Custom Training with YOLOv7 🔥

Some Important links

• Model Inference🤖
• 🚀Training Yolov7 on Kaggle
• Weight and Biases 🐝
• HuggingFace 🤗 Model Repo

Contact Information

• Name - Owais Ahmad
• Phone - +91-9515884381
• Email - owaiskhan9654@gmail.com
• Portfolio - https://owaiskhan9654.github.io/

Objective

To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.

Data Acquisition

The goal of this task is to train a model that can localize and classify each instance of Person and Car as accurately as possible.

• Link to the Downloadable Dataset

from IPython.display import Markdown, display

display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))

Custom Training with YOLOv7 🔥

In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format. To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:

• Export the dataset to YOLOv7
• Train YOLOv7 to recognize the objects in our dataset
• Evaluate our YOLOv7 model's performance
• Run test inference to view performance of YOLOv7 model at work

📦 YOLOv7

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/car-person-2.PNG" width=800>

Image Credit - jinfagang

Step 1: Install Requirements

!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
%cd yolov7
!pip install -qr requirements.txt
!pip install -q roboflow

Downloading YOLOV7 starting checkpoint

!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"

import os
import glob
import wandb
import torch
from roboflow import Roboflow
from kaggle_secrets import UserSecretsClient
from IPython.display import Image, clear_output, display # to display images



print(f"Setup complete. Using torch {torch._version_} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">

I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!

YOLOv7-Car-Person-Custom

try:
  user_secrets = UserSecretsClient()
  wandb_api_key = user_secrets.get_secret("wandb_api")
  wandb.login(key=wandb_api_key)
  anonymous = None
except:
  wandb.login(anonymous='must')
  print('To use your W&B account,
Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. 
Get your W&B access token from here: https://wandb.ai/authorize')
  
  
  
wandb.init(project="YOLOvR",name=f"7. YOLOv7-Car-Person-Custom-Run-7")

Step 2: Assemble Our Dataset

https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="">

In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.

In Roboflow, We can choose between two paths:

• Convert an existing Coco dataset to YOLOv7 format. In Roboflow it supports over 30 formats object detection formats for conversion.
• Uploading only these raw images and annotate them in Roboflow with Roboflow Annotate.

Version v2 Aug 12, 2022 Looks like this.

https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow.PNG" alt="">

user_secrets = UserSecretsClient()
roboflow_api_key = user_secrets.get_secret("roboflow_api")

rf = Roboflow(api_key=roboflow_api_key)
project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
dataset = project.version(2).download("yolov7")

Step 3: Training Custom pretrained YOLOv7 model

Here, I am able to pass a number of arguments: - img: define input image size - batch: determine

Robust Shelf Monitoring

We aim to build a Robust Shelf Monitoring system to help store keepers to maintain accurate inventory details, to re-stock items efficiently and on-time and to tackle the problem of misplaced items where an item is accidentally placed at a different location. Our product aims to serve as store manager that alerts the owner about items that needs re-stocking and misplaced items.

Training the model:

• Unzip the labelled dataset from kaggle and store it to your google drive.
• Follow the tutorial and update the training parameters in custom-yolov4-detector.cfg file in /darknet/cfg/ directory.
• filters = (number of classes + 5) * 3 for each yolo layer.
• max_batches = (number of classes) * 2000

Steps to run the prediction colab notebook:

1. Install the required dependencies; pymongo,dnspython.
2. Clone the darknet repository and the required python scripts.
3. Mount the google drive containing the weight file.
4. Copy the pre-trained weight file to the yolo content directory.
5. Run the detect.py script to peform the prediction. ## Presenting the predicted result. The detect.py script have option to send SMS notification to the shop keepers. We have built a front-end for building the phone-book for collecting the details of the shopkeepers. It also displays the latest prediction result and model accuracy.

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Project Overview:

The original goal was to use this model to monitor my rowing workouts and learn more about computer vision. To monitor the workouts, I needed the ability to identify the individual digits on the rowing machine. With the help of Roboflow's computer vision tools, such as assisted labeling, I was able to more quickly prepare, test, deploy and improve my YOLOv5 model. https://i.imgur.com/X1kHoEm.png" alt="Example Annotated Image from the Dataset">

https://i.imgur.com/uKRnFZc.png" alt="Inference on a Test Image using the rfWidget"> * How to Use the rfWidget

Roboflow's Upload API, which is suitable for uploading images, video, and annotations, worked great with a custom app I developed to modify the predictions from the deployed model, and export them in a format that could be uploaded to my workspace on Roboflow. * Uploading Annotations with the Upload API * Uploading Annotations with Roboflow's Python Package

What took me weeks to develop can now be done with the help of a single click utilize Roboflow Train, and the Upload API for Active Learning (dataset and model improvement). https://i.imgur.com/dsMo5VM.png" alt="Training Results - Roboflow FAST Model">

Dataset Classes:

• 1, 2, 3, 4, 5, 6, 7, 8, 9, 90 (class "90" is a stand-in for the digit, zero)

This dataset consits of 841 images. There are images from a different rowing machine and also from this repo. Some scenes are illuminated with sunlight. Others have been cropped to include only the LCD. Digits like 7, 8, and 9 are underrepresented.

For more information:

• A full tutorial for creating and deploying this model with YOLOv5: https://vision.philbrockman.com/

Real-Time Smoke and Fire Detection with YOLOv11

Overview

This project aims to develop a real-time smoke and fire detection system leveraging the power of YOLOv11, a state-of-the-art object detection model. By providing early and accurate detection of fire and smoke, this system enhances safety measures across various environments, helping to mitigate potential hazards and property damage.

Dataset

The dataset is a comprehensive, well-annotated collection of images containing instances of fire and smoke under diverse conditions. It is carefully curated to ensure robustness in model training, validation, and evaluation.

Classes

• Fire
• Smoke

Annotations

Each image is annotated with precise bounding boxes around instances of fire and smoke, enabling accurate localization and detection.

Dataset Distribution

• Training Set: 9,156 images
• Validation Set: 872 images
• Test Set: 435 images

Key Features

• Real-Time Detection: Processes video feeds in real-time, providing instant alerts upon detecting fire or smoke.
• High Accuracy: Utilizes YOLOv11’s advanced capabilities to achieve robust performance even in complex conditions.
• Customizable & Scalable: The model can be fine-tuned and adapted to various environments, making it ideal for applications in industrial safety, residential monitoring, and surveillance systems.

Usage

This dataset is designed for training and evaluating object detection models tailored for real-time fire and smoke detection. It is suitable for: - Surveillance systems (CCTV monitoring, smart security cameras) - Industrial safety applications (factories, warehouses, refineries) - Residential safety solutions (smart home fire detection) - Autonomous monitoring systems (drones, robotics, IoT devices)

Installation & Setup

Get started by cloning the dataset from Roboflow:

from roboflow import Roboflow
rf = Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("sayed-gamall").project("fire-smoke-detection-yolov11")
dataset = project.version(2).download("yolov11")

Get Started

1. Download the dataset using the above script.
2. Train your YOLOv11 model with the dataset.
3. Deploy the trained model for real-time fire and smoke detection.
4. Integrate alerts and notifications for automated safety monitoring.

This dataset provides a strong foundation for developing intelligent fire and smoke detection systems that can significantly improve safety and emergency response times.

🚀 Ready to Train?

Start building your real-time fire and smoke detection model today with Roboflow! 🔥