Removed Mentions of Mask RCNN #8853
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Removed Masked RCNN
Removed masked rcnn from overview
removed mentions of Mask RCNN
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Important Review skippedAuto incremental reviews are disabled on this repository. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe pull request introduces comprehensive updates to CVAT's documentation across multiple files. The changes focus on enhancing user understanding by providing more detailed information about the tool's capabilities, annotation formats, AI tools, and serverless functions. The updates span README.md, overview documentation, AI tools guide, and serverless tutorial, with improvements in content clarity, model descriptions, and deployment instructions. Changes
Sequence DiagramsequenceDiagram
participant User
participant CVAT
participant ServerlessFunction
participant AIModel
User->>CVAT: Select Annotation Task
CVAT->>ServerlessFunction: Deploy Serverless Function
ServerlessFunction->>AIModel: Load Pre-trained Model
User->>CVAT: Upload Image/Video
CVAT->>ServerlessFunction: Request Annotation
ServerlessFunction->>AIModel: Process Image
AIModel-->>ServerlessFunction: Return Annotations
ServerlessFunction-->>CVAT: Provide Annotation Results
CVAT-->>User: Display Annotated Data
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prateekshukla1108
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I’m not sure if removing all mentions of Mask R-CNN from the documentation is the right approach.
First, we still support the CPU version of it.
Second, it would be better to replace the Mask R-CNN section in the tutorial with another existing GPU detector, such as Faster R-CNN. Writing a new tutorial may not need to be part of this PR, but at the very least, we should retain the CPU section
| @@ -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 | ✔️ | | | |||
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Thist model was not removed
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Once again, this function is not removed.
hi klakhov, thanks for the correction, I will correct it! |
Added mask RCNN as CPU version is still supported
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I corrected it! Thanks for the feedback and sorry for the bad PR. |
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Could you please avoid deleting the GPU deployment section of the tutorial in the documentation? Instead, consider updating it to reference another available GPU function from the repository.
I believe removing the GPU tutorial entirely is bad idea, as having a slightly outdated tutorial is still better than having none at all.
| @@ -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 | ✔️ | | | |||
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Once again, this function is not removed.
| @@ -201,11 +201,9 @@ see {{< ilink "/docs/manual/advanced/automatic-annotation" "Automatic annotation | |||
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| | Model | Description | | |||
| | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | |||
| | 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) | | |||
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No need to delete this line
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| Now you should be able to annotate objects using segmentation masks. | ||
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Please check that this image is not used anywhere else and also delete it.
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I don't think that it is not used anywhere as I searched the whole website when I was removing mentions of mask rcnn I searched the whole website
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Ok, great.
Could deploy some other available gpu function and change the tutorial to use that function?
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Ok, great. Could deploy some other available gpu function and change the tutorial to use that function?
I am still getting familiar with codebase, I would do it by tomorrow
| @@ -161,7 +160,6 @@ Finally you will get bounding boxes. | |||
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| `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). | |||
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Lets not remove documentation about GPU functions deployment. As a bit outdated documentation is better than no docs at all.
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@klakhov Thanks for your patience. I am new to this project so I just made the changes without giving it much thought |
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Removed mentions of Masked RCNN from the website and README
Motivation and context
Mask RCNN was removed but the documentation and README wasn't modified
How has this been tested?
Just checked in the preview tab in github
Checklist
developbranch(cvat-canvas,
cvat-core,
cvat-data and
cvat-ui)
License
Feel free to contact the maintainers if that's a concern.
Summary by CodeRabbit
New Features
Bug Fixes
Documentation