From 8fb569e11a205f78dfa4f3b528da815d08a17c24 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Fri, 20 Dec 2024 17:05:24 +0530 Subject: [PATCH 01/10] Updated README.md Removed Masked RCNN --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 2a252b4eaed8..42e92691f861 100644 --- a/README.md +++ b/README.md @@ -195,7 +195,6 @@ up to 10x. Here is a list of the algorithms we support, and the platforms they c | [Segment Anything](/serverless/pytorch/facebookresearch/sam/nuclio/) | interactor | PyTorch | ✔️ | ✔️ | | [Deep Extreme Cut](/serverless/openvino/dextr/nuclio) | interactor | OpenVINO | ✔️ | | | [Faster RCNN](/serverless/openvino/omz/public/faster_rcnn_inception_resnet_v2_atrous_coco/nuclio) | detector | OpenVINO | ✔️ | | -| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio) | detector | OpenVINO | ✔️ | | | [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio) | detector | OpenVINO | ✔️ | | | [YOLO v7](/serverless/onnx/WongKinYiu/yolov7/nuclio) | detector | ONNX | ✔️ | ✔️ | | [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-0277/nuclio) | reid | OpenVINO | ✔️ | | @@ -207,7 +206,6 @@ up to 10x. Here is a list of the algorithms we support, and the platforms they c | [HRNet](/serverless/pytorch/saic-vul/hrnet/nuclio) | interactor | PyTorch | | ✔️ | | [Inside-Outside Guidance](/serverless/pytorch/shiyinzhang/iog/nuclio) | interactor | PyTorch | ✔️ | | | [Faster RCNN](/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio) | detector | TensorFlow | ✔️ | ✔️ | -| [Mask RCNN](/serverless/tensorflow/matterport/mask_rcnn/nuclio) | detector | TensorFlow | ✔️ | ✔️ | | [RetinaNet](serverless/pytorch/facebookresearch/detectron2/retinanet_r101/nuclio) | detector | PyTorch | ✔️ | ✔️ | | [Face Detection](/serverless/openvino/omz/intel/face-detection-0205/nuclio) | detector | OpenVINO | ✔️ | | From 822a71d0ea245ecfc489918bfc1d0e06f667c1c1 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Fri, 20 Dec 2024 17:12:26 +0530 Subject: [PATCH 02/10] Update ai-tools.md --- site/content/en/docs/manual/advanced/ai-tools.md | 1 - 1 file changed, 1 deletion(-) diff --git a/site/content/en/docs/manual/advanced/ai-tools.md b/site/content/en/docs/manual/advanced/ai-tools.md index 14b628cc882d..d8c235242df4 100644 --- a/site/content/en/docs/manual/advanced/ai-tools.md +++ b/site/content/en/docs/manual/advanced/ai-tools.md @@ -205,7 +205,6 @@ see {{< ilink "/docs/manual/advanced/automatic-annotation" "Automatic annotation | Faster RCNN | The model generates bounding boxes for each instance of an object in the image.
In this model, RPN and Fast R-CNN are combined into a single network.

For more information, see:
  • [GitHub: Faster RCNN](https://github.com/ShaoqingRen/faster_rcnn)
  • [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.

    For more information, see:
  • [GitHub: YOLO v3](https://github.com/ultralytics/yolov3)
  • [Site: YOLO v3](https://docs.ultralytics.com/#yolov3)
  • [Paper: YOLO v3](https://arxiv.org/pdf/1804.02767v1.pdf) | | Semantic segmentation for ADAS | This is a segmentation network to classify each pixel into 20 classes.

    For more information, see:
  • [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.

    For more information, see:
  • [GitHub: Mask RCNN](https://github.com/matterport/Mask_RCNN)
  • [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.
    In this model, RPN and Fast R-CNN are combined into a single network.

    For more information, see:
  • [Site: Faster RCNN with Tensorflow](https://docs.openvino.ai/2021.4/omz_models_model_faster_rcnn_inception_v2_coco.html)
  • [Paper: Faster RCNN](https://arxiv.org/pdf/1506.01497.pdf) | | RetinaNet | Pytorch implementation of RetinaNet object detection.


    For more information, see:
  • [Specification: RetinaNet](https://paperswithcode.com/lib/detectron2/retinanet)
  • [Paper: RetinaNet](https://arxiv.org/pdf/1708.02002.pdf)
  • [Documentation: RetinaNet](https://detectron2.readthedocs.io/en/latest/tutorials/training.html) | | Face Detection | Face detector based on MobileNetV2 as a backbone for indoor and outdoor scenes shot by a front-facing camera.


