I3Net

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Implementation of "I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors" (CVPR 2021)

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# I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors(CVPR 2021)
A Pytorch Implementation of "I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors".

## Introduction
Please follow [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch) respository to setup the environment. In this project, we use Pytorch 1.0.1 and CUDA version is 10.0.130.

## Datasets
* **PASCAL_VOC 07+12**: Please follow the [instruction](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models) to prepare VOC dataset.
* **Clipart/WaterColor/Comic**: Please follow the [instruction](https://github.com/naoto0804/cross-domain-detection/tree/master/datasets) to prepare dataset.

## Pre-trained Models
* First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
* By default, we assume you have downloaded the file in the `ssd.pytorch/weights` dir:
```
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
```
## Train
```
CUDA_VISIBLE_DEVICES=$GPU_ID \
       python train_I3N.py \
       --name file_name\
       --dataset source_dataset --dataset_target target_dataset \
       --basenet path_to_model
```
## Test
```
CUDA_VISIBLE_DEVICES=$GPU_ID \
       python eval_I3N.py \
       --dataset target_dataset\
       --trained_model_path path_to_model
```
## Citation
If you find this repository useful, please cite our paper:
```
@inproceedings{CHEN_2021_I3NET,
  title={I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors},
  author={Chen, Chaoqi and Zheng, Zebiao and Huang, Yue and Ding, Xinghao and Yu, Yizhou},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}
```

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