SwinT_detectron2
Swin Transformer for Object Detection by detectron2
This repo contains the supported code and configuration files to reproduce object detection results of Swin Transformer. It is based on detectron2.
You can find SwinV2 in this repo
Results and Models
RetinaNet
| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
|---|---|---|---|---|---|---|---|---|---|
| Swin-T | ImageNet-1K | 3x | 44.6 | - | - | - | config | - | model |
Faster R-CNN
| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
|---|---|---|---|---|---|---|---|---|---|
| Swin-T FPN | ImageNet-1K | 3x | 45.1 | - | - | - | config | - | model |
Mask R-CNN
| Backbone | Pretrain | Lr Schd | box mAP | mask mAP | #params | FLOPs | config | log | model |
|---|---|---|---|---|---|---|---|---|---|
| Swin-T FPN | ImageNet-1K | 3x | 45.5 | 41.8 | - | - | config | - | model |
The mask mAP (41.8 vs 41.6) is same as the mmdetection, but box mAP is worse (45.5 vs 46.0)
Usage
Please refer to get_started.md for installation and dataset preparation.
note: you need convert the original pretrained weights to d2 format by convert_to_d2.py