Remove the YOLOv5 serverless function (#6618)

Due to YOLOv5's recent license change to the AGPL (<ultralytics/yolov5#11359>) it is no longer acceptable to use it in the project as a matter of policy.
cvat-ai · Aug 9, 2023 · 70fc428 · 70fc428
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -32,7 +32,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Removed
 
-- TDB
+- The YOLOv5 serverless function
+  (<https://github.com/opencv/cvat/pull/6618>)
 
 ### Fixed
 

diff --git a/README.md b/README.md
@@ -193,7 +193,6 @@ up to 10x. Here is a list of the algorithms we support, and the platforms they c
 | [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-0277/nuclio)    | reid       | OpenVINO   | ✔️  |     |
 | [Semantic segmentation for ADAS](/serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio) | detector   | OpenVINO   | ✔️  |     |
 | [Text detection v4](/serverless/openvino/omz/intel/text-detection-0004/nuclio)                          | detector   | OpenVINO   | ✔️  |     |
-| [YOLO v5](/serverless/pytorch/ultralytics/yolov5/nuclio)                                                | detector   | PyTorch    | ✔️  |     |
 | [SiamMask](/serverless/pytorch/foolwood/siammask/nuclio)                                                | tracker    | PyTorch    | ✔️  | ✔️  |
 | [TransT](/serverless/pytorch/dschoerk/transt/nuclio)                                                    | tracker    | PyTorch    | ✔️  | ✔️  |
 | [f-BRS](/serverless/pytorch/saic-vul/fbrs/nuclio)                                                       | interactor | PyTorch    | ✔️  |     |

diff --git a/serverless/pytorch/ultralytics/yolov5/nuclio/function-gpu.yaml b/serverless/pytorch/ultralytics/yolov5/nuclio/function-gpu.yaml
diff --git a/serverless/pytorch/ultralytics/yolov5/nuclio/function.yaml b/serverless/pytorch/ultralytics/yolov5/nuclio/function.yaml
diff --git a/serverless/pytorch/ultralytics/yolov5/nuclio/main.py b/serverless/pytorch/ultralytics/yolov5/nuclio/main.py
diff --git a/site/content/en/docs/manual/advanced/ai-tools.md b/site/content/en/docs/manual/advanced/ai-tools.md
@@ -204,7 +204,6 @@ see [Automatic annotation](/docs/manual/advanced/automatic-annotation/).
 | Mask RCNN                      | The model generates polygons for each instance of an object in the image. <br><br> For more information, see: <li>[GitHub: Mask RCNN](https://github.com/matterport/Mask_RCNN) <li>[Paper: Mask RCNN](https://arxiv.org/pdf/1703.06870.pdf)                                                                                                                                                                                    |
 | Faster RCNN                    | The model generates bounding boxes for each instance of an object in the image. <br>In this model, RPN and Fast R-CNN are combined into a single network. <br><br> For more information, see: <li>[GitHub: Faster RCNN](https://github.com/ShaoqingRen/faster_rcnn) <li>[Paper: Faster RCNN](https://arxiv.org/pdf/1506.01497.pdf)                                                                                             |
 | YOLO v3                        | YOLO v3 is a family of object detection architectures and models pre-trained on the COCO dataset. <br><br> For more information, see: <li>[GitHub: YOLO v3](https://github.com/ultralytics/yolov3) <li>[Site: YOLO v3](https://docs.ultralytics.com/#yolov3) <li>[Paper: YOLO v3](https://arxiv.org/pdf/1804.02767v1.pdf)                                                                                                      |
-| YOLO v5                        | YOLO v5 is a family of object detection architectures and models based on the Pytorch framework. <br><br> For more information, see: <li>[GitHub: YOLO v5](https://github.com/ultralytics/yolov5) <li>[Site: YOLO v5](https://docs.ultralytics.com/#yolov5)                                                                                                                                                                    |
 | Semantic segmentation for ADAS | This is a segmentation network to classify each pixel into 20 classes. <br><br> For more information, see: <li>[Site: ADAS](https://docs.openvino.ai/2019_R1/_semantic_segmentation_adas_0001_description_semantic_segmentation_adas_0001.html)                                                                                                                                                                                |
 | Mask RCNN with Tensorflow      | Mask RCNN version with Tensorflow. The model generates polygons for each instance of an object in the image. <br><br> For more information, see: <li>[GitHub: Mask RCNN](https://github.com/matterport/Mask_RCNN) <li>[Paper: Mask RCNN](https://arxiv.org/pdf/1703.06870.pdf)                                                                                                                                                 |
 | Faster RCNN with Tensorflow    | Faster RCNN version with Tensorflow. The model generates bounding boxes for each instance of an object in the image. <br>In this model, RPN and Fast R-CNN are combined into a single network. <br><br> For more information, see: <li>[Site: Faster RCNN with Tensorflow](https://docs.openvino.ai/2021.4/omz_models_model_faster_rcnn_inception_v2_coco.html) <li>[Paper: Faster RCNN](https://arxiv.org/pdf/1506.01497.pdf) |

diff --git a/site/content/en/docs/manual/advanced/automatic-annotation.md b/site/content/en/docs/manual/advanced/automatic-annotation.md
@@ -93,7 +93,6 @@ List of pre-installed models:
 | RetinaNet R101            | RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross entropy loss to focus learning on hard negative examples. RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. <br><br>For more information, see: <li>[Site: RetinaNET](https://paperswithcode.com/lib/detectron2/retinanet)                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
 | Text detection            | Text detector based on PixelLink architecture with MobileNetV2, depth_multiplier=1.4 as a backbone for indoor/outdoor scenes. <br><br> For more information, see: <li>[Site: OpenVINO Text detection 004](https://docs.openvino.ai/2022.3/omz_models_model_text_detection_0004.html)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | YOLO v3                   | YOLO v3 is a family of object detection architectures and models pre-trained on the COCO dataset. <br><br> For more information, see: <li>[Site: YOLO v3](https://docs.openvino.ai/2022.3/omz_models_model_yolo_v3_tf.html)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| YOLO v5                   | YOLO v5 is a family of object detection architectures and models based on the Pytorch framework. <br><br> For more information, see: <li>[GitHub: YOLO v5](https://github.com/ultralytics/yolov5) <li>[Site: YOLO v5](https://docs.ultralytics.com/#yolov5)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
 | YOLO v7                   | YOLOv7 is an advanced object detection model that outperforms other detectors in terms of both speed and accuracy. It can process frames at a rate ranging from 5 to 160 frames per second (FPS) and achieves the highest accuracy with 56.8% average precision (AP) among real-time object detectors running at 30 FPS or higher on the V100 graphics processing unit (GPU). <br><br> For more information, see: <li>[GitHub: YOLO v7](https://github.com/WongKinYiu/yolov7) <li>[Paper: YOLO v7](https://arxiv.org/pdf/2207.02696.pdf)                                                                                                                                                                                                                                                                                                                                                                                                    |
 
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