yjh0410 b91f05518f add RTMDet-v2		2 年前
..
README.md	b91f05518f add RTMDet-v2	2 年前
build.py	b91f05518f add RTMDet-v2	2 年前
loss.py	b91f05518f add RTMDet-v2	2 年前
matcher.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_backbone.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_basic.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_head.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_neck.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_pafpn.py	b91f05518f add RTMDet-v2	2 年前
rtmdet_v2_pred.py	b91f05518f add RTMDet-v2	2 年前

RTMDet-v2: My Second Empirical Study of Real-Time General Object Detectors.

For training, we train my RTMDetv2 series series with 300 epochs on COCO.
For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of YOLOX, but we remove the rotation transformation which is used in YOLOX's strong augmentation.
For optimizer, we use AdamW with weight decay 0.05 and base per image lr 0.001 / 64.
For learning rate scheduler, we use linear decay scheduler.
Due to my limited computing resources, I can not train RTMDetv2-X with the setting of batch size=128.

Model	Scale	Batch	AP^{test 0.5:0.95}	AP^test 0.5	AP^val 0.5:0.95	AP^val 0.5	FLOPs ^(G)	Params ^(M)	Weight
RTMDetv2-N	640	8xb16
RTMDetv2-T	640	8xb16
RTMDetv2-S	640	8xb16
RTMDetv2-M	640	8xb16
RTMDetv2-L	640	8xb16
RTMDetv2-X	640