Comments (5)
dagshub
commented on June 20, 2024
Join the discussion on DagsHub!
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BloodAxe
commented on June 20, 2024
My guess that it is OOM error. In theory if the video has high resolution frames that we accumulate during processing may sum up and take all system memory. Do you mind sharing a video (or at least it's dimensions) you're trying to process?
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Lifeguard-alex
commented on June 20, 2024
i get the same problem on
yolo_nas_l.to(device).predict(input_video_path).save(output_video_path)
memory get all 125gb i have and crash + 2 gb of swap
i get this working ok on the same video on yolo 7 and 8 and yolox
i am infer on yolo_nas_l
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Louis-Dupont
commented on June 20, 2024
Hi @sivaji123256 and @Lifeguard-alex ,
Please check out #976, does this solve your issue ?
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BloodAxe
commented on June 20, 2024
We have implemented memory-efficient predict for video in super-gradients 3.5.0.
So you are welcome to upgrade to a new version of SG which should not have this issue.
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Related Issues (20)
- ERROR: Failed building wheel for pycocotools HOT 1
- pip install super-gradients failed HOT 2
- AttributeError: 'YoloNAS_S' object has no attribute 'model' HOT 4
- Error with Model Loaded from Checkpoint HOT 5
- Get loss and evaluation values=0 when training yolonas with custom dataset HOT 1
- image not identified error with model.predict()
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- AttributeError: 'Grayscale' object has no attribute 'apply_to_sample'AttributeError: 'Grayscale' object has no attribute 'apply_to_sample'
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- Error on using YoloXDetectionLoss with YOLO-NAS HOT 3
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- Save the quantized YoloNAS model HOT 4
- Feature Extraction using YOLO-NAS HOT 3
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