Alex Arion

I've started to build my own reduced size models!!

> > I've started to build my own reduced size models!! > > How? By starting from the -C model spec (yolov9-c.yaml) and reducing the various components sizes.

@minhnhathcmus I will, I had some challenges to train properly - https://github.com/WongKinYiu/yolov9/issues/414 But now I have a new lead.

@minhnhathcmus unfortunately I'm am unable to train a good model: the trained model mAP decreased when I remove the auxiliary branch using the reparameterization script. I think there is a...

@minhnhathcmus I did not change number of layers, my base mode is yolov9-c, only reduced number of elements/parameters in each layer. I did check the reparam code, and from what...

@minhnhathcmus allow me to add to that: I tried a retrain of yolov9-c, so no changes needed anywhere, and again, after removing auxiliary branch, results are slightly worse (cc: @WongKinYiu...

@minhnhathcmus , what I didn't do, is to let it train for 500 epochs... not sure if this would solve the problem. @WongKinYiu do we need to train for 500...

Will try tomorrow the docker you mentioned. I tried barebones with rtx 4090, and there is discrepancy after reparameterization. Also, on a A4500, same, but smaller, discrepancy.

@levipereira for me, it's the same problem with the docker container you mentioned: when training from scratch a custom model (derived from yolo-c.yaml), the mAP of the trained model is...

The values in the paper are the ones after running the conversion script ([yolov9](https://github.com/WongKinYiu/yolov9/tree/main)/[tools](https://github.com/WongKinYiu/yolov9/tree/main/tools) /reparameterization.ipynb)