Instance Segmentation performance?

Hi, I use the following command to train instance segmentation

python -m torch.distributed.launch --nproc_per_node=4  --use_en main.py --epochs 25 --lr_drop 15 --coco_path $COCO/data --frozen_weights pretrained/detr-r50-e632da11.pth  --batch_size 2 --masks --no_aux_loss

The trained model only has a Seg AP of 24.7, is the normal?

[alcinos]

Final performance: IoU metric: segm Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.565 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.339 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.368 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.531 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.280 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.433 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.666

[trqminh]

Hi @alcinos , I wonder what is the instance segmentation head is used here? Is it a transformer-based or conv-based? Thank you for your consideration.