    For more information, see:
  • [Site: Face Detection 0205](https://docs.openvino.ai/latest/omz_models_model_face_detection_0205.html) | From b565d458867076f22f7ea2e150f600de7ff25a10 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Fri, 20 Dec 2024 17:13:28 +0530 Subject: [PATCH 03/10] Update ai-tools.md --- site/content/en/docs/manual/advanced/ai-tools.md | 1 - 1 file changed, 1 deletion(-) diff --git a/site/content/en/docs/manual/advanced/ai-tools.md b/site/content/en/docs/manual/advanced/ai-tools.md index d8c235242df4..ac24cf3c0a16 100644 --- a/site/content/en/docs/manual/advanced/ai-tools.md +++ b/site/content/en/docs/manual/advanced/ai-tools.md @@ -201,7 +201,6 @@ see {{< ilink "/docs/manual/advanced/automatic-annotation" "Automatic annotation | Model | Description | | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| Mask RCNN | The model generates polygons for each instance of an object in the image.

    For more information, see:
  • [GitHub: Mask RCNN](https://github.com/matterport/Mask_RCNN)
  • [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.
    In this model, RPN and Fast R-CNN are combined into a single network.

    For more information, see:
  • [GitHub: Faster RCNN](https://github.com/ShaoqingRen/faster_rcnn)
  • [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.

    For more information, see:
  • [GitHub: YOLO v3](https://github.com/ultralytics/yolov3)
  • [Site: YOLO v3](https://docs.ultralytics.com/#yolov3)
  • [Paper: YOLO v3](https://arxiv.org/pdf/1804.02767v1.pdf) | | Semantic segmentation for ADAS | This is a segmentation network to classify each pixel into 20 classes.

