Facial Expression Recognition with PytTorch

[sreevidya-16]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Facial Expression Recognition with PytTorch
🔴 Aim : To develop a facial expression recognition system using PyTorch that classifies facial expressions into various categories using a convolutional neural network (CNN).

🔴 Dataset : The dataset used for this project is available on Kaggle. It contains images of faces categorized by different expressions such as happy, sad, angry, surprised, etc.
URL : https://www.kaggle.com/code/veb101/facial-expression-recognition-using-pytorch

🔴 Approach :

1. Exploratory Data Analysis (EDA): Perform EDA to understand the dataset, visualize the distribution of different expressions, and preprocess the data.
2. Model Development: Implement and compare 3-4 different algorithms/models to identify the best-performing model based on accuracy scores.
3. Model Evaluation: Evaluate the performance of each model using appropriate metrics and select the best-fit model.
4. Visualization: Provide visualizations of the results and model performance.

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✅ To be Mentioned while taking the issue :

• Full name : Patamsetti Sree Vidya
• GitHub Profile Link : https://github.com/sreevidya-16
• Email ID : [email protected]
• Approach for this Project :

1. Exploratory Data Analysis (EDA):

• Load and inspect the dataset.
• Visualize the distribution of facial expressions.
• Display sample images for each expression category.
• Clean the data if necessary.

2. Data Preprocessing:

• Resize images to a consistent size.
• Normalize pixel values.
• Apply data augmentation using Albumentations (rotations, flips, zooms).

3. Model Development:

• Model 1: Basic CNN with convolutional and pooling layers.
• Model 2: Fine-tune a pre-trained CNN (e.g., ResNet50) from the TIMM library.
• Model 3: Use another architecture (e.g., VGG16) and fine-tune.
• Compare models based on accuracy scores.

4. Model Evaluation:

• Evaluate each model using accuracy, precision, recall, and F1-score.
• Select the best-performing model.

5. Visualization:

• Visualize model performance and comparison.

• Provide conclusions and insights.

• What is your participant role? (Mention the Open Source program?
  I am Participating in GirlScript Summer of Code 2024 (GSSoC'24).

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Already present in the repository. Closing this issue as duplicate problem statement.