I prepared a small set of images containing cigarettes as I intend to have some object detection models to detect them.
The numbers of training and validation images are 453 and 102, respectively.
Here are a few questions, however.
- I trained both YOLOv4-tiny and detectnet_v2 using the same dataset.
I got a YOLOv4-tiny model reaching 88% mAP, but the one for detectnet_v2 got 0 or near 0 mAP.
I trained YOLOv4-tiny using the spec file that doesn’t use tfrecord. (the one whose name ends with “seq”), while it seems that tf records are required for training detectnet_v2.
Anyway, I still prepared tf records for detectnet_v2 and I didn’t encounter any error messages, but the training result is just terrible. I wonder what could be the possible reasons.
Here’re the 2 spec files I used when trying to train a detectnet_v2 model.
- There’re YOLOv4 and YOLOv4-tiny in TAO toolkit. I wonder if the differences between them are literally the spec files?
The default value of the backbone network for YOLOv4-tiny in its spec file is cspdarknet-tiny and there’re only big and middle anchor shapes.
I changed the backbone network to resnet18 and mobilenet_v1, set small anchor shapes for them and still successfully trained the network using the pretrained models of both backbone networks, respectively. Does this mean I was literally training “YOLOv4” instead of “YOLOv4-tiny” when the backbone network was no longer cspdarknet-tiny but other network containing more trainable parameters?