    For more information, see:
  • [Site: ADAS](https://docs.openvino.ai/2019_R1/_semantic_segmentation_adas_0001_description_semantic_segmentation_adas_0001.html) | From 1a4075e40ba254f0e3e5412f87d7eaaf5946f88c Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Fri, 20 Dec 2024 17:32:47 +0530 Subject: [PATCH 04/10] Update overview.md Removed masked rcnn from overview --- site/content/en/docs/getting_started/overview.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/site/content/en/docs/getting_started/overview.md b/site/content/en/docs/getting_started/overview.md index 45ce1f08740c..4b81ba8fed9c 100644 --- a/site/content/en/docs/getting_started/overview.md +++ b/site/content/en/docs/getting_started/overview.md @@ -108,7 +108,6 @@ Below is a detailed table of the supported algorithms and the platforms they ope | [Segment Anything](https://github.com/cvat-ai/cvat/tree/develop/serverless/pytorch/facebookresearch/sam/nuclio) | Interactor | PyTorch | ✔️ | ✔️ | | [Deep Extreme Cut](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/dextr/nuclio) | Interactor | OpenVINO | ✔️ | | | [Faster RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/faster_rcnn_inception_resnet_v2_atrous_coco/nuclio) | Detector | OpenVINO | ✔️ | | -| [Mask RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio) | Detector | OpenVINO | ✔️ | | | [YOLO v3](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/yolo-v3-tf/nuclio) | Detector | OpenVINO | ✔️ | | | [YOLO v7](https://github.com/cvat-ai/cvat/tree/develop/serverless/onnx/WongKinYiu/yolov7/nuclio) | Detector | ONNX | ✔️ | ✔️ | | [Object Reidentification](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/intel/person-reidentification-retail-0277/nuclio) | ReID | OpenVINO | ✔️ | | @@ -120,7 +119,6 @@ Below is a detailed table of the supported algorithms and the platforms they ope | [HRNet](https://github.com/cvat-ai/cvat/tree/develop/serverless/pytorch/saic-vul/hrnet/nuclio) | Interactor | PyTorch | | ✔️ | | [Inside-Outside Guidance](https://github.com/cvat-ai/cvat/tree/develop/serverless/pytorch/shiyinzhang/iog/nuclio) | Interactor | PyTorch | ✔️ | | | [Faster RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/tensorflow/faster_rcnn_inception_v2_coco/nuclio) | Detector | TensorFlow | ✔️ | ✔️ | -| [Mask RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/tensorflow/matterport/mask_rcnn/nuclio) | Detector | TensorFlow | ✔️ | ✔️ | | [RetinaNet](https://github.com/cvat-ai/cvat/tree/develop/serverless/pytorch/facebookresearch/detectron2/retinanet_r101/nuclio) | Detector | PyTorch | ✔️ | ✔️ | | [Face Detection](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/intel/face-detection-0205/nuclio) | Detector | OpenVINO | ✔️ | | From 72a4d55f138e25f585efe5fea78345d5e00e68be Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Fri, 20 Dec 2024 17:51:47 +0530 Subject: [PATCH 05/10] Update serverless-tutorial.md removed mentions of Mask RCNN --- .../manual/advanced/serverless-tutorial.md | 59 +------------------ 1 file changed, 1 insertion(+), 58 deletions(-) diff --git a/site/content/en/docs/manual/advanced/serverless-tutorial.md b/site/content/en/docs/manual/advanced/serverless-tutorial.md index 5211886208e8..de0f09b1db19 100644 --- a/site/content/en/docs/manual/advanced/serverless-tutorial.md +++ b/site/content/en/docs/manual/advanced/serverless-tutorial.md @@ -24,8 +24,7 @@ that can _perfectly_ annotate 50% of your data equates to reducing manual annota Since we know DL models can help us to annotate faster, how then do we use them? In CVAT all such DL models are implemented as serverless functions using the [Nuclio][nuclio-homepage] serverless platform. There are multiple implemented functions that can be -found in the [serverless][cvat-builtin-serverless] directory such as _Mask RCNN, -Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc. +found in the [serverless][cvat-builtin-serverless] directory such as _Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc. Follow [the installation guide][cvat-auto-annotation-guide] to build and deploy these serverless functions. See [the user guide][cvat-ai-tools-user-guide] to understand how to use these functions in the UI to automatically annotate data. @@ -161,7 +160,6 @@ Finally you will get bounding boxes. ![SiamMask results](/images/siammask_results.gif) `SiamMask` model is more optimized to work on Nvidia GPUs. -For more information about deploying the model for the GPU, [read on](#objects-segmentation-using-mask-rcnn). ### Object detection using YOLO-v3 @@ -215,61 +213,6 @@ CVAT will run the serverless function on every frame of the task and submit results directly into database. For more details please read [the guide][cvat-auto-annotation-user-guide]. -### Objects segmentation using Mask-RCNN - -If you have a detector, which returns polygons, you can segment objects. One -of such detectors is `Mask-RCNN`. There are several implementations of the -detector available out of the box: - -- `serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco` is - optimized using [Intel OpenVINO framework][intel-openvino-url] and works well - if it is run on an Intel CPU. -- `serverless/tensorflow/matterport/mask_rcnn/` is optimized for GPU. - -The deployment process for a serverless function optimized for GPU is similar. -Just need to run `serverless/deploy_gpu.sh` script. It runs mostly the same -commands but utilize `function-gpu.yaml` configuration file instead of -`function.yaml` internally. See next sections if you want to understand the -difference. - -_Note: Please do not run several GPU functions at the same time. In many cases it -will not work out of the box. For now you should manually schedule different -functions on different GPUs and it requires source code modification. Nuclio -autoscaler does not support the local platform (docker)._ - -
