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MI-DETR

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[CVPR 2025 🔥] MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism

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<h2 align="center"> <a href="https://arxiv.org/abs/2503.01463">MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism</a></h2>
<h4 align="center" color="A0A0A0"> Zhixiong Nan, Xianghong Li, Jifeng Dai, Tao Xiang</h4>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5>

[![arXiv](https://img.shields.io/badge/Arxiv-2503.01463-b31b1b.svg?logo=arXiv)](https://www.arxiv.org/abs/2503.01463)
[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/CQU-ADHRI-Lab/MI-DETR)
[![License](https://img.shields.io/badge/Code%20License-Apache2.0-yellow)](https://github.com/CQU-ADHRI-Lab/MI-DETR/blob/main/LICENSE)

</div>

# MI-DETR
This is the official implementation of the paper "MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism".

Based on analyzing the character of cascaded decoder architecture commonly adopted in existing DETR-like models, this paper proposes a new decoder architecture. _The cascaded decoder architecture constrains object queries to update in the cascaded direction, only enabling object queries to learn relatively-limited information from image features._ However, the challenges for object detection in natural scenes (e.g., extremely-small, heavily-occluded, and confusingly mixed with the background) require an object detection model to fully utilize image features, which motivates us to propose a new decoder architecture with the parallel **Multi-time Inquiries (MI)** mechanism. _**MI** enables object queries to learn more comprehensive information, and our **MI** based model, **MI-DETR**, outperforms all existing DETR-like models on COCO benchmark under different backbones and training epochs_,  achieving **+2.3** AP and **+0.6** AP improvements compared to the most representative model DINO and SOTA model Relation-DETR under ResNet-50 backbone.

## Update
[2025/3] Code for [MI-DETR](https://github.com/CQU-ADHRI-Lab/MI-DETR) is available here!

[2025/2] MI-DETR has been accepted at CVPR 2025 as a poster!

## Installation

We tested our code with `Python=3.8.39, PyTorch=1.12.0, CUDA=11.3`. Please install PyTorch first according to [official instructions](https://pytorch.org/get-started/previous-versions/). Our code is based on [detrex](https://github.com/IDEA-Research/detrex/tree/main). Please refer to the [installation](https://detrex.readthedocs.io/en/latest/tutorials/Installation.html) of detrex.

Example conda environment setup：

```bash
# Create a new virtual environment
conda create -n midetr python=3.8 -y
conda activate midetr

# Install PyTorch
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113

# initialize the detectron2 submodule
git init
git submodule init
git submodule update

# Install detectron2
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

# Under your working directory
git clone https://github.com/CQU-ADHRI-Lab/MI-DETR.git
cd MI-DETR/
pip install -r requirements.txt

# build an editable version of detrex
pip install -e .
```

## Models

<table style="width: 100%; border-collapse: collapse;"  id="model-table">
    <tr style="border: 1px solid black; background-color: #f2f2f2; text-align: center; padding: 8px;">
        <th align="center">Name</th>
        <th align="center">Backbone</th>
        <th align="center">Epochs</th>
        <th align="center"><i>AP</i></th>
        <th align="center">Download</th>
    </tr>
    <tr align="center">
        <td align="center"><a href="./projects/midetr/configs/midetr-resnet/midetr_r50_4scale_12ep.py" style="text-decoration: none; color: black;">MI-DETR</a></td>
        <td align="center">ResNet50</td>
        <td align="center">12</td>
        <td align="center">50.2</td>
        <td align="center"><a href="https://drive.google.com/file/d/1ONmGGOWcj4uzFjfrAQ9_H9ge8UhpdVxw/view?usp=drive_link" style="text-decoration: none; color: blue;">model</a></td>
    </tr>
    <tr align="center">
        <td align="center"><a href="./projects/midetr/configs/midetr-resnet/midetr_r50_4scale_24ep.py" style="text-decoration: none; color: black;">MI-DETR</a></td>
        <td align="center">ResNet50</td>
        <td align="center">24</td>
        <td align="center">51.2</td>
        <td align="center"><a href="https://drive.google.com/file/d/1FO1ht5N44clB1_65w5WQoUqB1COoM-lJ/view?usp=drive_link" style="text-decoration: none; color: blue;">model</a></td>
    </tr>
    <tr align="center">
        <td align="center"><a href="./projects/midetr/configs/midetr-swin/midetr_swin_large_384_4scale_12ep.py" style="text-decoration: none; color: black;">MI-DETR</a></td>
        <td align="center">Swin-Large-384</td>
        <td align="center">12</td>
        <td align="center">57.5</td>
        <td align="center"><a href="https://drive.google.com/file/d/1pCEOIIJ_jrQPWDxdWV8pxV-ODATFlKol/view?usp=drive_link" style="text-decoration: none; color: blue;">model</a></td>
    </tr>
</table>

## Run

### Training

Train MI-DETR with 8 GPUs:

```sh
python tools/train_net.py --config-file projects/midetr/configs/midetr-resnet/midetr_r50_4scale_12ep.py --num-gpus 8 --resume
```

### Evaluation

You can download our pretrained models and evaluate them with the following commands. 
```sh
python tools/train_net.py --config-file /path/to/config_file --num-gpus 8 --eval-only train.init_checkpoint=/path/to/model_checkpoint
```
For example, to reproduce our result, you can copy the config path from the model table, download the pretrained checkpoint into `/path/to/checkpoint_file`, and run 
```sh
python tools/train_net.py --config-file projects/midetr/configs/midetr-resnet/midetr_r50_4scale_12ep.py --num-gpus 8 --eval-only train.init_checkpoint=/path/to/model_checkpoint
```

## <a name="CitingMIDETR"></a>Citing MI-DETR

If you find our work helpful for your research, please consider citing the following BibTeX entry.

```BibTeX
@inproceedings{nan2024mi,
  title={MI-DETR: An Object Detection Model with Multi-time Inquiries Mechanism}, 
  author={Zhixiong Nan and Xianghong Li and Jifeng Dai and Tao Xiang},
  booktitle={Proceedings of  the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2025}
}
```

## Acknowledgement

Many thanks to these excellent opensource projects: 
* [DINO](https://github.com/IDEA-Research/DINO)
* [detrex](https://github.com/IDEA-Research/detrex)

MI-DETR

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