| # TridentNet in Detectron2 | |
| **Scale-Aware Trident Networks for Object Detection** | |
| Yanghao Li\*, Yuntao Chen\*, Naiyan Wang, Zhaoxiang Zhang | |
| [[`TridentNet`](https://github.com/TuSimple/simpledet/tree/master/models/tridentnet)] [[`arXiv`](https://arxiv.org/abs/1901.01892)] [[`BibTeX`](#CitingTridentNet)] | |
| <div align="center"> | |
| <img src="https://drive.google.com/uc?export=view&id=10THEPdIPmf3ooMyNzrfZbpWihEBvixwt" width="700px" /> | |
| </div> | |
| In this repository, we implement TridentNet-Fast in Detectron2. | |
| Trident Network (TridentNet) aims to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. TridentNet-Fast is a fast approximation version of TridentNet that could achieve significant improvements without any additional parameters and computational cost. | |
| ## Training | |
| To train a model, run | |
| ```bash | |
| python /path/to/detectron2/projects/TridentNet/train_net.py --config-file <config.yaml> | |
| ``` | |
| For example, to launch end-to-end TridentNet training with ResNet-50 backbone on 8 GPUs, | |
| one should execute: | |
| ```bash | |
| python /path/to/detectron2/projects/TridentNet/train_net.py --config-file configs/tridentnet_fast_R_50_C4_1x.yaml --num-gpus 8 | |
| ``` | |
| ## Evaluation | |
| Model evaluation can be done similarly: | |
| ```bash | |
| python /path/to/detectron2/projects/TridentNet/train_net.py --config-file configs/tridentnet_fast_R_50_C4_1x.yaml --eval-only MODEL.WEIGHTS model.pth | |
| ``` | |
| ## Results on MS-COCO in Detectron2 | |
| |Model|Backbone|Head|lr sched|AP|AP50|AP75|APs|APm|APl|download| | |
| |-----|--------|----|--------|--|----|----|---|---|---|--------| | |
| |Faster|R50-C4|C5-512ROI|1X|35.7|56.1|38.0|19.2|40.9|48.7|<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/metrics.json">metrics</a>| | |
| |TridentFast|R50-C4|C5-128ROI|1X|38.0|58.1|40.8|19.5|42.2|54.6|<a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_50_C4_1x/148572687/model_final_756cda.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_50_C4_1x/148572687/metrics.json">metrics</a>| | |
| |Faster|R50-C4|C5-512ROI|3X|38.4|58.7|41.3|20.7|42.7|53.1|<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/model_final_f97cb7.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/metrics.json">metrics</a>| | |
| |TridentFast|R50-C4|C5-128ROI|3X|40.6|60.8|43.6|23.4|44.7|57.1|<a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_50_C4_3x/148572287/model_final_e1027c.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_50_C4_3x/148572287/metrics.json">metrics</a>| | |
| |Faster|R101-C4|C5-512ROI|3X|41.1|61.4|44.0|22.2|45.5|55.9|<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/model_final_298dad.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/metrics.json">metrics</a>| | |
| |TridentFast|R101-C4|C5-128ROI|3X|43.6|63.4|47.0|24.3|47.8|60.0|<a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_101_C4_3x/148572198/model_final_164568.pkl">model</a> \| <a href="https://dl.fbaipublicfiles.com/detectron2/TridentNet/tridentnet_fast_R_101_C4_3x/148572198/metrics.json">metrics</a>| | |
| ## <a name="CitingTridentNet"></a>Citing TridentNet | |
| If you use TridentNet, please use the following BibTeX entry. | |
| ``` | |
| @InProceedings{li2019scale, | |
| title={Scale-Aware Trident Networks for Object Detection}, | |
| author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang}, | |
| journal={The International Conference on Computer Vision (ICCV)}, | |
| year={2019} | |
| } | |
| ``` | |