I am using a Raspberry Pi with DepthAI to detect passing planks. I have tried to make a YoloV7 model in the past, but realized that it could be too intensive for this simple task and the limited amount of computer resource I have (RPi).
I have tried running the YoloV4-tiny with custom data tutorial. However, the tutorial uses Tensorflow 1.x, which is unusable with Google Colab. Then, I have tried running it with Tensorflow 2.x. Everything ran smooth, until the training, where it gave me a cuDNN error :
cuDNN status Error in: file: ./src/convolutional_kernels.cu function: forward_convolutional_layer_gpu() line: 543
cuDNN Error: CUDNN_STATUS_BAD_PARAM
Darknet error location: ./src/convolutional_kernels.cu, forward_convolutional_layer_gpu(), line #543
cuDNN Error: CUDNN_STATUS_BAD_PARAM: Success
I could make the YoloV7 work, but I feel like it is unnecessary for the work I need the detection to do. But, if it is the only way to make it work due to the new tensorflow requirements, it'll have to do.
Therefore, here are my three questions :
- Is it possible to still use the yolov4-tiny tutorial even with the new TensorFlow requirements?
- Will there be an updated version, or a yoloV5-6-7-tiny tutorial?
- Unrelated, but can I use 300x300 images for training? The tutorial says the resolutions have to be divisible by 32. I have used 320x320 instead to make it work, but I am unsure if it's going to work.
Thanks a lot and have a nice day!