,9 +3845,9 @@ regnetx-3.2gf: model was pre-trained in PyTorch*. All RegNet classification models have been pre-trained on the ImageNet dataset. For details about this family of models, check out the Codebase for Image Classification Research . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/facebookresearch/pycls/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: regnetx-3.2gf openvino_devices: CPU, GPU pytorch_devices: CPU @@ -3881,9 +3881,9 @@ repvgg-a0: matching with those in the ImageNet database. For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/DingXiaoH/RepVGG/main/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: repvgg-a0 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -3917,9 +3917,9 @@ repvgg-b1: matching with those in the ImageNet database. For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/DingXiaoH/RepVGG/main/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: repvgg-b1 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -3953,9 +3953,9 @@ repvgg-b3: matching with those in the ImageNet database. For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/DingXiaoH/RepVGG/main/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: repvgg-b3 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -3986,9 +3986,9 @@ resnest-50-pytorch: For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/zhanghang1989/ResNeSt/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: resnest-50-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4017,9 +4017,9 @@ resnet-18-pytorch: The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: resnet-18-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4048,9 +4048,9 @@ resnet-34-pytorch: The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: resnet-34-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4079,9 +4079,9 @@ resnet-50-pytorch: The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: resnet-50-pytorch openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4102,9 +4102,9 @@ resnet-50-tf: classification model pre-trained on the ImageNet dataset. Originally redistributed in Saved model format, converted to frozen graph using "tf.graph_util" module. For details see paper , repository .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: resnet-50-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/resnet-50-tf @@ -4125,7 +4125,7 @@ resnet18-xnor-binary-onnx-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/classification_benchmark_demo/cpp description: ResNet-18 Binary with XNOR weight binarization license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/resnet18-xnor-binary-onnx-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/resnet18-xnor-binary-onnx-0001.json name: resnet18-xnor-binary-onnx-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/resnet18-xnor-binary-onnx-0001 @@ -4147,7 +4147,7 @@ resnet50-binary-0001: description: ResNet-50 Binary fp32 conv MFlops: '960' license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/resnet50-binary-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/resnet50-binary-0001.json name: resnet50-binary-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/resnet50-binary-0001 @@ -4190,7 +4190,7 @@ retinanet-tf: description: RetinaNet is the dense object detection model with ResNet50 backbone, originally trained on Keras*, then converted to TensorFlow* protobuf format. For details, see paper , repository . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/fizyr/keras-retinanet/master/LICENSE name: retinanet-tf openvino_devices: CPU @@ -4221,9 +4221,9 @@ rexnet-v1-x1.0: with those in the ImageNet database. For details see repository and paper .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/clovaai/rexnet/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: rexnet-v1-x1.0 openvino_devices: CPU pytorch_devices: CPU @@ -4245,10 +4245,10 @@ rfcn-resnet101-coco-tf: description: R-FCN ResNet-101 model, pre-trained on Common Objects in Context (COCO) dataset. Used for object detection. For details, see the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE mAP: 45.02% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-image-info.