You can download pre-trained models from https://drive.google.com/drive/folders/1LQnVHb5Xo6fKknUiOa1fXmGI_MCucGTC?usp=sharing
| ModelName | Dataset | Backbone | mAP | with DCN | with Sync BN | Target Size | Max Size | IM_PER_IMAGE | Number of GPUs | Epochs |
|---|---|---|---|---|---|---|---|---|---|---|
| FCOS No Tricks | COCO2017 | ResNet50 | 0.367 | False | False | 800 | 1333 | 4 | 4 | 6 |
| FCOS No Tricks | COCO2017 | Mobilenetv1-1.0 | 0.222 | False | False | 500 | 833 | 4 | 4 | 6 |
| FCOS | COCO2017 | ResNet50 | - | True | False | 800 | 1000 | 2 | 3 | 14 |
| HRNet-cls | - | - | See HRNet | - | - | - | - | - | - | - |
| RetinaNet | COCO2017 | ResNet50 | 0.325 | False | False | 500 | 833 | 2 | 3 | 6 |
| OpenPose | COCO2017 | Dilated-ResNet50 | 0.564 | False | False | 368 | 368 | 4 | 3 | 40 |
| OpenPose | COCO2017 | VGG16 | 0.561 | False | False | 368 | 368 | 4 | 3 | 40 |
| RFCN | VOC12+07 | Dilated-ResNet101 | 0.825 | Only 3 | False | 800 | 1280 | 1 | 3 | 6 |
| RFCN | VOC12+07 | Dilated-ResNet50 | 0.804 | Only 3 | False | 800 | 1280 | 1 | 3 | 6 |
| FPN(MS) | COCO2017 | SEResNext50_32x4d | 0.376 | True | False | 800 | 1280 | 1 | 4 | 5 |
| FPN(MS) | COCO2017 | Dilated-ResNet101 | 0.412 | True | False | 800 | 1280 | 1 | 4 | 5 |
Notes:
FCOS No Tricks means the setting is same as original paper, i.e., centerness is on cls branch, GN is added, use P5 instead of C5,
and other setting like norm_on_bbox, centerness_on_reg, center_sampling is set to False. The mAP reported by the original paper is 0.371.
For more information about FCOS, please see fcos.md
For OpenPose, please go into https://github.com/kohillyang/mx-openpose for more information.
RFCN trained on VOC is reported as mAP@IoU=0.5 according to VOC Metric, and it is slightly different from mAP @IoU=0.5 of COCO.
MS means the model is using multi-scaling when training.
FPN(MS) and RFCN are bought from https://github.com/msracver/Deformable-ConvNets and rewritten by new Gluon API, their performance should be same with the model from https://github.com/msracver/Deformable-ConvNets.
Greatly thanks to https://github.com/wkcn/MobulaOP by @wkcn.
If you have any question or suggestion, please feel free to send me a mail or create an issue.