Comments (1)
Hi again. I found that the solution to this lies in implementing a code to read and analyze the 'det' variable (cuda tensor) obtained in the inference script (predict.py). The number of rows it has will essentially be equivalent to the number of detections while there are 38 columns. The bounding box info could be obtained from the first 4 columns, the 5th column represents the confidence and columns 6 to 38, I believe, corresponds to the segmentation mask information. It's possible to implement a code to sort and filter instances as needed using this information :)
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Related Issues (20)
- Dataset quantity. HOT 2
- RuntimeError : Inplace update to interence tensor outside InterenceMode is not allowed HOT 1
- requirements.txt install cpu version of torch HOT 1
- where is cfg? HOT 2
- Do you have any plan for larger model? HOT 2
- Validation Error HOT 1
- customize segmentation points
- IndexError: boolean index did not match indexed array along dimension 0 HOT 6
- how to get the area of segmented object? HOT 3
- Is there an interpretation video for the predict.py code? HOT 1
- How can I get a heat map for each class on this? HOT 3
- Segmentation: Binary Mask instead of Mask Overlay HOT 4
- Uploading the trained model
- How can I obtain only segmentation mask without bounding box? HOT 3
- Yolov7 tiny for segmentation HOT 1
- yolov7 segemntation onnx HOT 1
- getting segmentation points as pixel value HOT 1
- yolov7 segmentation on custom data HOT 1
- negative samples HOT 4
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