Fine-tuned SGGen model mAP result about scene-graph-benchmark.pytorch

[narchitect]

Do not replace the full back

Thank you so much for your reply! I was also planning to use another object detection model. hope I get better results soon thanks again!

[Maelic]

Hi @narchitect ,

The mAP you are referring to here is the performance of your object detection alone (i.e. bounding box regression + classification), I would suggest you to switch from Faster-RCNN to another detector which will be more performant for few-shot settings, which seems to be your case. Faster-RCNN is a pretty old and bad detector at this point, especially for few-shot, using a more recent detector pretrained on a larger dataset such as Swin transformer, DETR, ViT etc will be better.
Then you can train a SGGen model by freezing the weights of your object detector and replacing the RPN layers, as I explained it in here.
Do not replace the full backbone layers or you will have to change the features extractor as well, which is more complex.

[Lxy811]

Hi @narchitect ,

The mAP you are referring to here is the performance of your object detection alone (i.e. bounding box regression + classification), I would suggest you to switch from Faster-RCNN to another detector which will be more performant for few-shot settings, which seems to be your case. Faster-RCNN is a pretty old and bad detector at this point, especially for few-shot, using a more recent detector pretrained on a larger dataset such as Swin transformer, DETR, ViT etc will be better. Then you can train a SGGen model by freezing the weights of your object detector and replacing the RPN layers, as I explained it in here. Do not replace the full backbone layers or you will have to change the features extractor as well, which is more complex.

How to freeze the weight of your object detector and how to implement the code

[Maelic]

Hi @narchitect ,
The mAP you are referring to here is the performance of your object detection alone (i.e. bounding box regression + classification), I would suggest you to switch from Faster-RCNN to another detector which will be more performant for few-shot settings, which seems to be your case. Faster-RCNN is a pretty old and bad detector at this point, especially for few-shot, using a more recent detector pretrained on a larger dataset such as Swin transformer, DETR, ViT etc will be better. Then you can train a SGGen model by freezing the weights of your object detector and replacing the RPN layers, as I explained it in here. Do not replace the full backbone layers or you will have to change the features extractor as well, which is more complex.

How to freeze the weight of your object detector and how to implement the code

How to freeze the weights depends on your detector, however here you can do something simpler by forcing your detector to be in eval mode with something like model.rpn.eval() or model.backbone.eval() somewhere before your training loop, to ensure no gradients are computed.