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+# HybridNets: End2End Perception Network
+
+
+<div align="center">
+ 
+![logo](images/hybridnets.jpg)  
+**HybridNets Network Architecture.**  
+
+[![Generic badge](https://img.shields.io/badge/License-MIT-<COLOR>.svg?style=for-the-badge)](https://github.com/datvuthanh/HybridNets/blob/main/LICENSE) 
+[![PyTorch - Version](https://img.shields.io/badge/PYTORCH-1.10+-red?style=for-the-badge&logo=pytorch)](https://pytorch.org/get-started/locally/) 
+[![Python - Version](https://img.shields.io/badge/PYTHON-3.7+-red?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/downloads/)
+<br>
+<!-- [![Contributors][contributors-shield]][contributors-url]
+[![Forks][forks-shield]][forks-url]
+[![Stargazers][stars-shield]][stars-url]
+[![Issues][issues-shield]][issues-url] -->
+ 
+</div>
+
+> [**HybridNets: End-to-End Perception Network**](https://arxiv.org/abs/2203.09035)
+>
+> by Dat Vu, Bao Ngo, [Hung Phan](https://scholar.google.com/citations?user=V3paQH8AAAAJ&hl=vi&oi=ao)<sup> :email:</sup>     [*FPT University*](https://uni.fpt.edu.vn/en-US/Default.aspx)
+>
+>  (<sup>:email:</sup>) corresponding author.
+>
+> *arXiv technical report ([arXiv 2203.09035](https://arxiv.org/abs/2203.09035))*
+    
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/hybridnets-end-to-end-perception-network-1/traffic-object-detection-on-bdd100k)](https://paperswithcode.com/sota/traffic-object-detection-on-bdd100k?p=hybridnets-end-to-end-perception-network-1)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/hybridnets-end-to-end-perception-network-1/lane-detection-on-bdd100k)](https://paperswithcode.com/sota/lane-detection-on-bdd100k?p=hybridnets-end-to-end-perception-network-1)
+
+<!-- TABLE OF CONTENTS -->
+<details>
+  <summary>Table of Contents</summary>
+  <ol>
+    <li>
+      <a href="#about-the-project">About The Project</a>
+      <ul>
+        <li><a href="#project-structure">Project Structure</a></li>
+      </ul>
+    </li>
+    <li>
+      <a href="#getting-started">Getting Started</a>
+      <ul>
+        <li><a href="#installation">Installation</a></li>
+        <li><a href="#demo">Demo</a></li>
+      </ul>
+    </li>
+    <li>
+      <a href="#usage">Usage</a>
+      <ul>
+        <li><a href="#data-preparation">Data Preparation</a></li>
+        <li><a href="#training">Training</a></li>
+      </ul>
+    </li>
+    <li><a href="#training-tips">Training Tips</a></li>
+    <li><a href="#results">Results</a></li>
+    <li><a href="#license">License</a></li>
+    <li><a href="#acknowledgements">Acknowledgements</a></li>
+    <li><a href="#citation">Citation</a></li>
+  </ol>
+</details>
+
+
+## About The Project
+<!-- #### <div align=center> **HybridNets** = **real-time** :stopwatch: * **state-of-the-art** :1st_place_medal: * (traffic object detection + drivable area segmentation + lane line detection) :motorway: </div> -->
+HybridNets is an end2end perception network for multi-tasks. Our work focused on traffic object detection, drivable area segmentation and lane detection.  HybridNets can run real-time on embedded systems, and obtains SOTA Object Detection, Lane Detection on BDD100K Dataset.
+![intro](images/intro.jpg)  
+
+### Project Structure
+```bash
+HybridNets
+│   backbone.py                   # Model configuration
+│   hubconf.py                    # Pytorch Hub entrypoint
+│   hybridnets_test.py            # Image inference
+│   hybridnets_test_videos.py     # Video inference
+│   train.py                      # Train script
+│   val.py                        # Validate script
+│
+├───encoders                      # https://github.com/qubvel/segmentation_models.pytorch/tree/master/segmentation_models_pytorch/encoders
+│       ...
