https://github.com/lddl/road-anomaly-detection-train
Training object detector for road accidents detection
https://github.com/lddl/road-anomaly-detection-train
computer-vision neural-network object-detection yolov8
Last synced: over 1 year ago
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Training object detector for road accidents detection
- Host: GitHub
- URL: https://github.com/lddl/road-anomaly-detection-train
- Owner: LdDl
- Created: 2024-06-22T19:14:41.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-06-26T10:08:44.000Z (about 2 years ago)
- Last Synced: 2025-01-21T18:49:13.294Z (over 1 year ago)
- Topics: computer-vision, neural-network, object-detection, yolov8
- Language: Python
- Homepage: https://github.com/LdDl/road-anomaly-detection
- Size: 24.4 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Training object detector for road accidents detection
## Table of Contents
- [About](#about)
- [Pretrained models](#pretrained-models)
- [Usage](#usage)
- [References](#references)
## About
It is just bunch of scripts to train road accidents detector for my pet-project written in Rust programming languages: https://github.com/LdDl/road-anomaly-detection
There are two scripts in this repository:
- `download.py` to download dataset of interest;
- `train.py` to run training process; (w.i.p)
## Pretrained models
If you want just to download pretrained models here are links:
- YOLOv8 nano - [Best weights (ONNX)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_nano.onnx), [Best weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_nano.pt) [Last weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/last_nano.pt)
Parameters:
- Image size: 608x608
- Batch size: 16
- Epochs: 300
- Cache images: yes
Training results (images, CSV)
Training and validation batches examples are [here](runs/detect/nano/)
| 
|
|:--:|
| Results . Corresponding CSV is [here](runs/detect/nano/results.csv) |
| 
|
|:--:|
| *Confusion matrix* |
| 
|
|:--:|
| *Normalized confusion matrix* |
| 
|
|:--:|
| *F1 Curve* |
| 
|
|:--:|
| *P Curve* |
| 
|
|:--:|
| *R Curve* |
| 
|
|:--:|
| *PR Curve* |
| 
|
|:--:|
| *Labels* |
| 
|
|:--:|
| *Labels correlogram* |
- YOLOv8 small - [Best weights (ONNX)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_small.onnx), [Best weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_small.pt) [Last weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/last_small.pt)
Parameters:
- Image size: 608x608
- Batch size: 16
- Epochs: 300
- Cache images: yes
Training results (images, CSV)
Training and validation batches examples are [here](runs/detect/small/)
| 
|
|:--:|
| Results . Corresponding CSV is [here](runs/detect/small/results.csv) |
| 
|
|:--:|
| *Confusion matrix* |
| 
|
|:--:|
| *Normalized confusion matrix* |
| 
|
|:--:|
| *F1 Curve* |
| 
|
|:--:|
| *P Curve* |
| 
|
|:--:|
| *R Curve* |
| 
|
|:--:|
| *PR Curve* |
| 
|
|:--:|
| *Labels* |
| 
|
|:--:|
| *Labels correlogram* |
- YOLOv8 medium - @todo train
- YOLOv8 large - @todo train
- YOLOv8 extra large - [Best weights (ONNX)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_extra_large.onnx), [Best weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/best_extra_large.pt) [Last weights (Pytorch)](https://github.com/LdDl/road-anomaly-detection-train/releases/download/v0.2.0/last_extra_large.pt)
Parameters:
- Image size: 608x608
- Batch size: 16
- Epochs: 300
- Cache images: yes
Training results (images, CSV)
Training and validation batches examples are [here](runs/detect/extra_large/)
| 
|
|:--:|
| Results . Corresponding CSV is [here](runs/detect/extra_large/results.csv) |
| 
|
|:--:|
| *Confusion matrix* |
| 
|
|:--:|
| *Normalized confusion matrix* |
| 
|
|:--:|
| *F1 Curve* |
| 
|
|:--:|
| *P Curve* |
| 
|
|:--:|
| *R Curve* |
| 
|
|:--:|
| *PR Curve* |
| 
|
|:--:|
| *Labels* |
| 
|
|:--:|
| *Labels correlogram* |
## Usage
* Clone the repository and navigate to root folder:
```shell
git clone https://github.com/LdDl/road-anomaly-detection-train.git
cd road-anomaly-detection-train
```
* Install dependencies
```shell
pip3 install -r requirements.txt
```
* Navigate to selected dataset. In this case the link is:
```
https://universe.roboflow.com/accident-detection-ffdrf/accident-detection-8dvh5
```
Click `Download` button:
* Navigate to `Terminal` tab and get dataset ID and unique key to download it.
* Run `download.py` script
```shell
export DATASET_ID=YOUR-DATASET-ID
export ROBOFLOW_KEY=YOUR-ACCOUNT-KEY
python3 download.py --dataset_id $DATASET_ID --key $ROBOFLOW_KEY --output dataset.zip
```
You can adjust classes if you need to in lines [119](download.py#L119) and [124](download.py#L124):
- Undefined classes would be marked as (max class ID + 1).
- Warning: Re-labeled annotations would be stored in `/train/labels`, `/test/labels` and `/valid/labels`. Source labels would be stored in `/train/labels_source`, `/test/labels_source` and `/valid/labels_source` respectively.
* Run `train.py` script
```shell
python3 train.py --cache_images t --model_size n --image_size 608 --yaml_path extracted_dataset --batch_size 16 --epochs 300
```
When training is done you can extract both ONNX and Pytorch weights from `run` directory which would be generated during training process.
## References
* Developers of YOLOv8 - https://github.com/ultralytics/ultralytics. If you are aware of some original papers for YOLOv8 architecture, please contact me to mention it in this README.
* Dataset source https://universe.roboflow.com/accident-detection-ffdrf/accident-detection-8dvh5