    - - -```bash -serverless/deploy_gpu.sh serverless/tensorflow/matterport/mask_rcnn -``` - - - -``` -Deploying serverless/tensorflow/matterport/mask_rcnn function... -21.07.12 16:48:48.995 nuctl (I) Deploying function {"name": ""} -21.07.12 16:48:48.995 nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""} -21.07.12 16:48:49.356 nuctl (I) Cleaning up before deployment {"functionName": "tf-matterport-mask-rcnn"} -21.07.12 16:48:49.470 nuctl (I) Function already exists, deleting function containers {"functionName": "tf-matterport-mask-rcnn"} -21.07.12 16:48:50.247 nuctl (I) Staging files and preparing base images -21.07.12 16:48:50.248 nuctl (I) Building processor image {"imageName": "cvat/tf.matterport.mask_rcnn:latest"} -21.07.12 16:48:50.249 nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"} -21.07.12 16:48:53.674 nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"} -21.07.12 16:48:57.424 nuctl.platform (I) Building docker image {"image": "cvat/tf.matterport.mask_rcnn:latest"} -21.07.12 16:48:57.763 nuctl.platform (I) Pushing docker image into registry {"image": "cvat/tf.matterport.mask_rcnn:latest", "registry": ""} -21.07.12 16:48:57.764 nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/tf.matterport.mask_rcnn:latest"} -21.07.12 16:48:57.764 nuctl (I) Build complete {"result": {"Image":"cvat/tf.matterport.mask_rcnn:latest","UpdatedFunctionConfig":{"metadata":{"name":"tf-matterport-mask-rcnn","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"tensorflow","name":"Mask RCNN via Tensorflow","spec":"[\n { \"id\": 0, \"name\": \"BG\" },\n { \"id\": 1, \"name\": \"person\" },\n { \"id\": 2, \"name\": \"bicycle\" },\n { \"id\": 3, \"name\": \"car\" },\n { \"id\": 4, \"name\": \"motorcycle\" },\n { \"id\": 5, \"name\": \"airplane\" },\n { \"id\": 6, \"name\": \"bus\" },\n { \"id\": 7, \"name\": \"train\" },\n { \"id\": 8, \"name\": \"truck\" },\n { \"id\": 9, \"name\": \"boat\" },\n { \"id\": 10, \"name\": \"traffic_light\" },\n { \"id\": 11, \"name\": \"fire_hydrant\" },\n { \"id\": 12, \"name\": \"stop_sign\" },\n { \"id\": 13, \"name\": \"parking_meter\" },\n { \"id\": 14, \"name\": \"bench\" },\n { \"id\": 15, \"name\": \"bird\" },\n { \"id\": 16, \"name\": \"cat\" },\n { \"id\": 17, \"name\": \"dog\" },\n { \"id\": 18, \"name\": \"horse\" },\n { \"id\": 19, \"name\": \"sheep\" },\n { \"id\": 20, \"name\": \"cow\" },\n { \"id\": 21, \"name\": \"elephant\" },\n { \"id\": 22, \"name\": \"bear\" },\n { \"id\": 23, \"name\": \"zebra\" },\n { \"id\": 24, \"name\": \"giraffe\" },\n { \"id\": 25, \"name\": \"backpack\" },\n { \"id\": 26, \"name\": \"umbrella\" },\n { \"id\": 27, \"name\": \"handbag\" },\n { \"id\": 28, \"name\": \"tie\" },\n { \"id\": 29, \"name\": \"suitcase\" },\n { \"id\": 30, \"name\": \"frisbee\" },\n { \"id\": 31, \"name\": \"skis\" },\n { \"id\": 32, \"name\": \"snowboard\" },\n { \"id\": 33, \"name\": \"sports_ball\" },\n { \"id\": 34, \"name\": \"kite\" },\n { \"id\": 35, \"name\": \"baseball_bat\" },\n { \"id\": 36, \"name\": \"baseball_glove\" },\n { \"id\": 37, \"name\": \"skateboard\" },\n { \"id\": 38, \"name\": \"surfboard\" },\n { \"id\": 39, \"name\": \"tennis_racket\" },\n { \"id\": 40, \"name\": \"bottle\" },\n { \"id\": 41, \"name\": \"wine_glass\" },\n { \"id\": 42, \"name\": \"cup\" },\n { \"id\": 43, \"name\": \"fork\" },\n { \"id\": 44, \"name\": \"knife\" },\n { \"id\": 45, \"name\": \"spoon\" },\n { \"id\": 46, \"name\": \"bowl\" },\n { \"id\": 47, \"name\": \"banana\" },\n { \"id\": 48, \"name\": \"apple\" },\n { \"id\": 49, \"name\": \"sandwich\" },\n { \"id\": 50, \"name\": \"orange\" },\n { \"id\": 51, \"name\": \"broccoli\" },\n { \"id\": 52, \"name\": \"carrot\" },\n { \"id\": 53, \"name\": \"hot_dog\" },\n { \"id\": 54, \"name\": \"pizza\" },\n { \"id\": 55, \"name\": \"donut\" },\n { \"id\": 56, \"name\": \"cake\" },\n { \"id\": 57, \"name\": \"chair\" },\n { \"id\": 58, \"name\": \"couch\" },\n { \"id\": 59, \"name\": \"potted_plant\" },\n { \"id\": 60, \"name\": \"bed\" },\n { \"id\": 61, \"name\": \"dining_table\" },\n { \"id\": 62, \"name\": \"toilet\" },\n { \"id\": 63, \"name\": \"tv\" },\n { \"id\": 64, \"name\": \"laptop\" },\n { \"id\": 65, \"name\": \"mouse\" },\n { \"id\": 66, \"name\": \"remote\" },\n { \"id\": 67, \"name\": \"keyboard\" },\n { \"id\": 68, \"name\": \"cell_phone\" },\n { \"id\": 69, \"name\": \"microwave\" },\n { \"id\": 70, \"name\": \"oven\" },\n { \"id\": 71, \"name\": \"toaster\" },\n { \"id\": 72, \"name\": \"sink\" },\n { \"id\": 73, \"name\": \"refrigerator\" },\n { \"id\": 74, \"name\": \"book\" },\n { \"id\": 75, \"name\": \"clock\" },\n { \"id\": 76, \"name\": \"vase\" },\n { \"id\": 77, \"name\": \"scissors\" },\n { \"id\": 78, \"name\": \"teddy_bear\" },\n { \"id\": 79, \"name\": \"hair_drier\" },\n { \"id\": 80, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"Mask RCNN optimized for GPU","handler":"main:handler","runtime":"python:3.6","env":[{"name":"MASK_RCNN_DIR","value":"/opt/nuclio/Mask_RCNN"}],"resources":{"limits":{"nvidia.com/gpu":"1"}},"image":"cvat/tf.matterport.mask_rcnn:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":1,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"functionConfigPath":"serverless/tensorflow/matterport/mask_rcnn/nuclio/function-gpu.yaml","image":"cvat/tf.matterport.mask_rcnn","baseImage":"tensorflow/tensorflow:1.15.5-gpu-py3","directives":{"postCopy":[{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"apt update \u0026\u0026 apt install --no-install-recommends -y git curl"},{"kind":"RUN","value":"git clone --depth 1 https://github.com/matterport/Mask_RCNN.git"},{"kind":"RUN","value":"curl -L https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 -o Mask_RCNN/mask_rcnn_coco.h5"},{"kind":"RUN","value":"pip3 install numpy cython pyyaml keras==2.1.0 scikit-image Pillow"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}} -21.07.12 16:48:59.071 nuctl.platform (I) Waiting for function to be ready {"timeout": 60} -21.07.12 16:49:00.437 nuctl (I) Function deploy complete {"functionName": "tf-matterport-mask-rcnn", "httpPort": 49155} -``` - -
    - -Now you should be able to annotate objects using segmentation masks. - -![Mask RCNN results](/images/mask_rcnn_results.jpg) - ## Adding your own DL models ### Choose a DL model From 03b4d0da4e7c78cdf5f5bc3e3f4277e779582763 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Mon, 23 Dec 2024 21:15:38 +0530 Subject: [PATCH 06/10] Update README.md Added mask RCNN as CPU version is still supported --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 42e92691f861..b45ae1100d90 100644 --- a/README.md +++ b/README.md @@ -195,6 +195,7 @@ up to 10x. Here is a list of the algorithms we support, and the platforms they c | [Segment Anything](/serverless/pytorch/facebookresearch/sam/nuclio/) | interactor | PyTorch | ✔️ | ✔️ | | [Deep Extreme Cut](/serverless/openvino/dextr/nuclio) | interactor | OpenVINO | ✔️ | | | [Faster RCNN](/serverless/openvino/omz/public/faster_rcnn_inception_resnet_v2_atrous_coco/nuclio) | detector | OpenVINO | ✔️ | | +| [Mask RCNN](/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio) | detector | OpenVINO | ✔️ | | | [YOLO v3](/serverless/openvino/omz/public/yolo-v3-tf/nuclio) | detector | OpenVINO | ✔️ | | | [YOLO v7](/serverless/onnx/WongKinYiu/yolov7/nuclio) | detector | ONNX | ✔️ | ✔️ | | [Object reidentification](/serverless/openvino/omz/intel/person-reidentification-retail-0277/nuclio) | reid | OpenVINO | ✔️ | | From f5416abd85b1f186c9b5839aa3feeda885781881 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Sat, 28 Dec 2024 18:35:19 +0530 Subject: [PATCH 07/10] Update overview.md --- site/content/en/docs/getting_started/overview.md | 1 + 1 file changed, 1 insertion(+) diff --git a/site/content/en/docs/getting_started/overview.md b/site/content/en/docs/getting_started/overview.md index 4b81ba8fed9c..258eb4890fbb 100644 --- a/site/content/en/docs/getting_started/overview.md +++ b/site/content/en/docs/getting_started/overview.md @@ -108,6 +108,7 @@ Below is a detailed table of the supported algorithms and the platforms they ope | [Segment Anything](https://github.com/cvat-ai/cvat/tree/develop/serverless/pytorch/facebookresearch/sam/nuclio) | Interactor | PyTorch | ✔️ | ✔️ | | [Deep Extreme Cut](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/dextr/nuclio) | Interactor | OpenVINO | ✔️ | | | [Faster RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/faster_rcnn_inception_resnet_v2_atrous_coco/nuclio) | Detector | OpenVINO | ✔️ | | +| [Mask RCNN](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco/nuclio) | Detector | OpenVINO | ✔️ | | | [YOLO v3](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/public/yolo-v3-tf/nuclio) | Detector | OpenVINO | ✔️ | | | [YOLO v7](https://github.com/cvat-ai/cvat/tree/develop/serverless/onnx/WongKinYiu/yolov7/nuclio) | Detector | ONNX | ✔️ | ✔️ | | [Object Reidentification](https://github.com/cvat-ai/cvat/tree/develop/serverless/openvino/omz/intel/person-reidentification-retail-0277/nuclio) | ReID | OpenVINO | ✔️ | | From 9085a9c5b39212eb13314addacac5598f2e4a326 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Sat, 28 Dec 2024 18:38:06 +0530 Subject: [PATCH 08/10] Update ai-tools.md --- site/content/en/docs/manual/advanced/ai-tools.md | 1 + 1 file changed, 1 insertion(+) diff --git a/site/content/en/docs/manual/advanced/ai-tools.md b/site/content/en/docs/manual/advanced/ai-tools.md index ac24cf3c0a16..d8c235242df4 100644 --- a/site/content/en/docs/manual/advanced/ai-tools.md +++ b/site/content/en/docs/manual/advanced/ai-tools.md @@ -201,6 +201,7 @@ see {{< ilink "/docs/manual/advanced/automatic-annotation" "Automatic annotation | Model | Description | | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| Mask RCNN | The model generates polygons for each instance of an object in the image.

