# roadDamageDetection2020
**Repository Path**: chrischow23/roadDamageDetection2020
## Basic Information
- **Project Name**: roadDamageDetection2020
- **Description**: efficientDet网络代码，做扣件缺陷识别与检测
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 3
- **Forks**: 0
- **Created**: 2020-11-20
- **Last Updated**: 2024-03-01
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Road Crack Detection Challange 2020 - IEEE Bigdata Cup Challange
-----
This repository contain codes for paper ["An Efficient And Scalable Deep Learning Approach for Road Damage Detection"](https://arxiv.org/abs/2011.09577)
The solution is based on EfficientDet paper by Tan et al.
#### Model checkpoints
| Model | Input Image Resolution | #params | Inf Time (Image/ms) b=16 | AP | AP50 | AP75 | F1 |
|------------------------ |------------------------ |--------- |:-------------------------: |------ |------ |------ |------- |
| D0 [checkpoint](https://drive.google.com/file/d/1E6U21K_DxPOsRx2lKIDDb8MZ8Njjq-xh/view?usp=sharing) | 512 | 3.9M | 178 | 19.1 | 47.2 | 11.5 | 54.04 |
| D0-AUG [checkpoint](https://drive.google.com/file/d/1HFYDYkjkIo2NX42B6zEEceWDS6cyM1A-/view?usp=sharing) | 512 | 3.9M | 178 | 19.8 | 48.5 | 12.1 | 54.03 |
| D1 [checkpoint](https://drive.google.com/file/d/1HFYDYkjkIo2NX42B6zEEceWDS6cyM1A-/view?usp=sharing) | 640 | 6.5M | 147 | 21.7 | 51.5 | 13.4 | 56.9 |
| D1-AUG [checkpoint](https://drive.google.com/file/d/1fH-XqSn1RmY5Oi0lFoDKojzcs8xf5A0Q/view?usp=sharing) | 640 | 6.5M | 147 | 22.0 | 51.7 | 13.1 | 56.5 |
| D2 [checkpoint](https://drive.google.com/file/d/1b_-N8Txo7irYWxt4MWfVgs016jj2heil/view?usp=sharing) | 768 | 8M | 100 | 22.9 | 53.5 | 14.9 | 56.7 |
| D2-AUG [checkpoint](https://drive.google.com/file/d/1fw34x7w7cWumBZ6X6Z186rArANeGhxuw/view?usp=sharing) | 768 | 8M | 100 | 22.9 | 54.2 | 15.2 | 56.6 |
| D3 [checkpoint](https://drive.google.com/file/d/1_9gXbJzFgWuaF3v3-ONnH-vOhxiIQYAV/view?usp=sharing) | 796 | 11.9M | 57 | 23.0 | 53.4 | 15.0 | 56.5 |
| D3- AUG [checkpoint](https://drive.google.com/file/d/1VpH6HPFWNuiv88zT_cYOKSFM67A4PlpQ/view?usp=sharing) | 796 | 11.9M | 57 | 22.6 | 53.4 | 14.7 | 56.8 |
| D4 [checkpoint](https://drive.google.com/file/d/1LdGPqPPQRP8gj8ZcEtfYNP4rbBN1GqkK/view?usp=sharing) | 1024 | 20.5M | 38 | 22.8 | 53.3 | 15.1 | 57.2 |
| D7-AUG [checkpoint](https://drive.google.com/file/d/1wVNMeAMHyS85qJ1hsxLxnzFpVw_oT8Mz/view?usp=sharing) | 1536 | 51M | 10 | 23.4 | 53.6 | 15.0 | 56.5 |
### Usage
Consider unisng a workspace for cleaner
1. install required Libraries :
1.1.
- python>= 3.6
- pytorch 1.4 or 1.6
- torchvision >= 0.5
- apex is also needed
- timm >= 1.28
`pip install -r requirements.txt`
1.2.
install apex (2020-10-26):
```
git clone https://github.com/NVIDIA/apex
pip install -r apex/requirements.txt
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex
```
2. download Dataset :
for ease of use we have provided annotations and ... in coco format downloadable from this [link](https://drive.google.com/file/d/1IHaqAxpMtFwPHia7msB_1QPAywPgg7fW/view?usp=sharing).
otherwise one can download original data from sekilab github repo and convert using tools provided in utils folder.
3. for training :
- Train on single GPU :
```
python train.py ../data --model tf_efficientdet_d0 -b 40 --amp --lr .15 --sync-bn --opt fusedmomentum --warmup-epochs 3 --lr-noise 0.3 0.9 --model-ema --model-ema-decay 0.9998 -j 25 --epochs 300
```
- Distributed Training : (note you may need to make the file executable before training using `chmod +x distributed_train.sh`)
```
./distributed_train.sh 3 ../data --model tf_efficientdet_d0 -b 40 --amp --lr .15 --sync-bn --opt fusedmomentum --warmup-epochs 3 --lr-noise 0.3 0.9 --model-ema --model-ema-decay 0.9998 -j 25 --epochs 300
```
4. for inference on testset and generating submission file :
```
python infer.py ./data --model tf_efficientdet_d0 --checkpoint ./path/to/model/checkpoint --use-ema --anno test1 -b 17 --threshold 0.300
```
5. Image Inference to generate detected images
- first create image_info_annotations(e.g. if image folder is in `../data` path. One should first create image info in json format using `python utils/createimageinfo.py` then folder structure should be like )
```
..
├── data
│ └── annotations
| ├── image_info_test1.json
├── test1
│ ├── Japan_XXX.jpg
│ └── Czech_xxx.jpg
| └── ....
```
following command will create generated file with bounding boxes in ./predictions
```
python detector.py ../data --model tf_efficientdet_d0 --checkpoint path.to/modelfile.pth.tar --anno test1 -b 20 --use-ema --tosave ./predictions
```
for validation (AP scores) and benchmarking with `cuda.Event()` use the following command :
```
python validate.py ../data --model tf_efficientdet_d0 --checkpoint path/to/model/checkpoint.pth.tar --anno val -b 20 --use-ema
```
### Utils
Some utils are provided in utils folder such as tools to calculate anchor boxes ratis mean and std of train set and validation set and ... .
### cite
Please cite if you use paper or code : Paper is accepted at IEEE BigData 2020.
```
@misc{naddafsh2020efficient,
title={An Efficient and Scalable Deep Learning Approach for Road Damage Detection},
author={Sadra Naddaf-sh and M-Mahdi Naddaf-sh and Amir R. Kashani and Hassan Zargarzadeh},
year={2020},
eprint={2011.09577},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### other repos used :
- a pytorch implementation of efficientDet by rwightman
- timm pytorch model tools
- apex.
- effientDet paper
- k-means anchor calculator.