+│
+├───hybridnets
+│       autoanchor.py             # Generate new anchors by k-means
+│       dataset.py                # BDD100K dataset
+│       loss.py                   # Focal, tversky (dice)
+│       model.py                  # Model blocks
+│
+├───projects
+│       bdd100k.yml               # Project configuration
+│
+└───utils
+    │   plot.py                   # Draw bounding box
+    │   smp_metrics.py            # https://github.com/qubvel/segmentation_models.pytorch/blob/master/segmentation_models_pytorch/metrics/functional.py
+    │   utils.py                  # Various helper functions (preprocess, postprocess, eval...)
+    │
+    └───sync_batchnorm            # https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/tree/master/sync_batchnorm 
+            ...
+```
+
+## Getting Started [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Uc1ZPoPeh-lAhPQ1CloiVUsOIRAVOGWA?usp=sharing)
+### Installation
+The project was developed with [**Python>=3.7**](https://www.python.org/downloads/) and [**Pytorch>=1.10**](https://pytorch.org/get-started/locally/).
+```bash
+git clone https://github.com/datvuthanh/HybridNets
+cd HybridNets
+pip install -r requirements.txt
+```
+ 
+### Demo
+```bash
+# Download end-to-end weights
+mkdir weights
+curl -L -o weights/hybridnets.pth https://github.com/datvuthanh/HybridNets/releases/download/v1.0/hybridnets.pth
+
+# Image inference
+python hybridnets_test.py -w weights/hybridnets.pth --source demo/image --output demo_result --imshow False --imwrite True
+
+# Video inference
+python hybridnets_test_videos.py -w weights/hybridnets.pth --source demo/video --output demo_result
+
+# Result is saved in a new folder called demo_result
+```
+
+## Usage
+### Data Preparation
+Recommended dataset structure:
+```bash
+HybridNets
+└───datasets
+    ├───imgs
+    │   ├───train
+    │   └───val
+    ├───det_annot
+    │   ├───train
+    │   └───val
+    ├───da_seg_annot
+    │   ├───train
+    │   └───val
+    └───ll_seg_annot
+        ├───train
+        └───val
+```
+Update your dataset paths in `projects/your_project_name.yml`.
+
+For BDD100K: [imgs](https://bdd-data.berkeley.edu/), [det_annot](https://drive.google.com/file/d/19CEnZzgLXNNYh1wCvUlNi8UfiBkxVRH0/view), [da_seg_annot](https://drive.google.com/file/d/1NZM-xqJJYZ3bADgLCdrFOa5Vlen3JlkZ/view), [ll_seg_annot](https://drive.google.com/file/d/1o-XpIvHJq0TVUrwlwiMGzwP1CtFsfQ6t/view)
+
+### Training
+#### 1) Edit or create a new project configuration, using bdd100k.yml as a template
+```python
+# mean and std of dataset in RGB order
+mean: [0.485, 0.456, 0.406]
+std: [0.229, 0.224, 0.225]
+
+# bdd100k anchors
+anchors_scales: '[2**0, 2**0.70, 2**1.32]'
+anchors_ratios: '[(0.62, 1.58), (1.0, 1.0), (1.58, 0.62)]'
+
+# must match your dataset's category_id.
+# category_id is one_indexed,
+# for example, index of 'car' here is 0, while category_id is 1
+obj_list: ['car']
+
+seg_list: ['road',
+          'lane']
+
+dataset:
+  color_rgb: false
+  dataroot: path/to/imgs
+  labelroot: path/to/det_annot
+  laneroot: path/to/ll_seg_annot
+  maskroot: path/to/da_seg_annot
+...
