🐕 Stray Dogs Detection System 🐕

I. Introduction

Motivation

• High population of stray dogs in Karachi leading to safety concerns and conflicts between humans and stray dogs.

Objective

• Develop a stray dog detection model to identify and locate stray dogs across the city.

Solution

• Create a system using AI and object detection to detect stray dogs and provide their location to authorities.

Benefits

• Efficiently capture stray dogs, enabling authorities to sterilize and vaccinate them, reducing fear and promoting harmony between humans and stray dogs.

II. The Dataset

• Find the raw data at Drive-link
• Find the pre-processed dataset at Drive-link

III. Model Training

• Download the pre-processed dataset from the drive link

• Upload it on Colab, or your own Drive.

• If you uploaded it on Drive, then mount your Google Drive in your Colab file, else just upload the dataset's zip file on Colab (it might take some time).

• Unzip the file using:
  

   !unzip <path_to_dataset.zip_file>

• Clone Yolov5's GitHub, cd to yolov5 and install the requirements using:
  

  !git clone https://github.com/ultralytics/yolov5
  %cd yolov5
  %pip install -r requirements.txt

• Upload yolo5s.pt and custom_dataset.yaml on your Colab

• Train your own model using the command:
  

  !python train.py --img 640 --cfg /content/yolov5/models/yolov5m.yaml --hyp /content/yolov5/data/hyps/hyp.scratch-med.yaml --batch 32 --epochs 50 --data /content/custom_dataset.yaml --weights /content/yolov5s.pt  --workers 24 

  I trained for 100 epochs initially but 90 epochs gave the best result.

• Save the trained model. You can find it at /contents/yolov5/runs/train/exp/weights/best.pt

• The trained model is the best.pt file in this repo, but you can also find it at Drive-link

• Apply object detection on a video/image file using:

  !python detect.py --img 640 --source <path_to_source_file> --conf 0.5 --weights /content/yolov5/runs/train/exp/weights/best.pt

Reminder: you can just use some other dataset, or change the parameters as per your preference to train your own custom object detection model :))

IV. Model Deployment Setup

• This project might only work on your system if you have a GPU (else it will only work for image files and not the video ones)
• You can replace the best.pt file with your own custom object detection model.
• cmd:

   cd <path_to_your_fav_directory>

  git clone https://github.com/tselane2110/stray-dogs-detection-system

  cd <path_to_the_cloned_repository>

• create 4 folders in static\ :
  • images
  • predicted_images
  • videos
  • predicted_videos
• create a virtual environment by executing the following command in your cmd

  python -m venv virtual

• activate the virtual environment by executing the following command in your cmd

  virtual\Scripts\Activate.ps1

• install the required libaries by executing the following command in your cmd

  pip install -r requirements.txt

• run the following command in your cmd

  python main-app.py

• go to 192.168.1.101:5000 on your browser
• choose a file then upload it
• that's it for now, have fun!

Note: This is an ongoing project, so Ill be updating it from time to time
Also, if you encounter any issue, kindly do let me know so I can fix it!