Dự án phát hiện biển báo giao thông với CenterNet cho dự án VIA.

This project classifies detects traffic signs, uses CenterNet with some modify.

Getting started

These instructions will give you a copy of the project and running on your local machine

Prerequisites

• conda

Installing

Please execute below commands to install essential packages.

Create a new conda environment with python3.7 initial.

    $ conda create --name via python=3.7
    $ conda activate via

Install torch and torchvision.

If you don’t have gpu.

    (via) $ conda install pytorch==1.7.1 torchvision==0.8.2 cpuonly -c pytorch

Or if you have gpu.
Please visit this and this to install appropriate version of cuda and cudnn, respectively.

Then, run below command.

    (via) $ conda install pytorch==1.7.1 torchvision==0.8.2 -c pytorch 

Install other essential packages.

    $ conda install --file requirements.txt

Dataset

Please visit this link and follow instructions to download via-trafficsign dataset. Extract and put it in the project root directory.

Train on your own dataset

See this

Directory structure

VIA-Intelligent-CenterNet
├── assets
|     ├── result.png
|
├── models                      # source code for model
|     ├── ...
|
├── readme
|     ├── ...
|
├── samples
|     ├── ...
|
├── via-trafficsign
|     ├── images
│     |     ├── train
│     |     |     ├── 00001.jpg
|     |     |     ├── ...
|     |     |     ├── 10292.jpg
│     |     ├── val
│     |     |     ├── 00001.jpg
|     |     |     ├── ...
|     |     |     ├── 00588.jpg
|     |
|     ├── labels
|     |     ├── train   
│     |     |     ├── 00001.txt
|     |     |     ├── ...
|     |     |     ├── 10292.txt
│     |     ├── val
│     |     |     ├── 00001.txt
|     |     |     ├── ...
|     |     |     ├── 00588.txt
|     
├── .gitignore
├── config.py
├── datasets.py
├── inference.py
├── README.md
├── requirements.txt
├── train.py
├── utils.py

Training

    $ python train.py --train-image-dir=$TRAIN_IMAGE_DIR \
                      --train-label-dir=$TRAIN_LABEL_DIR \
                      --val-image-dir=$VAL_IMAGE_DIR \
                      --val-label-dir=$VAL_LABEL_DIR

Example

    $ python train.py --train-image-dir='via-trafficsign/images/train' \
                      --train-label-dir='via-trafficsign/labels/train' \
                      --val-image-dir='via-trafficsign/images/val' \
                      --val-label-dir='via-trafficsign/labels/val'

Testing

    $ python inference.py --val-image-dir=$VAL_IMAGE_DIR \
                          --val-label-dir=$VAL_LABEL_DIR \
                          --checkpoint=$CHECKPOINT \
                          --outdir=$OUTDIR

Example

    $ python inference.py --val-image-dir='via-trafficsign/images/val' \
                          --val-label-dir='via-trafficsign/labels/val' \
                          --checkpoint='archives/centernet_vgg.ckpt' \
                          --outdir='result'

Demo

    $ python demo.py --image-path=$IMAGE_PATH \
                     --checkpoint=$CHECKPOINT \
                     --outpath=$OUTPATH

Example

    $ python demo.py --image-path='samples/1.jpg' \
                     --checkpoint='archives/centernet_vgg.ckpt' \
                     --outpath='result/1.jpg'

Pretrained model

Download pretrained-models and put it in archives/ directory.

Result