    For more information, see:
  • [GitHub: Mask RCNN](https://github.com/matterport/Mask_RCNN)
  • [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.
    In this model, RPN and Fast R-CNN are combined into a single network.

    For more information, see:
  • [GitHub: Faster RCNN](https://github.com/ShaoqingRen/faster_rcnn)
  • [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.

    For more information, see:
  • [GitHub: YOLO v3](https://github.com/ultralytics/yolov3)
  • [Site: YOLO v3](https://docs.ultralytics.com/#yolov3)
  • [Paper: YOLO v3](https://arxiv.org/pdf/1804.02767v1.pdf) | | Semantic segmentation for ADAS | This is a segmentation network to classify each pixel into 20 classes.

    For more information, see:
  • [Site: ADAS](https://docs.openvino.ai/2019_R1/_semantic_segmentation_adas_0001_description_semantic_segmentation_adas_0001.html) | From 05b160b48b6574855eb0ba5ed90c60a9213864d7 Mon Sep 17 00:00:00 2001 From: Prateek Shukla Date: Sat, 28 Dec 2024 18:42:49 +0530 Subject: [PATCH 09/10] Update serverless-tutorial.md Made the required changes --- site/content/en/docs/manual/advanced/serverless-tutorial.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/site/content/en/docs/manual/advanced/serverless-tutorial.md b/site/content/en/docs/manual/advanced/serverless-tutorial.md index de0f09b1db19..1b3c77c3e843 100644 --- a/site/content/en/docs/manual/advanced/serverless-tutorial.md +++ b/site/content/en/docs/manual/advanced/serverless-tutorial.md @@ -24,7 +24,7 @@ that can _perfectly_ annotate 50% of your data equates to reducing manual annota Since we know DL models can help us to annotate faster, how then do we use them? In CVAT all such DL models are implemented as serverless functions using the [Nuclio][nuclio-homepage] serverless platform. There are multiple implemented functions that can be -found in the [serverless][cvat-builtin-serverless] directory such as _Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc. +found in the [serverless][cvat-builtin-serverless] directory such as _Mask RCNN, Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc. Follow [the installation guide][cvat-auto-annotation-guide] to build and deploy these serverless functions. See [the user guide][cvat-ai-tools-user-guide] to understand how to use these functions in the UI to automatically annotate data. @@ -161,6 +161,8 @@ Finally you will get bounding boxes. `SiamMask` model is more optimized to work on Nvidia GPUs. +- For more information about deploying the model for the GPU, [read on](#objects-segmentation-using-mask-rcnn). + ### Object detection using YOLO-v3 First of all let's deploy the DL model. 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