# 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)]
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
```
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|model \| metrics|
|TridentFast|R50-C4|C5-128ROI|1X|38.0|58.1|40.8|19.5|42.2|54.6|model \| metrics|
|Faster|R50-C4|C5-512ROI|3X|38.4|58.7|41.3|20.7|42.7|53.1|model \| metrics|
|TridentFast|R50-C4|C5-128ROI|3X|40.6|60.8|43.6|23.4|44.7|57.1|model \| metrics|
|Faster|R101-C4|C5-512ROI|3X|41.1|61.4|44.0|22.2|45.5|55.9|model \| metrics|
|TridentFast|R101-C4|C5-128ROI|3X|43.6|63.4|47.0|24.3|47.8|60.0|model \| metrics|
## 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}
}
```