+```
+
+#### 2) Train
+```bash
+python train.py -p bdd100k        # your_project_name
+                -c 3              # coefficient of effnet backbone, result from paper is 3
+                -n 4              # num_workers
+                -b 8              # batch_size per gpu
+                -w path/to/weight # use 'last' to resume training from previous session
+                --freeze_det      # freeze detection head, others: --freeze_backbone, --freeze_seg
+                --lr 1e-5         # learning rate
+                --optim adamw     # adamw | sgd
+                --num_epochs 200
+```
+Please check `python train.py --help` for every available arguments.
+
+#### 3) Evaluate
+```bash
+python val.py -p bdd100k -c 3 -w checkpoints/weight.pth
+```
+
+## Training Tips
+### Anchors :anchor:
+If your dataset is intrinsically different from COCO or BDD100K, or the metrics of detection after training are not as high as expected, you could try enabling autoanchor in `project.yml`:
+```python
+...
+model:
+  image_size:
+  - 640
+  - 384
+need_autoanchor: true  # set to true to run autoanchor
+pin_memory: false
+...
+```
+This automatically finds the best combination of anchor scales and anchor ratios for your dataset. Then you can manually edit them `project.yml` and disable autoanchor.
+ 
+If you're feeling lucky, maybe mess around with base_anchor_scale in `backbone.py`:
+```python
+class HybridNetsBackbone(nn.Module):
+  ...
+  self.pyramid_levels = [5, 5, 5, 5, 5, 5, 5, 5, 6]
+  self.anchor_scale = [1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,]
+  self.aspect_ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])
+  ...
+```
+and `model.py`:
+```python
+class Anchors(nn.Module):
+  ...
+  for scale, ratio in itertools.product(self.scales, self.ratios):
+    base_anchor_size = self.anchor_scale * stride * scale
+    anchor_size_x_2 = base_anchor_size * ratio[0] / 2.0
+    anchor_size_y_2 = base_anchor_size * ratio[1] / 2.0
+  ...
+```
+to get a grasp on how anchor boxes work.
+ 
+And because a picture is worth a thousand words, you can visualize your anchor boxes in [Anchor Computation Tool](https://github.com/Cli98/anchor_computation_tool).
+### Training stages
+We experimented with training stages and found that this settings achieved the best results:
+ 
+1. `--freeze_seg True` ~ 100 epochs
+2. `--freeze_backbone True --freeze_det True` ~ 50 epochs
+3. Train end-to-end ~ 50 epochs
+
+ The reason being detection head is harder to converge early on, so we basically skipped segmentation head to focus on detection first.
+ 
+## Results
+### Traffic Object Detection
+ 
+<table>
+<tr><th>Result </th><th>Visualization</th></tr>
+<tr><td>
+
+|        Model       |  Recall (%)  |   [email protected] (%)   |
+|:------------------:|:------------:|:---------------:|
+|     `MultiNet`     |     81.3     |       60.2      |
+|      `DLT-Net`     |     89.4     |       68.4      |
+|   `Faster R-CNN`   |     77.2     |       55.6      |
+|      `YOLOv5s`     |     86.8     |       77.2      |
+|       `YOLOP`      |     89.2     |       76.5      |
+|  **`HybridNets`**  |   **92.8**   |     **77.3**    |
+
+</td><td>
+
+<img src="images/det1.jpg" width="50%" /><img src="images/det2.jpg" width="50%" />
+
+</td></tr> </table>
+ 
+<!--
+|        Model       |  Recall (%)  |   [email protected] (%)   |
+|:------------------:|:------------:|:---------------:|
+|     `MultiNet`     |     81.3     |       60.2      |
+|      `DLT-Net`     |     89.4     |       68.4      |
+|   `Faster R-CNN`   |     77.2     |       55.6      |
