Cobra

KO | EN

gitlite — search

#javascript #typescript #go #golang #android #docker #nodejs #windows #kubernetes #awesome-list #pytorch #security #mcp #openai

(★ 9)

[Pattern Recognition] Official Pytorch Code for CoBra 🐍 : Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation

Cobra Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# CoBra 🐍 : Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation

[[Project Page]](https://anno9555.github.io/)

![voc](https://raw.githubusercontent.com/MICV-yonsei/Cobra/main/img/fig1.png)

We propose **Complementary Branch (CoBra)**, a novel dual branch framework consisting of two distinct architectures which provide valuable complementary knowledge of class (from CNN) and semantic (from vision transformer) to each branch. In particular, we learn **Class-Aware Projection (CAP)** for the CNN branch and **Semantic-Aware Projection (SAP)** for the vision transformer branch to explicitly fuse their complementary knowledge and facilitate a new type of extra patch-level supervision. Extensive experiments qualitatively and quantitatively investigate how CNN and vision transformer complement each other on the PASCAL VOC 2012 dataset showing a state-of-the-art WSSS result.

## :book: Contents

   * [Prerequisite](#Prerequisite)
   * [Usage](#Usage)
      * [Pretrained Weight](#Pretrained-Weight)
   * [About CoBra](#About-CoBra)

## 🔧 Prerequisite
- Download [PASCAL VOC2012 devkit](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/)
- Ubuntu 20.04, with Python 3.10 and the following python dependencies.
```bash
pip install -r requirements.txt
```

## 💻 Usage
**Step1:**
- Run the run.sh script for training Cobra, it makes **Seed** and elements for making better Mask.
- We train [IRNet](https://github.com/jiwoon-ahn/irn) to generate mask to refine Seed.

- change ```title``` and ```pascal_dataset_path``` in run shell script.
```bash
bash run.sh
```

### 🏋️ Pretrained Weight
<table style="margin: auto">
  <tr>
    <td align="center">CAK Branch</td>
    <td align="center"><i>ep19_cnn_checkpoint.pth</i></td>
    <td><a href="https://drive.google.com/file/d/1X0kn_imyesfKlguBWqoysar5_4RWMFZ1/view?usp=sharing](https://drive.google.com/drive/folders/1ZUCTrz7J4eCUrMTLgbaqHEJqqW_ZYamy?usp=sharing">link</td>
  </tr>
  <tr>
    <td align="center">SAK Branch</td>
    <td align="center"><i>ep19_tran_checkpoint.pth</i></td>
    <td><a href="https://drive.google.com/file/d/1GAEO-Qta_iUnR1ptZL7z5ZTiCuTA9QWx/view?usp=sharing](https://drive.google.com/drive/folders/1ZUCTrz7J4eCUrMTLgbaqHEJqqW_ZYamy?usp=sharing">link</td>
  </tr>
</table>

**Step2:** For the Segmentation part, we used DeepLabV2 with resnet101 backbone and MiT-B2 backbone.
- DeepLabV2 (https://github.com/kazuto1011/deeplab-pytorch)

## 🐍 About CoBra
<center><img src="./img/main.png" width="700px" height="700px" title="Github_Logo"/>
</center>

### Results
- Qualitative Results on Pascal VOC 2012 dataset.
![voc](https://raw.githubusercontent.com/MICV-yonsei/Cobra/main/img/VOC_results.png)

- Qualitative Results on MS-COCO 2014 dataset.
![coco](https://raw.githubusercontent.com/MICV-yonsei/Cobra/main/img/COCO_results.png)

## :scroll: Acknowledgement
This repository has been developed based on the [IRNet](https://github.com/jiwoon-ahn/irn) repository. Thanks for the good work!

Cobra

Cobra Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Cobra

Cobra Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README