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-image-info.json name: rfcn-resnet101-coco-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/rfcn-resnet101-coco-tf @@ -4301,9 +4301,9 @@ shufflenet-v2-x1.0: The model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/pytorch/vision/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: shufflenet-v2-x1.0 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4319,7 +4319,7 @@ single-human-pose-estimation-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/single_human_pose_estimation_demo/python description: Single human pose estimation model based on paper . license: https://raw.githubusercontent.com/opencv/openvino_training_extensions/develop/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/single-human-pose-estimation-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/single-human-pose-estimation-0001.json name: single-human-pose-estimation-0001 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4503,7 +4503,7 @@ ssd_mobilenet_v1_coco: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/single_human_pose_estimation_demo/python description: The "ssd_mobilenet_v1_coco" model is a Single-Shot multibox Detection (SSD) network intended to perform object detection. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE name: ssd_mobilenet_v1_coco openvino_devices: CPU, GPU @@ -4528,7 +4528,7 @@ ssd_mobilenet_v1_fpn_coco: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/single_human_pose_estimation_demo/python description: MobileNetV1 FPN is used for object detection. For details, see the paper . - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE name: ssd_mobilenet_v1_fpn_coco openvino_devices: CPU, GPU @@ -4554,7 +4554,7 @@ ssdlite_mobilenet_v2: description: 'The "ssdlite_mobilenet_v2" model is used for object detection. For details, see the paper , MobileNetV2: Inverted Residuals and Linear Bottlenecks.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_91cl_bkgr.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_91cl_bkgr.txt license: https://raw.githubusercontent.com/tensorflow/models/master/LICENSE name: ssdlite_mobilenet_v2 openvino_devices: CPU, GPU @@ -4584,9 +4584,9 @@ swin-tiny-patch4-window7-224: More details provided in the paper and repository .' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/imagenet_2012.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/imagenet_2012.txt license: https://raw.githubusercontent.com/rwightman/pytorch-image-models/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/preproc-aspect-ratio.json name: swin-tiny-patch4-window7-224 openvino_devices: CPU, GPU pytorch_devices: CPU @@ -4872,7 +4872,7 @@ vehicle-attributes-recognition-barrier-0039: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/security_barrier_camera_demo/cpp description: Vehicle attributes recognition with modified ResNet10 backbone license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0039.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0039.json name: vehicle-attributes-recognition-barrier-0039 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-attributes-recognition-barrier-0039 @@ -4901,7 +4901,7 @@ vehicle-attributes-recognition-barrier-0042: gray: 77.47% green: 81.82% license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0042.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-attributes-recognition-barrier-0042.json name: vehicle-attributes-recognition-barrier-0042 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-attributes-recognition-barrier-0042 @@ -4923,7 +4923,7 @@ vehicle-detection-0200: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: MobileNetV2-SSD with two heads and clustered priors for 256x256 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-detection-0200.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-detection-0200.json name: vehicle-detection-0200 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-detection-0200 @@ -4940,7 +4940,7 @@ vehicle-detection-0201: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: MobileNetV2-SSD with two heads and clustered priors for 384x384 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-detection-0201.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-detection-0201.json name: vehicle-detection-0201 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-detection-0201 @@ -4957,7 +4957,7 @@ vehicle-detection-0202: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: MobileNetV2-SSD with two heads and clustered priors for 512x512 resolution. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-detection-0202.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-detection-0202.json name: vehicle-detection-0202 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-detection-0202 @@ -4983,7 +4983,7 @@ vehicle-detection-adas-0002: description: Vehicle detector based on SSD + MobileNet with reduced number of channels and depthwise head. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-detection-adas-0002.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-detection-adas-0002.json name: vehicle-detection-adas-0002 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-detection-adas-0002 @@ -5007,7 +5007,7 @@ vehicle-license-plate-detection-barrier-0106: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/object_detection_demo/python description: Multiclass (vehicle - license plates) detector based on MobileNetV2+SSD license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/vehicle-license-plate-detection-barrier-0106.