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https://github.com/cutupdev/road-damage-detection
This is repository for detection damage and judgement on road.
https://github.com/cutupdev/road-damage-detection
image-processing python yolo
Last synced: about 2 months ago
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This is repository for detection damage and judgement on road.
- Host: GitHub
- URL: https://github.com/cutupdev/road-damage-detection
- Owner: cutupdev
- License: apache-2.0
- Created: 2024-02-20T07:00:13.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-03-11T08:40:03.000Z (10 months ago)
- Last Synced: 2024-10-31T23:34:32.801Z (3 months ago)
- Topics: image-processing, python, yolo
- Language: Python
- Homepage:
- Size: 9.28 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Road Damage Detection
-----
This repository contain codes for damage detection on the road.
In the city, it's important problem.
This project introduce in Oslo traffic center.
The solution is based on Deep CNN.
### 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:
- [train and validation sets](https://drive.google.com/file/d/1IHaqAxpMtFwPHia7msB_1QPAywPgg7fW/view?usp=sharing)
- [test1 and test2 data](https://drive.google.com/uc?id=1apjJfNHUlKQS64IaHRg3qRp_T0NnZnnQ&export=download)(no annotation).
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
```