Vehicle Detection Using Deep Learning and YOLO Algorithm

Related tags

Deep Learning deep-learning image-processing object-detection fine-tuning yolov5
Overview

VehicleDetection

Vehicle Detection Using Deep Learning and YOLO Algorithm

a1

test_res_1

Dataset

take or find vehicle images for create a special dataset for fine-tuning.

Train : 70%

Validition : 20%

Test : 10%

dataset.yaml

config dataset.yaml for the address and information of your dataset.

path: Dataset/dataset-vehicles  # dataset root dir
train: images/train  # train images (relative to 'path')
val: images/val  # val images (relative to 'path')
test:  # test images (optional)

# Classes
nc: 5  # number of classes
names: [ 'Car', 'Motorcycle', 'Truck', 'Bus', 'Bicycle']  # class names

Clone Vehicle-Detection Repository

git clone https://github.com/MaryamBoneh/Vehicle-Detection
cd Vehicle-Detection
pip install -r requirements.txt

wandb

to have mAP, loss, confusion matrix, and other metrics, sign in www.wandb.ai.

pip install wandb

Train

fine-tuning on a pre-trained model of yolov5.

python train.py --img 640 --batch 16 --epochs 50 --data dataset.yaml --weights yolov5m.pt

Test

after train, gives you weights of train and you should use them for test.

python detect.py --weights runs/train/exp12/weights/best.pt --source test_images/imtest13.JPG

you can also use the weight file in path 'runs/train/exp12/weights/best.pt' without the train. this weight is the result of 128 epoch train on the following dataset.

My Vehicle Dataset

https://b2n.ir/vehicleDataset
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Comments
  • error on install

    error on install

    hi i can not install it

    Python 3.10.1 (tags/v3.10.1:2cd268a, Dec  6 2021, 19:10:37) [MSC v.1929 64 bit (AMD64)] on win32

     pip install -r requirements.txt
Collecting matplotlib>=3.2.2
  Using cached matplotlib-3.5.1-cp310-cp310-win_amd64.whl (7.2 MB)
Collecting numpy>=1.18.5
  Using cached numpy-1.22.1-cp310-cp310-win_amd64.whl (14.7 MB)
Collecting opencv-python>=4.1.2
  Using cached opencv_python-4.5.5.62-cp36-abi3-win_amd64.whl (35.4 MB)
Collecting Pillow>=8.0.0
  Using cached Pillow-9.0.0-cp310-cp310-win_amd64.whl (3.2 MB)
Collecting PyYAML>=5.3.1
  Using cached PyYAML-6.0-cp310-cp310-win_amd64.whl (151 kB)
Collecting scipy>=1.4.1
  Using cached scipy-1.7.3-cp310-cp310-win_amd64.whl (34.3 MB)
ERROR: Could not find a version that satisfies the requirement torch>=1.7.0 (from versions: none)
ERROR: No matching distribution found for torch>=1.7.0
    opened by parsibox 5
  • feature/yolox5x6

    feature/yolox5x6

    I had to use Yolov5 in a project of mine. This dataset gave better results than the Yolov5 in my case. So, took the x6 and trained in on this dataset. Attached is a drive link to the README.md since GitHub doesn't allow files larger than 100MB and LFS couldn't be used on a forked project.

    opened by BilawalAhmed0900 0
Owner
Maryam Boneh
B.Sc computer student at sadjad university - programmer - ui designer
Maryam Boneh
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