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/vehicle-license-plate-detection-barrier-0106.json name: vehicle-license-plate-detection-barrier-0106 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/vehicle-license-plate-detection-barrier-0106 @@ -5032,7 +5032,7 @@ vehicle-license-plate-detection-barrier-0123: description: This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the "Barrier" use case. license: https://raw.githubusercontent.com/opencv/training_toolbox_tensorflow/develop/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/vehicle-license-plate-detection-barrier-0123.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/vehicle-license-plate-detection-barrier-0123.json name: vehicle-license-plate-detection-barrier-0123 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/vehicle-license-plate-detection-barrier-0123 @@ -5126,7 +5126,7 @@ weld-porosity-detection-0001: - https://github.com/openvinotoolkit/open_model_zoo/tree/master//demos/action_recognition_demo/python description: ResNet18 based network for porosity weld recognition. license: https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/LICENSE - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/intel/weld-porosity-detection-0001.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/intel/weld-porosity-detection-0001.json name: weld-porosity-detection-0001 openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/intel/weld-porosity-detection-0001 @@ -5397,10 +5397,10 @@ yolo-v3-tf: from this repository and converted to TensorFlow* framework. This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE mAP: 62.27% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v3-tf.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v3-tf.json name: yolo-v3-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/yolo-v3-tf @@ -5448,10 +5448,10 @@ yolo-v3-tiny-tf: Keras* from this repository and converted to TensorFlow* framework. This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes. - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE mAP: 35.9% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v3-tiny-tf.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v3-tiny-tf.json name: yolo-v3-tiny-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/yolo-v3-tiny-tf @@ -5474,10 +5474,10 @@ yolo-v4-tf: For details see repository . This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE mAP: 71.23% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v4-tf.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v4-tf.json name: yolo-v4-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/yolo-v4-tf @@ -5500,10 +5500,10 @@ yolo-v4-tiny-tf: For details see repository . This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes.' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt license: https://raw.githubusercontent.com/david8862/keras-YOLOv3-model-set/master/LICENSE mAP: 40.37% - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/public/yolo-v4-tiny-tf.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/public/yolo-v4-tiny-tf.json name: yolo-v4-tiny-tf openvino_devices: CPU, GPU readme: https://github.com/openvinotoolkit/open_model_zoo/tree/master//models/public/yolo-v4-tiny-tf @@ -5566,13 +5566,13 @@ mobilenetv2-7: format: onnx task_type: classification readme: https://github.com/onnx/models/tree/main/validated/vision/classification/mobilenet - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/onnx/mobilenetv2-7.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/onnx/mobilenetv2-7.json emotion-ferplus-8: source: public format: onnx task_type: classification readme: https://github.com/onnx/models/tree/main/validated/vision/body_analysis/emotion_ferplus - model-proc: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gstreamer/model_proc/onnx/emotion-ferplus-8.json + model-proc: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/model_proc/onnx/emotion-ferplus-8.json # TorchVision models torchvision.models.detection.ssdlite320_mobilenet_v3_large: @@ -5581,7 +5581,7 @@ torchvision.models.detection.ssdlite320_mobilenet_v3_large: task_type: detection readme: https://pytorch.org/vision/main/models/generated/torchvision.models.detection.ssdlite320_mobilenet_v3_large.html GFLOPs: '0.583' - labels-file: https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/labels/coco_80cl.txt + labels-file: https://github.com/open-edge-platform/dlstreamer/tree/master/samples/labels/coco_80cl.txt dlstreamer_support: gva, gst_openvino, gst_pytorch, ffmpeg_openvino dlstreamer_gva: gvadetect model=ssdlite320_mobilenet_v3_large.xml device=CPU dlstreamer_gst_openvino: