---
title: EAEFNet_Detection
canonical_url: "https://www.modelscope.cn/models/AIBS/EAEFNet_Detection"
md_url: "https://www.modelscope.cn/models/AIBS/EAEFNet_Detection.md"
repository: AIBS/EAEFNet_Detection
last_updated: 2025-06-18
license: "Apache License 2.0"
pipeline_tag: image-object-detection
tasks:
  - image-object-detection
library_name:
  - onnx
  - other
frameworks:
  - other
downloads: 21
stars: 0
---

# EAEFNet_Detection

> EAEFNet_Detection - AIBS 在 ModelScope 开源的模型。Introduction This is the official pytorch implementation of EAEFNet: Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks. Some of the codes are borrowed from MFNet , RTFNet , LSNet and RGBT-CC. The…

AIBS/EAEFNet_Detection 是 ModelScope 魔搭社区上的image-object-detection模型，采用 Apache License 2.0 许可。

- **Repository**: AIBS/EAEFNet_Detection
- **License**: Apache License 2.0
- **Tasks**: image-object-detection
- **Downloads**: 21
- **Stars**: 0
- **Last updated**: 2025-06-18

Source: https://www.modelscope.cn/models/AIBS/EAEFNet_Detection

---

# EAEFNet

![license](https://img.shields.io/badge/license-MIT-green) ![PyTorch-1.10.0](https://img.shields.io/badge/PyTorch-1.10.0-blue)


## Introduction
This is the official pytorch implementation of EAEFNet: Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks. Some of the codes are borrowed from [MFNet](https://github.com/haqishen/MFNet-pytorch) , [RTFNet](https://github.com/yuxiangsun/RTFNet) , [LSNet](https://github.com/zyrant/LSNet) and [RGBT-CC](https://github.com/chen-judge/RGBTCrowdCounting). The master branch works with **PyTorch 1.10+**.

## Installation

**Crow-Counting**

```shell
conda create -n EAEF python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate EAEF
cd EAEFNet_RGBT-CC
pip install -r requirements.txt
```

**Detection**

```shell
conda create -n EAEF python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate EAEF
pip install openmim
mim install "mmengine>=0.3.1"
mim install "mmcv>=2.0.0rc1,<2.1.0"
mim install "mmdet>=3.0.0rc3,<3.1.0"
cd EAEFNet_Detection/EAEF_mmyolo
# Install albumentations
pip install -r requirements/albu.txt
# Install MMYOLO , don't forget it!
mim install -v -e .
```

**Segmentation**

```shell
conda create -n EAEF python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate EAEF
cd EAEFNet_Seg_MF/EAEFNet_MF
pip install -r requirements.txt
```

**SOD**

```shell
conda create -n EAEF python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate EAEF
cd EAEFNet_SOD
pip install -r requirements.txt
```


## Data Download 

<img width="1483" alt="b992b3bc62cb98ca8a87364468bdb32" src="https://github.com/FreeformRobotics/EAEFNet/assets/45811724/43c1960d-a529-432d-ad8b-00e08b0312c9">



MFTNet Dataset: http://gofile.me/4jm56/CfukComo1

PST900 Dataset: https://drive.google.com/file/d/1hZeM-MvdUC_Btyok7mdF00RV-InbAadm/view

M3FD Dataset: https://drive.google.com/drive/folders/1H-oO7bgRuVFYDcMGvxstT1nmy0WF_Y_6

RGBT-CC Dataset: https://www.dropbox.com/sh/o4ww2f5tv3nay9n/AAA4CfVMTZcdwsFxFlhwDsSba?dl=0

###### Ps: You also can download them from [here](https://drive.google.com/drive/folders/1fqNwaumH0BrcAIvS0ebAjS35LX31Yw4S?usp=share_link)!

## Trained Model Download


| Task         | Dataset | model   | mIoU   | Trained Model                                               |
| ------------ | ------- | ------- | ------ | ------------------------------------------------------------ |
| Segmentation | MFNet   | EAEFNet | 58.91% | https://drive.google.com/drive/folders/12ONwVaaO35VbW7rZ83P-pSVWp_bFiPhv?usp=share_link |
| Segmentation | PSP900  | EAEFNet | 85.56% | https://drive.google.com/drive/folders/1Czm7vtmaW6fTCk4fBAfO2OAWoHrJry9Z?usp=share_link |

| Task      | Dataset | model       | mAP@0.5 | Trained Model                                               |
| --------- | ------- | ----------- | ------- | ------------------------------------------------------------ |
| Detection | M3FD    | EAEF+Yolov5 | 80.4%   | https://drive.google.com/drive/folders/1JcvZUmTUB936H9JoYjYrM9H-jHKnjNzc?usp=share_link |

| Task          | Dataset | model | RMSE   | Trained Model                                               |
| ------------- | ------- | ----- | ------ | ------------------------------------------------------------ |
| Crowcount    | RGBTCC | EAEF  | 21.85% | https://drive.google.com/drive/folders/1eb0GwISb0AUULrDpUo8jBZC5Oh4zShgD?usp=share_link |

|Task          | Dataset | model | MAE   | Trained Model                                               |
| ------------- | ------- | ----- | ------ | ------------------------------------------------------------ |
| SOD    | VT5000 | EAEF  | 0.0031 |[https://drive.google.com/file/d/1mIcCKumip27fcSFnbvGIsycUvUTrYk36/view?usp=share_link](https://drive.google.com/drive/folders/1nxvFOOQN8a0U17hFGqcny8VgsGTPBBWz?usp=sharing) |



## Test
#### M3FD Detection
```
python EAEF_mmyolo/tools/test.py yolov5/bi_yolov5 
```
#### MFNet Segmentation
```
python EAEF_MF/run_own_pth.py
```
#### PST900 Segmentation
```
python EAEF_PST/run_own_pth.py
```
#### RGBT-CC Crow-counting
```
python EAEF_CC/test.py
```
#### VT821/VT1000/VT5000 SOD
```
python EAEF_SOD/test.py
```