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f3843aa74f
### 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 ``` 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> |
3 years ago | |
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| .. | ||
| common/openvino | 3 years ago | |
| onnx/WongKinYiu/yolov7/nuclio | 3 years ago | |
| openvino | 3 years ago | |
| pytorch | 3 years ago | |
| tensorflow | 3 years ago | |
| deploy_cpu.sh | 5 years ago | |
| deploy_gpu.sh | 5 years ago | |