snehitvaddi / yolov3-cloud-based-fire-detection Goto Github PK

A Project on Fire detection using YOLOv3 model. This repo consists of code used for training and detecting Fire using custom YoloV3 model. I trained my custom detector on existing yolov3 weights trained to detect 80 classes.
The Dataset is collected from google images using Download All Images chrome extension. Tool for Dataset labelling Label Img.
Find some readily labelled datasets are available here @Google's Open Image Dataset v5.

I have made Fire-Implementation.ipynb private for personal reasons, contact me @[email protected] for complete directory ✌

🧾 Colab Notebook	📂 Dataset with Labels	🔑 Trained Model Weights	✍ LabelImg
	Dataset with Labels	Download Weights	Label Img

• For executing Streamlit application, install streamlit using pip install streamlit --user.
• Open Command Prompt, and execute streamlit run UI.py.
• A Streamlit server gets started and opens up web UI in the default browser.

Input	Output

Here is the chart to describe how my performed during entire training process. It shows average loss vs. iterations. For a model to be 'accurate' you would aim for a loss under 2.

• Darknet repository
• Labeled Custom Dataset
• Custom .cfg file
• obj.data and obj.names files
• train.txt file (test.txt is optional here as well)

I referenced this tutorial from an YouTube Video by TheAIGuy channel. You can follow a step-by-step walkthrough of video and the code here: https://www.youtube.com/watch?v=10joRJt39Ns

You can download the yolov3 pretrained weights by clicking here and yolov3-tiny here

If you are a student like me, and unable to pay such amount, here is a jugad for you. 😉

👉Step 1: In colab notebook, type CTRL + SHIFT + I (Inspect element)
👉Step 2: Go to the console tab and paste the code given in the image below.

function ClickConnect(){
console.log("Working");
document.querySelector("colab-toolbar-button#connect").click()
}
setInterval(ClickConnect,60000)

• Integrate the model with IOT and leverage Cloud services for real-time monitoring and alerting system.

Streamlit Yolo Deployment - https://srishti.hashnode.dev/object-detection-app-using-yolov3-opencv-and-streamlit-1
YoloV3 Custom Model taining - https://www.youtube.com/channel/UCrydcKaojc44XnuXrfhlV8Q

I'm facing bugs with uploading images through Streamlit and Displaying them using OpenCV. Any kind of suggestions will be appriciable.