Develop a Model for predicting whether the person wearing sunglasses or not

[UppuluriKalyani]

Problem:
Detecting whether a person is wearing sunglasses or not in images is a common computer vision task with various practical applications. For example, in security systems, it can help identify individuals in surveillance footage or control access to secure areas. In retail, it can be used for personalized advertising or customer analytics. However, accurately distinguishing between sunglass-wearing and non-sunglass-wearing individuals can be challenging due to factors like varying lighting conditions, poses, and facial occlusions.

Solution:
To tackle this problem, we can leverage machine learning and computer vision techniques to build a robust model for sunglasses detection. Here's a step-by-step solution:

Data Collection:
Gather a diverse dataset of images containing individuals with and without sunglasses. Ensure the dataset covers a wide range of scenarios, including different lighting conditions, angles, and backgrounds.
Data Preprocessing:
Preprocess the images to standardize them for training. This may involve resizing the images to a consistent size, normalizing pixel values, and converting them to a suitable format for model input (e.g., grayscale or RGB).
Feature Extraction:
Extract relevant features from the images that can help distinguish between sunglass-wearing and non-sunglass-wearing individuals. This could involve traditional feature extraction techniques like Histogram of Oriented Gradients (HOG) or more advanced methods using deep learning.
Model Selection and Training:
Choose an appropriate machine learning model for classification tasks, such as convolutional neural networks (CNNs). CNNs are well-suited for image classification tasks due to their ability to automatically learn hierarchical features from data.
Split the dataset into training and testing sets and train the model using the training data. Use techniques like data augmentation to increase the diversity of the training dataset and prevent overfitting.
Model Evaluation:
Evaluate the trained model's performance using the testing data. Calculate metrics like accuracy, precision, recall, and F1-score to assess its effectiveness. Adjust the model architecture or training parameters as needed to improve performance.
Deployment:
Once satisfied with the model's performance, deploy it in real-world applications. This could involve integrating the model into existing systems, such as security cameras, mobile apps, or retail analytics platforms.
Continuous Improvement:
Monitor the model's performance over time and collect feedback from users. Update the model as needed with new data or fine-tune its parameters to maintain or improve its accuracy and reliability.

[github-actions]

Hello @UppuluriKalyani, Thank you for generating an issue to this project! Please wait while we get back to you.

[thesakshimittal]

Please assign this issue to me...I can solve this effectively using deep learning and other models

[thesakshimittal]

I have done some similar kind of problems earlier. Please assign this issue to me...I can solve this effectively using deep learning and other models.

[Gaurav-576]

I would like to contribute to this issue. Web Scrapping and using Data Augmentation are some of the techniques I would like to use to fulfill the end goals of this project. I am quite interested regarding CNN models and it would be a great opportunity for me if you let me contribute to this particular issue.

[KhushAgrawal001]

Hi there, I would like to solve this issue as a GSSoc'24 so please assign this issue to me as i will do my best and excell in fulfilling it.

[coldn00dles]

Greetings,
I can work on this issue and solve it by employing the Yolov8 model using a dataset tagged with images containing people wearing sunglasses. YOLO has been setting the industry standard for a very long time and the task can be accomplished using it. Requesting you to assign this issue to me @UppuluriKalyani

[UppuluriKalyani]

Greetings, I can work on this issue and solve it by employing the Yolov8 model using a dataset tagged with images containing people wearing sunglasses. YOLO has been setting the industry standard for a very long time and the task can be accomplished using it. Requesting you to assign this issue to me @UppuluriKalyani

Hey, as a contributor, I don't have the authority to assign the issue. Could you kindly request the project admin to Assign this issue.

[UppuluriKalyani]

@akshitagupta15june mam, please assign this issue to me.

[yusuf-khaan]

@akshitagupta15june Greetings ! , I would gladly like to work on this issue generated by @UppuluriKalyani if it is possible to assign multiple candidates , As we know 2 is always better than 1 . I have compatible experience and skills for this issue

Thanks

[Pranshu-jais]

approach : using Convolutional Neural Networks (CNNs) architectures like, ResNet, VGG, or MobileNet and fine-tune them for sunglasses detection along with preprocessing of images and evaluation of the model. I am a GSSOC 24 contributor and want this task to assign to me with the gssoc label and the appropriate level (1/2/3).

[thakurboy004]

Hi, I am a contributor in GSSoC'24 and I would like to contribute to this since I have hands-on experience in machine learning and deep learning, please assign this issue to me as I would be glad to contribute, and also add appropriate tags to this issue to properly define whether this is a level 1, level 2 or a level 3 issue

[AnoushkaIshi]

Please assign this issue to me since I've already done similar projects.

[AbhishekPalsra]

Assign this issue to me ! I'll provide the necessary solution .

[Diyaa0313]

Please assign this issue to me. I have some experience in making similar projects and will be able to solve the issue

[UppuluriKalyani]

@akshitagupta15june mam can you please assign this issue to me please.

[why-aditi]

I'd like to work on this issue as a SSOC'24 Contributor