+|      `YOLOv5s`     |     86.8     |       77.2      |
+|       `YOLOP`      |     89.2     |       76.5      |
+|  **`HybridNets`**  |   **92.8**   |     **77.3**    |
+
+<p align="middle">
+ <img src="images/det1.jpg" width="49%" />
+ <img src="images/det2.jpg" width="49%" /> 
+</p>
+
+-->
+ 
+### Drivable Area Segmentation
+ 
+<table>
+<tr><th>Result </th><th>Visualization</th></tr>
+<tr><td>
+
+|       Model      | Drivable mIoU (%) |
+|:----------------:|:-----------------:|
+|    `MultiNet`    |        71.6       |
+|     `DLT-Net`    |        71.3       |
+|     `PSPNet`     |        89.6       |
+|      `YOLOP`     |        91.5       |
+| **`HybridNets`** |      **90.5**     |
+
+</td><td>
+
+<img src="images/road1.jpg" width="50%" /><img src="images/road2.jpg" width="50%" />
+
+</td></tr> </table>
+ 
+<!--
+|       Model      | Drivable mIoU (%) |
+|:----------------:|:-----------------:|
+|    `MultiNet`    |        71.6       |
+|     `DLT-Net`    |        71.3       |
+|     `PSPNet`     |        89.6       |
+|      `YOLOP`     |        91.5       |
+| **`HybridNets`** |      **90.5**     |
+<p align="middle">
+ <img src="images/road1.jpg" width="49%" />
+ <img src="images/road2.jpg" width="49%" /> 
+</p>
+-->
+ 
+### Lane Line Detection
+ 
+<table>
+<tr><th>Result </th><th>Visualization</th></tr>
+<tr><td>
+
+|      Model       | Accuracy (%) | Lane Line IoU (%) |
+|:----------------:|:------------:|:-----------------:|
+|      `Enet`      |     34.12    |       14.64       |
+|      `SCNN`      |     35.79    |       15.84       |
+|    `Enet-SAD`    |     36.56    |       16.02       |
+|      `YOLOP`     |     70.5     |        26.2       |
+| **`HybridNets`** |   **85.4**   |      **31.6**     |
+
+</td><td>
+
+<img src="images/lane1.jpg" width="50%" /><img src="images/lane2.jpg" width="50%" />
+
+</td></tr> </table>
+ 
+<!--
+|      Model       | Accuracy (%) | Lane Line IoU (%) |
+|:----------------:|:------------:|:-----------------:|
+|      `Enet`      |     34.12    |       14.64       |
+|      `SCNN`      |     35.79    |       15.84       |
+|    `Enet-SAD`    |     36.56    |       16.02       |
+|      `YOLOP`     |     70.5     |        26.2       |
+| **`HybridNets`** |   **85.4**   |      **31.6**     |
+ 
+<p align="middle">
+ <img src="images/lane1.jpg" width="49%" />
+ <img src="images/lane2.jpg" width="49%" /> 
+</p>
+-->
+<div align="center">
+ 
+ ![](images/full_video.gif)
+ 
+ [Original footage](https://www.youtube.com/watch?v=lx4yA1LEi9c) courtesy of [Hanoi Life](https://www.youtube.com/channel/UChT1Cpf_URepCpsdIqjsDHQ)
+ 
+ </div>
+ 
+## License
+
+Distributed under the MIT License. See `LICENSE` for more information.
+ 
+## Acknowledgements
+
+Our work would not be complete without the wonderful work of the following authors:
+
+* [EfficientDet](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch)
+* [YOLOv5](https://github.com/ultralytics/yolov5)
+* [YOLOP](https://github.com/hustvl/YOLOP)
+* [KMeans Anchors Ratios](https://github.com/mnslarcher/kmeans-anchors-ratios)
+* [Anchor Computation Tool](https://github.com/Cli98/anchor_computation_tool)
+
+## Citation
+
+If you find our paper and code useful for your research, please consider giving a star :star:   and citation :pencil: :
+
+```BibTeX
+@misc{vu2022hybridnets,
+      title={HybridNets: End-to-End Perception Network}, 
+      author={Dat Vu and Bao Ngo and Hung Phan},
+      year={2022},
+      eprint={2203.09035},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+ 
+<!-- MARKDOWN LINKS & IMAGES -->
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