object_detect model=ssdlite320_mobilenet_v3_large.xml device=CPU diff --git a/docs/scripts/generate_models_table.py b/docs/scripts/generate_models_table.py index 3051b14c..dc4947cf 100755 --- a/docs/scripts/generate_models_table.py +++ b/docs/scripts/generate_models_table.py @@ -12,7 +12,7 @@ from argparse import ArgumentParser from jsonschema import validate -DLSTREAMER_URL='https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/' +DLSTREAMER_URL='https://github.com/open-edge-platform/dlstreamer/tree/master/' PIPELINE_ZOO_URL='https://github.com/dlstreamer/pipeline-zoo-models/tree/main/' dldt_str = 'dl' + 'dt' diff --git a/docs/scripts/models_yaml_from_zoo.py b/docs/scripts/models_yaml_from_zoo.py index 15b2ae48..f3658a34 100644 --- a/docs/scripts/models_yaml_from_zoo.py +++ b/docs/scripts/models_yaml_from_zoo.py @@ -13,7 +13,7 @@ from jsonschema import validate OV_MODEL_ZOO_URL = 'https://github.com/openvinotoolkit/open_model_zoo/tree/master/' -DLSTREAMER_URL='https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/' +DLSTREAMER_URL='https://github.com/open-edge-platform/dlstreamer/tree/master/' PIPELINE_ZOO_URL='https://github.com/dlstreamer/pipeline-zoo-models/tree/main/' dldt_str = 'dl' + 'dt' diff --git a/docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md b/docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md index fdf7e707..9774343a 100644 --- a/docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md +++ b/docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md @@ -95,9 +95,8 @@ pip install meson==1.4.1 ninja==1.11.1.1 ```bash cd ~ -git clone https://github.com/open-edge-platform/edge-ai-libraries.git -cd edge-ai-libraries -git submodule update --init libraries/dl-streamer/thirdparty/spdlog +git clone --recursive https://github.com/open-edge-platform/dlstreamer.git +cd dlstreamer ``` ## Step 5: Install OpenVINO™ Toolkit @@ -107,7 +106,7 @@ git submodule update --init libraries/dl-streamer/thirdparty/spdlog ```bash - cd ~/edge-ai-libraries/libraries/dl-streamer + cd ~/dlstreamer sudo ./scripts/install_dependencies/install_openvino.sh ``` @@ -203,12 +202,12 @@ Set up the required environment variables: ```bash export LIBVA_DRIVER_NAME=iHD - export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" - export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:/opt/intel/dlstreamer/rdkafka/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" + export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" + export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:/opt/intel/dlstreamer/rdkafka/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" export LIBVA_DRIVERS_PATH="/usr/lib/x86_64-linux-gnu/dri" export GST_VA_ALL_DRIVERS="1" - export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" - export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib/pkgconfig:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" + export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" + export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX export GI_TYPELIB_PATH="/opt/intel/dlstreamer/gstreamer/lib/girepository-1.0:/usr/lib/x86_64-linux-gnu/girepository-1.0gi" export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH" @@ -220,12 +219,12 @@ Set up the required environment variables: ```bash export LIBVA_DRIVER_NAME=iHD - export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" - export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" + export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" + export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" export LIBVA_DRIVERS_PATH="/usr/lib64/dri-nonfree" export GST_VA_ALL_DRIVERS="1" - export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" - export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib/pkgconfig:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" + export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" + export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH" ``` @@ -248,12 +247,12 @@ Set up the required environment variables: ```bash export LIBVA_DRIVER_NAME=iHD - export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" - export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/lib:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" + export GST_PLUGIN_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib/gstreamer-1.0:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/gstreamer-1.0:$GST_PLUGIN_PATH" + export LD_LIBRARY_PATH="/opt/intel/dlstreamer/Release/lib:/opt/intel/dlstreamer/gstreamer/lib:/opt/intel/dlstreamer/opencv/lib:$HOME/dlstreamer/build/intel64/Release/lib:$HOME/dlstreamer/build/deps/gstreamer-bin/lib:$HOME/dlstreamer/build/deps/opencv-bin/lib:$HOME/dlstreamer/build/deps/rdkafka-bin/lib:$LD_LIBRARY_PATH" export