Spaces:
Runtime error
Runtime error
| # MODEL_ZOO | |
| ### Common settings and notes | |
| - Multiscale training is used by default in all models. The results are all reported using single-scale testing. | |
| - We report runtime on our local workstation with a TitanXp GPU and a Titan RTX GPU. | |
| - All models are trained on 8-GPU servers by default. The 1280 models are trained on 24G GPUs. Reducing the batchsize with the linear learning rate rule should be fine. | |
| - All models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1meZIsz8E3Ia9CRxLOAULDLeYrKMhhjJE). | |
| ## COCO | |
| ### CenterNet | |
| | Model | val mAP | FPS (Titan Xp/ Titan RTX) | links | | |
| |-------------------------------------------|---------|---------|-----------| | |
| | CenterNet-S4_DLA_8x | 42.5 | 50 / 71 |[config](../configs/CenterNet-S4_DLA_8x.yaml)/[model](https://drive.google.com/file/d/1AVfs9OoLePk_sqTPvqdRi1cXmO2cD0W_)| | |
| | CenterNet-FPN_R50_1x | 40.2 | 20 / 24 |[config](../configs/CenterNet-FPN_R50_1x.yaml)/[model](https://drive.google.com/file/d/1iYlmjsBt9YIcaI8NzEwiMoaDDMHRmcR9)| | |
| #### Note | |
| - `CenterNet-S4_DLA_8x` is a re-implemented version of the original CenterNet (stride 4), with several changes, including | |
| - Using top-left-right-bottom box encoding and GIoU Loss; adding regression loss to the center 3x3 region. | |
| - Adding more positive pixels for the heatmap loss whose regression loss is small and is within the center3x3 region. | |
| - Using more heavy crop augmentation (EfficientDet-style crop ratio 0.1-2), and removing color augmentations. | |
| - Using standard NMS instead of max pooling. | |
| - Using RetinaNet-style optimizer (SGD), learning rate rule (0.01 for each batch size 16), and schedule (8x12 epochs). | |
| - `CenterNet-FPN_R50_1x` is a (new) FPN version of CenterNet. It includes the changes above, and assigns objects to FPN levels based on a fixed size range. The model is trained with standard short edge 640-800 multi-scale training with 12 epochs (1x). | |
| ### CenterNet2 | |
| | Model | val mAP | FPS (Titan Xp/ Titan RTX) | links | | |
| |-------------------------------------------|---------|---------|-----------| | |
| | CenterNet2-F_R50_1x | 41.7 | 22 / 27 |[config](../configs/CenterNet2-F_R50_1x.yaml)/[model](X)| | |
| | CenterNet2_R50_1x | 42.9 | 18 / 24 |[config](../configs/CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1Qn0E_F1cmXtKPEdyZ_lSt-bnM9NueQpq)| | |
| | CenterNet2_X101-DCN_2x | 49.9 | 6 / 8 |[config](../configs/CenterNet2_X101-DCN_2x.yaml)/[model](https://drive.google.com/file/d/1yuJbIlUgMiXdaDWRWArcsRsSoHti9e1y)| | |
| | CenterNet2_DLA-BiFPN-P3_4x | 43.8 | 40 / 50|[config](../configs/CenterNet2_DLA-BiFPN-P3_4x.yaml)/[model](https://drive.google.com/file/d/1UGrnOE0W8Tgu6ffcCOQEbeUgThtDkbuQ)| | |
| | CenterNet2_DLA-BiFPN-P3_24x | 45.6 | 40 / 50 |[config](../configs/CenterNet2_DLA-BiFPN-P3_24x.yaml)/[model](https://drive.google.com/file/d/17osgvr_Zhp9SS2uMa_YLiKwkKJIDtwPZ)| | |
| | CenterNet2_R2-101-DCN_896_4x | 51.2 | 9 / 13 |[config](../configs/CenterNet2_R2-101-DCN_896_4x.yaml)/[model](https://drive.google.com/file/d/1YiJm7UtMstl63E8I4qQ8owteYC5zRFuQ)| | |
| | CenterNet2_R2-101-DCN-BiFPN_1280_4x | 52.9 | 6 / 8 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml)/[model](https://drive.google.com/file/d/1BIfEH04Lm3EvW9ov76yEPntUOJxaVoKd)| | |
| | CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST | 56.1 | 3 / 5 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml)/[model](https://drive.google.com/file/d/1GZyzJLB3FTcs8C7MpZRQWw44liYPyOMD)| | |
| | CenterNet2_DLA-BiFPN-P5_640_24x_ST | 49.2 | 33 / 38 |[config](../configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml)/[model](https://drive.google.com/file/d/1pGXpnHhvi66my_p5dASTnTjvaaj0FEvE)| | |
| #### Note | |
| - `CenterNet2-F_R50_1x` uses Faster RCNN as the second stage. All other CenterNet2 models use Cascade RCNN as the second stage. | |
| - `CenterNet2_DLA-BiFPN-P3_4x` follows the same training setting as [realtime-FCOS](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md). | |
| - `CenterNet2_DLA-BiFPN-P3_24x` is trained by repeating the `4x` schedule (starting from learning rate 0.01) 6 times. | |
| - R2 means [Res2Net](https://github.com/Res2Net/Res2Net-detectron2) backbone. To train Res2Net models, you need to download the ImageNet pre-trained weight [here](https://github.com/Res2Net/Res2Net-detectron2) and place it in `output/r2_101.pkl`. | |
| - The last 4 models in the table are trained with the EfficientDet-style resize-and-crop augmentation, instead of the default random resizing short edge in detectron2. We found this trains faster (per-iteration) and gives better performance under a long schedule. | |
| - `_ST` means using [self-training](https://arxiv.org/abs/2006.06882) using pseudo-labels produced by [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) on COCO unlabeled images, with a hard score threshold 0.5. Our processed pseudo-labels can be downloaded [here](https://drive.google.com/file/d/1R9tHlUaIrujmK6T08yJ0T77b2XzekisC). | |
| - `CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST` finetunes from `CenterNet2_R2-101-DCN-BiFPN_1280_4x` for an additional `4x` schedule with the self-training data. It is trained under `1280x1280` but tested under `1560x1560`. | |
| ## LVIS v1 | |
| | Model | val mAP box | links | | |
| |-------------------------------------------|--------------|-----------| | |
| | LVIS_CenterNet2_R50_1x | 26.5 |[config](../configs/LVIS_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1oOOKEDQIWW19AHhfnTb7HYZ3Z9gkZn_K)| | |
| | LVIS_CenterNet2_R50_Fed_1x | 28.3 |[config](../configs/LVIS_CenterNet2_R50_Fed_1x.yaml)/[model](https://drive.google.com/file/d/1ETurGA7KIC5XMkMBI8MOIMDD_iJyMTif)| | |
| - The models are trained with repeat-factor sampling. | |
| - `LVIS_CenterNet2_R50_Fed_1x` is CenterNet2 with our federated loss. Check our Appendix D of our [paper](https://arxiv.org/abs/2103.07461) or our [technical report at LVIS challenge](https://www.lvisdataset.org/assets/challenge_reports/2020/CenterNet2.pdf) for references. | |
| ## Objects365 | |
| | Model | val mAP| links | | |
| |-------------------------------------------|---------|-----------| | |
| | O365_CenterNet2_R50_1x | 22.6 |[config](../configs/O365_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/11d1Qx75otBAQQL2raxMTVJb17Qr56M3O)| | |
| #### Note | |
| - Objects365 dataset can be downloaded [here](https://www.objects365.org/overview.html). | |
| - The model is trained with class-aware sampling. | |