YoloV7 serverless detector feature for auto annotation #5552
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
@hardikdava , thanks for the contribution! |
|
@hardikdava , could you please help us to fix remark and pylint linters? Archives with logs can be found here: https://github.com/opencv/cvat/actions/runs/3858059000 and https://github.com/opencv/cvat/actions/runs/3858058989 |
nmanovic
requested review from
yasakova-anastasia
and removed request for
nmanovic
yasakova-anastasia
approved these changes
Jan 10, 2023
mikhail-treskin
pushed a commit
to retailnext/cvat
that referenced
this pull request
Jul 1, 2023
### Motivation and context Integration of YOLOv7 as a serverless nuclio function that can be used for auto-labeling. YoloV7 is the SOTA at the time of this PR therefore it would make sense to support it in CVAT. The integration is quite simple into CVAT as docker based on Ultralytics YoloV5 with coco pretrained model (https://github.com/WongKinYiu/yolov7) and a docker image (https://hub.docker.com/r/ultralytics/yolov5). related issue: cvat-ai#5548 ### How has this been tested? Automatic annotation was run using YOLOv7 on a custom dataset. The serverless function was deployed using ``` nuctl deploy --project-name cvat \ --path serverless/onnx/WongKinYiu/yolov7/nuclio \ --volume `pwd`/serverless/common:/opt/nuclio/common \ --platform local ``` Then using the 'Automatic annotation' action the function was tested and the auto-generated labels were controlled to check that no coordinates misfit is happening. ### Use custom model: 1. Export your model with NMS for image resolution of 640x640 (preferable). 2. Copy your custom model yolov7-custom.onnx to /serverless/common 3. Modify function.yaml file according to your labels. 4. Modify model_handler.py as follow: ``` self.model_path = "yolov7-custom.onnx" ``` Co-authored-by: Nikita Manovich <nikita@cvat.ai> Co-authored-by: yasakova-anastasia <yasakova_anastasiya@mail.ru>
|
Great! I deeply appreciate this PR. Does anyone know how we can tweak the parameters, like decrease the confidence interval? Or how can we even use yolov8 in cvat's auto annotation? Thanks |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Motivation and context
Integration of YOLOv7 as a serverless nuclio function that can be used for auto-labeling. YoloV7 is the SOTA at the time of this PR therefore it would make sense to support it in CVAT. The integration is quite simple into CVAT as docker based on Ultralytics YoloV5 with coco pretrained model (https://github.com/WongKinYiu/yolov7) and a docker image (https://hub.docker.com/r/ultralytics/yolov5).
related issue: #5548
How has this been tested?
Automatic annotation was run using YOLOv7 on a custom dataset.
The serverless function was deployed using
Then using the 'Automatic annotation' action the function was tested and the auto-generated labels were controlled to check that no coordinates misfit is happening.
Checklist
developbranchcvat-core, cvat-data and cvat-ui)
Use custom model:
License
Feel free to contact the maintainers if that's a concern.