The additional step is to add a [security group and rule to allow incoming connections](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network-security.html).
For any of above, don't forget to set the `CVAT_HOST` environemnt variable to the exposed
For any of above, don't forget to set the `CVAT_HOST` environment variable to the exposed
| 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) |
| 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) |
| 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) |
| RetinaNet | Pytorch implementation of RetinaNet object detection. <br><br><br> For more information, see: <li>[Specification: RetinaNet](https://paperswithcode.com/lib/detectron2/retinanet) <li>[Paper: RetinaNet](https://arxiv.org/pdf/1708.02002.pdf)<li>[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. <br><br><br> For more information, see: <li>[Site: Face Detection 0205](https://docs.openvino.ai/latest/omz_models_model_face_detection_0205.html) |
@ -276,8 +276,8 @@ All annotated objects will be automatically tracked when you move to the next fr
| TrackerMIL | OpenCV | TrackerMIL model is not bound to <br>labels and can be used for any <br>object. It is a fast client-side model <br>designed to track simple non-overlapping objects. <br><br>For more information, see: <li>[Article: Object Tracking using OpenCV](https://learnopencv.com/tag/mil/) |  |
| SiamMask | AI Tools | Fast online Object Tracking and Segmentation. The trackable object will <br>be tracked automatically if the previous frame <br>was the latest keyframe for the object. <br><br>For more information, see:<li> [Github: SiamMask](https://github.com/foolwood/SiamMask) <li> [Paper: SiamMask](https://arxiv.org/pdf/1812.05050.pdf) |  |
| Transformer Tracking (TransT) | AI Tools | Simple and efficient online tool for object tracking and segmentation. <br>If the previous frame was the latest keyframe <br>for the object, the trackable object will be tracked automatically.<br>This is a modified version of the PyTracking <br> Python framework based on Pytorch<br><br><br>For more information, see: <li> [Github: TransT](https://github.com/chenxin-dlut/TransT)<li> [Paper: TransT](https://arxiv.org/pdf/2103.15436.pdf) |  |
| SiamMask | AI Tools | Fast online Object Tracking and Segmentation. The trackable object will <br>be tracked automatically if the previous frame <br>was the latest keyframe for the object. <br><br>For more information, see:<li> [GitHub: SiamMask](https://github.com/foolwood/SiamMask) <li> [Paper: SiamMask](https://arxiv.org/pdf/1812.05050.pdf) |  |
| Transformer Tracking (TransT) | AI Tools | Simple and efficient online tool for object tracking and segmentation. <br>If the previous frame was the latest keyframe <br>for the object, the trackable object will be tracked automatically.<br>This is a modified version of the PyTracking <br> Python framework based on Pytorch<br><br><br>For more information, see: <li> [GitHub: TransT](https://github.com/chenxin-dlut/TransT)<li> [Paper: TransT](https://arxiv.org/pdf/2103.15436.pdf) |  |
|| **Brush** adds new mask/ new regions to the previously added mask).|
||**Eraser** removes part of the mask.|
||**Polygon** selection tool. Selection will become a mask.|
||**Remove polygon selection** substracts part of the polygon selection.|
||**Remove polygon selection** subtracts part of the polygon selection.|
||**Brush size** in pixels. <br>**Note:** Visible only when **Brush** or **Eraser** are selected.|
||**Brush shape** with two options: circle and square. <br>**Note:** Visible only when **Brush** or **Eraser** are selected.|
||**Remove underlying pixels**. When you are drawing or editing a mask with this tool, <br>pixels on other masks that are located at the same positions as the pixels of the <br>current mask are deleted.|
|  | **Fit views**. Click to restore the layout to its original appearance. <p>If you've expanded any images in the layout, they will returned to their original size. <p>This won't affect the number of context images on the screen. |
|  | **Add new image**. Click to add context image to the layout. |
|  | **Reload layout**. Click to reload layout to the default view. <p>Note, that this action can change the number of context images reseting them back to three. |
|  | **Reload layout**. Click to reload layout to the default view. <p>Note, that this action can change the number of context images resetting them back to three. |
Roman Donchenko
3 years ago
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GitHub