Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/gyanbardhan/emotion-detection

Step into the realm of Emotion Detection Generalization! Uncover the power of deep learning as we decode human emotions from facial images. Explore our arsenal of fine-tuned CNN models and curated datasets, shaping a future where technology empathizes and connects on a deeper level. Revolutionize human-computer interaction with us today!
https://github.com/gyanbardhan/emotion-detection

ai artificial-intelligence cnn deep-learning emotion-detection image-processing visualization weights-and-biases

Last synced: 8 days ago
JSON representation

Step into the realm of Emotion Detection Generalization! Uncover the power of deep learning as we decode human emotions from facial images. Explore our arsenal of fine-tuned CNN models and curated datasets, shaping a future where technology empathizes and connects on a deeper level. Revolutionize human-computer interaction with us today!

Awesome Lists containing this project

README 

        
# Emotion Detection

## Description:

Welcome to the Emotion Detection Generalization repository! Here, we embark on a journey to enhance human-computer interaction and emotional intelligence through the power of deep learning. Our project is dedicated to creating a robust, accessible, and efficient solution for identifying emotions from facial images. With a focus on leveraging state-of-the-art convolutional neural network (CNN) architectures and transfer learning techniques, we aim to empower various applications, from virtual assistants to mental health support systems.



Inside this repository, you'll find a comprehensive array of resources meticulously crafted to support our mission:



## Notebooks:

Dive into our collection of Jupyter notebooks, each dedicated to implementing and experimenting with different CNN architectures for emotion detection. Whether it's a Custom CNN, VGG16, or ResNet, we've meticulously fine-tuned each model to capture subtle emotional nuances. Additionally, explore the notebook for the final model evaluation and comparison to witness our journey unfold.



### Notebooks-

- Custom CNN: https://www.kaggle.com/code/gyanbardhan/fer-2013-1

- VGG16: https://www.kaggle.com/code/gyanbardhan/fer-emotion-detection-vgg16

- ResNet: https://www.kaggle.com/code/clay108/resnet-emotion-detection-fer



## Datasets:

Explore the datasets that fuel our models' training and validation. Our curated dataset includes a diverse range of facial images annotated with corresponding emotions, meticulously prepared to enhance model performance and accuracy. Test the robustness of our models with unseen images provided in the test dataset.



- Dataset :- https://www.kaggle.com/datasets/msambare/fer2013/data



## Models:

Witness the culmination of our efforts in the form of trained model files ready for deployment and inference. Each model represents hours of fine-tuning and optimization to ensure top-notch performance in emotion detection.

-ResNet: https://github.com/Gyanbardhaniiitn/Emotion-Detection/blob/main/ResNet50%20(1).h5



## Web Application:

Experience the magic of our emotion detection system firsthand with our user-friendly web application. Upload facial images effortlessly and receive instant emotion predictions powered by our trained models.

- https://emotion-detection-dahzbnsabyd579zzop8xhd.streamlit.app/

  

## References:

Delve into the wealth of knowledge that informed our project's methodology and approach. Explore research papers, articles, and resources that shaped our understanding and guided our decisions.



## Important Links:

Access essential links to our deployed web application, notebooks, datasets, and trained models for seamless navigation and exploration.



### Join us in our quest to enhance emotional intelligence in technology and make a tangible difference in human-computer interaction. Together, let's cultivate a future where technology not only understands but empathizes with human emotions, fostering better connections and experiences.