Inside your yolov4_custom.cfg or the any cfg that you were using, you need to change the subdivisions to match your GPU performance. For example, for 32 GB GPU-VRAM, set …
03.08.2021 · I think a recent update with either Colab or Cuda is throwing off the YOLOv4 model. I have previously built this notebook and trained a complete model with it with no problems but now when I run the exact same code, with no changes, I get this problem: 672 x 672 try to allocate additional workspace_size = 65.03 MB CUDA allocate done!
I think a recent update with either Colab or Cuda is throwing off the YOLOv4 model. I have previously built this notebook and trained a complete model with ...
CUDA Error: out of memory darknet: ./src/cuda.c:36: check_error: Assertio `0' failed. Need to modify the parameters of subdivision in the model cfg file ...
17.02.2021 · I got this Error: RuntimeError: CUDA out of memory. GPU 0; 1.95 GiB total capacity; 1.23 GiB already allocated 1.27 GiB reserved in total by PyTorch. But it is not out of memory, it …
So when you try to execute the training, and you don't have enough free CUDA memory available, then the framework you're using throws this out of memory error.