LIBVA_DRIVERS_PATH="/usr/lib/dri" export GST_VA_ALL_DRIVERS="1" - export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/bin:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" - export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/edge-ai-libraries/libraries/dl-streamer/build/intel64/Release/lib/pkgconfig:$HOME/edge-ai-libraries/libraries/dl-streamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" + export PATH="/opt/intel/dlstreamer/Release/bin:/opt/intel/dlstreamer/gstreamer/bin:/opt/intel/dlstreamer/opencv/bin:$HOME/dlstreamer/build/intel64/Release/bin:$HOME/dlstreamer/build/deps/gstreamer-bin/bin:$HOME/dlstreamer/build/deps/opencv-bin/bin:$HOME/.local/bin:$HOME/python3venv/bin:$PATH" + export PKG_CONFIG_PATH="/opt/intel/dlstreamer/Release/lib/pkgconfig:/opt/intel/dlstreamer/gstreamer/lib/pkgconfig::$HOME/dlstreamer/build/intel64/Release/lib/pkgconfig:$HOME/dlstreamer/build/deps/gstreamer-bin/lib/pkgconfig:$PKG_CONFIG_PATH" export GST_PLUGIN_FEATURE_RANK=${GST_PLUGIN_FEATURE_RANK},ximagesink:MAX export PYTHONPATH="/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:/opt/intel/dlstreamer/python:/opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:$PYTHONPATH" ``` @@ -275,6 +274,6 @@ sudo apt-get install -y -q --no-install-recommends gcc cmake python3-full python libopencv-calib3d-dev libopencv-core-dev libopencv-dnn-dev libgirepository1.0-dev source ~/python3venv/bin/activate -cd ~/edge-ai-libraries/libraries/dl-streamer +cd ~/dlstreamer python3 -m pip install -r requirements.txt ``` diff --git a/docs/source/dev_guide/advanced_install/advanced_install_on_windows.md b/docs/source/dev_guide/advanced_install/advanced_install_on_windows.md index 1f370576..77ee5dda 100644 --- a/docs/source/dev_guide/advanced_install/advanced_install_on_windows.md +++ b/docs/source/dev_guide/advanced_install/advanced_install_on_windows.md @@ -8,9 +8,8 @@ from the source code provided in ## Step 1: Clone Deep Learning Streamer repository ```bash -git clone https://github.com/open-edge-platform/edge-ai-libraries.git -cd edge-ai-libraries -git submodule update --init libraries/dl-streamer/thirdparty/spdlog +git clone --recursive https://github.com/open-edge-platform/dlstreamer.git +cd dlstreamer ``` ## Step 2: Run installation script diff --git a/src/monolithic/gst/elements/gvametapublish/Readme.md b/src/monolithic/gst/elements/gvametapublish/Readme.md index 03e94ce2..62e34cb7 100644 --- a/src/monolithic/gst/elements/gvametapublish/Readme.md +++ b/src/monolithic/gst/elements/gvametapublish/Readme.md @@ -16,14 +16,14 @@ A GStreamer element to publish JSON data to a designated file, or a chosen messa If you are building from source according to the provided instructions, all dependencies should already be satisfied. You can find the source build instructions [here](../../../../../docs/source/dev_guide/advanced_install/advanced_install_guide_compilation.md). - If you are not following the source instructions, you may need to run the [install_metapublish_dependencies.sh](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/scripts/install_metapublish_dependencies.sh) script and rebuild DL Streamer with the following parameters enabled: + If you are not following the source instructions, you may need to run the [install_metapublish_dependencies.sh](https://github.com/open-edge-platform/dlstreamer/tree/master/scripts/install_metapublish_dependencies.sh) script and rebuild DL Streamer with the following parameters enabled: ```bash -DENABLE_PAHO_INSTALLATION=ON \ -DENABLE_RDKAFKA_INSTALLATION=ON \ ``` -2. Run [metapublish](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/samples/gst_launch/metapublish/metapublish.sh) sample to test +2. Run [metapublish](https://github.com/open-edge-platform/dlstreamer/tree/master/samples/gstreamer/gst_launch/metapublish/metapublish.sh) sample to test 3. Create your own pipeline and add gvametapublish element with the following parameters: diff --git a/src/monolithic/gst/elements/gvametapublish/gvametapublish.cpp b/src/monolithic/gst/elements/gvametapublish/gvametapublish.cpp index 21a69a96..727f5707 100644 --- a/src/monolithic/gst/elements/gvametapublish/gvametapublish.cpp +++ b/src/monolithic/gst/elements/gvametapublish/gvametapublish.cpp @@ -188,7 +188,7 @@ class GvaMetaPublishPrivate { _base, "Failed to create element for method: %s\n\n" "Please refer to 'install_metapublish_dependencies.sh' script to install required dependencies:\n" - "https://github.com/open-edge-platform/edge-ai-libraries/tree/main/libraries/dl-streamer/scripts/" + "https://github.com/open-edge-platform/dlstreamer/tree/master/scripts/" "install_metapublish_dependencies.sh\n" "After installation clear GStreamer registry cache to refresh plugins.\n", method_type_to_string(_method));