CarPartDetection: Detect Five Car Parts using YOLOv3

This repo is to detect car parts using the state-of-the-art YOLOv3 computer vision algorithm. For a short write up check out this @muehle/how-to-train-your-own-yolov3-detector-from-scratch-224d10e55de2">medium post.

This Project is to detect Five Parts of the car:

1. Light(Front and Back Light)
2. Glass(Front and Back Glass)
3. SideGlass
4. Door
5. Wheel

Checkout this inputs and outputs,

Pipeline Overview

To build and test your object detection algorithm follow the below steps:

1. Image Annotation
   • Install Microsoft’s Visual Object Tagging Tool (VoTT)
   • Annotate images
2. Training
   • Download pre-trained weights
   • Train your custom YOLO model on annotated images
3. Inference
   • Detect objects in new images and videos

Repo structure

• 1_Image_Annotation: Scripts and instructions on annotating images
• 2_Training: Scripts and instructions on training your YOLOv3 model
• 3_Inference: Scripts and instructions on testing your trained YOLO model on new images and videos
• Data: Input Data, Output Data, Model Weights and Results
• Utils: Utility scripts used by main scripts

Getting Started

NEW: Google Colab Tutorial

With Google Colab you can skip most of the set up steps and start training your own model right away.

Requisites

The only hard requirement is a running version of python 3.3 or newer. To install the latest python 3.x version go to

• python.org/downloads

and follow the installation instructions.

To speed up training, it is recommended to use a GPU with CUDA support. For example on AWS you can use a p2.xlarge instance (Tesla K80 GPU with 12GB memory). Inference is very fast even on a CPU with approximately ~2 images per second.

Installation

1a. Setting up Virtual Environment [Linux or Mac]

Clone this repo with:

git clone https://github.com/bhadreshpsavani/CarPartsDetectionChallenge
cd CarPartsDetectionChallenge/

Create Virtual (Linux/Mac) Environment (requires venv which is included in the standard library of Python 3.3 or newer):

python3 -m venv env
source env/bin/activate

Make sure that, from now on, you run all commands from within your virtual environment.

1b. Setting up Virtual Environment [Windows]

Use the Github Desktop GUI to clone this repo to your local machine. Navigate to the CarPartsDetectionChallenge project folder and open a power shell window by pressing Shift + Right Click and selecting Open PowerShell window here in the drop-down menu.

Create Virtual (Windows) Environment (requires venv which is included in the standard library of Python 3.3 or newer):

py -m venv env
.\env\Scripts\activate

Make sure that, from now on, you run all commands from within your virtual environment.

2. Install Required Packages [Windows, Mac or Linux]

Install all required packages with:

pip install -r requirements.txt

If this fails, you may have to upgrade your pip version first with pip install pip --upgrade. If your system has working CUDA drivers, it will use your GPU automatically for training and inference.

Quick Start (Inference only)

To test the cat face detector on test images located in CarPartsDetectionChallenge/Data/Source_Images/Test_Images run the Minimal_Example.py script in the root folder with:

python Minimal_Example.py

The outputs are saved in CarPartsDetectionChallenge/Data/Source_Images/Test_Image_Detection_Results. This includes:

• Cat pictures with bounding boxes around faces with confidence scores and

• Detection_Results.csv file with file names and locations of bounding boxes.

  If you want to detect car parts in your own pictures, replace the cat images in Data/Source_Images/Test_Images with your own images.

Full Start (Training and Inference)

To train your own custom YOLO object detector please follow the instructions detailed in the three numbered subfolders of this repo:

• 1_Image_Annotation,
• 2_Training and
• 3_Inference.

To make everything run smoothly it is highly recommended to keep the original folder structure of this repo!

Each *.py script has various command line options that help tweak performance and change things such as input and output directories. All scripts are initialized with good default values that help accomplish all tasks as long as the original folder structure is preserved. To learn more about available command line options of a python script <script_name.py> run:

python <script_name.py> -h

License

Unless explicitly stated otherwise at the top of a file, all code is licensed under the MIT license.
This repo is the fork of TrainYourOwnYOLO which is to train YOLOv3 algorithm cat detection. This repo makes use of ilmonteux/logohunter which itself is inspired by qqwweee/keras-yolo3.

Acknowledgements

I would like to thank Anton Muelemann for creating TrainYourOwnYOLO repository which really helped me to create this custom object detector.

Troubleshooting

1. If you encounter any error, please make sure you follow the instructions exactly (word by word). Once you are familiar with the code, you’re welcome to modify it as needed but in order to minimize error, I encourage you to not deviate from the instructions above.

2. If you are using pipenv and are having trouble running python3 -m venv env, try:

    pipenv shell

3. If you are having trouble getting cv2 to run, try:

    apt-get update
    apt-get install -y libsm6 libxext6 libxrender-dev
    pip install opencv-python

4. If you are a Linux user and having trouble installing *.snap package files try:

    snap install --dangerous vott-2.1.0-linux.snap

   See Snap Tutorial for more information.

Resources and Links:

• [AUG 23, 2021] -> If you have an object detection model, you can now use it with OpenAI’s zero shot object tracking repository to do object tracking - no additional modeling required.

Stay Up-to-Date

• ⭐ star this repo to get notifications on future improvements and
• 🍴 fork this repo if you like to use it as part of your own project.