Closed sonyta29 closed 3 years ago
π Hello @sonyta29, thank you for your interest in YOLOv5 π! Please visit our βοΈ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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@sonyta29 π Hello! Thanks for asking about CUDA memory issues. YOLOv5 π can be trained on CPU, single-GPU, or multi-GPU. When training on GPU it is important to keep your batch-size small enough that you do not use all of your GPU memory, otherwise you will see a CUDA Out Of Memory (OOM) Error and your training will crash. You can observe your CUDA memory utilization using either the nvidia-smi
command or by viewing your console output:
If you encounter a CUDA OOM error, the steps you can take to reduce your memory usage are:
--batch-size
--img-size
--batch-size
@glenn-jocher Thank you for replying and explaining this issue... I already trained the yolov5s on Google Colab and then i have just downloaded the trained weights... by running the val.py with -- weights (my_custom_weights).pt make this problem.
I'm going to follow the reducing memory usage steps that you've mentioned. Thank you !
π Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
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hello, have you slove your problem?
βQuestion
I'm trying to evaluate the performance of yolov5s trained on costum dataset using val.py but i'm getting this error : CUDA out of memory ( when evaluating the yolov5s trained on the coco dataset i'm not getting this error) I tried this solution https://stackoverflow.com/questions/59129812/how-to-avoid-cuda-out-of-memory-in-pytorch but it didn't work
I'll be grateful for anyone who can help or suggests a working solution for this issue