Iranian Cars Detection using Yolov5

Instalation

1- Clone this repository using the following command:

https://github.com/NahidEbrahimian/Cars-Detection-using-Yolov5.git

2- In ./Cars-Detection-using-Yolov5 directory, run the following command to install requirements:

!pip install -U -r requirements.txt

Dataset

Dataset contains 2100 images of the cars in five categories.

Datast link: iranians cars

../Dataset/images/im0.jpg  # image
../Dataset/labels/im0.txt  # label

Train

1- Clone this repository using the following command:

https://github.com/NahidEbrahimian/Cars-Detection-using-Yolov5.git

2- In ./Cars-Detection-using-Yolov5 directory, run the following command to install requirements:

!pip install -U -r requirements.txt

3- For training, in ./Cars-Detection-using-Yolov5 directory, train YOLOv5s on Dataset for 30 epochs using following command:

!python train.py --img 640 --batch 8 --epochs 30 --data data/coco128.yaml --weights yolov5s.pt

• For train on your dataset, you must creat dataset.yaml file.

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: ../Dataset  # dataset root dir
train: ../Dataset/Train/images  # train images (relative to 'path') 128 images
val: ../Dataset/Val/images  # val images (relative to 'path') 128 images

# Classes
nc: 5  # number of classes
names: ['iranKhodro_dena', 'kia_cerato', 'mazda_3', 'peugeot_206', 'saipa_saina']  # class names

You can change this sections in .data/coco128.yaml.

Inference

For inference, in ./Cars-Detection-using-Yolov5 directory, run the following command.

!python inference.py --weights runs/train/exp13/weights/last.pt --img 640 --conf 0.4 --source inputs/iranKhodro_dena26.jpg

Test

For test, in ./Cars-Detection-using-Yolov5 directory, run the following command. you must set your test data path in coco128.yaml file that prepared in Train step.

!python val.py --data coco128.yaml --weights runs/train/exp13/weights/last.pt --img 640

TensorRT and Pytorch models Comparision

1- Comparision inference time of Pytorch model and TensorRT_FP32

Model	Pytorch(ms)	TensorRT_FP32(ms)
Yolov5s	8.6	8.5