A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

Deep SAD model with customized datasets. Source: https://github.com/lukasruff/Deep-SAD-PyTorch

This project is a deep learning-based facial expression recognition system implemented using PyTorch. It can classify facial expressions into seven different emotions: angry, disgust, fear, happy, neutral, sad, and surprise.

A PyTorch deep learning model and an iOS app in Swift for facial expression detection. It employs a CoreML model to identify expressions like Anger, Disgust, Fear, Happiness, Sadness, Surprise, Neutral, and Contempt in real time, providing instant notifications based on detected emotions.

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A implement of DeepSAD algorithm with customized datasets. Source code is from https://github.com/lukasruff/Deep-SAD-PyTorch.

Our deep learning model can predict your puppy's mood just my looking at the image. Puppy's can have 3 different moods - Sad, Relaxed, Happy. We have used Pytorch, Cuda framework, Python, Deep Learning Neural Networks.

A Deep Learning Recurrent Neural Network by PyTorch to identify the different emotions in the text, feedback & paragraph by training the model to predict accurately, the emotions of anger, fear, joy, surprise & sadness

Developed a real-time Facial Emotion Recognition (FER) system using deep learning models. Leveraged PyTorch and OpenCV with a dedicated pre-trained model to classify human emotions such as happy, sad, angry, and more.

A deep learning–based Facial Emotion Recognition System built using PyTorch and deployed with a Flask web application. Detects emotions like Happy, Sad, Angry, Neutral, Fear, and Surprise from images or webcam captures.

Facial Emotion Recognition with [Deep Learning Framework, e.g., TensorFlow/PyTorch] and OpenCV. This project demonstrates real-time facial emotion recognition, capturing video from a webcam, identifying faces, and predicting emotions like happiness, sadness, anger, and more.

Create an emotion classification model using deep learning and natural language processing. Utilize Python, TensorFlow, or PyTorch for efficient neural network development. Train on emotion-labeled data to accurately identify sentiments like joy, sadness, and anger in text.

This project uses deep learning to detect and classify human emotions from facial expressions in images. Built with PyTorch, the model is trained on a labeled dataset of facial images and classifies emotions such as happiness, sadness, anger, surprise, and more.

Real-time Face Emotion Recognition using OpenCV and PyTorch This project detects faces from a webcam feed and recognizes emotions like happy, sad, angry, etc., using a deep learning model. Built with OpenCV and facial_emotion_recognition library for live emotion classification.

Real-time Face Emotion Recognition using OpenCV and PyTorch This project detects faces from a webcam feed and recognizes emotions like happy, sad, angry, etc., using a deep learning model. Built with OpenCV and facial_emotion_recognition library for live emotion classification.

A real-time facial emotion recognition system using deep learning and image processing. Built with a modified VGG16 CNN model trained on the FER-2013 dataset, this project uses PyTorch and OpenCV to classify emotions (happy, sad, angry, surprised, neutral) from live webcam input.

A deep learning-based music mood classification system that analyzes audio features to predict moods like Happy, Sad, Energetic, and Calm. It uses Librosa for feature extraction, PyTorch for classification, and the YouTube Data API to recommend mood-based playlists, along with audio visualizations for analysis.

Developed an emotion recognition system that classifies human emotions (e.g., happy, sad, angry) from speech audio using Mel Frequency Cepstral Coefficients (MFCCs) and a deep learning model in PyTorch. Preprocessed .wav audio data, extracted relevant acoustic features, and trained a fully connected neural network to predict emotional labels

Facial Emotion Recognition AI detects emotions like happiness, sadness, and anger using deep learning and OpenCV. It uses a CNN model for real-time or image-based emotion classification. Features include pretrained models, OpenCV-based face detection, and API deployment support. Built with Python, TensorFlow/PyTorch, and Flask/FastAPI.

Multi-task deep learning pipeline for classifying emotions (angry, disgust, fear, happy, neutral, sad, pleasant surprise) and age group (young, old) from speech audio using the TESS dataset. Features include audio preprocessing (log-mel spectrograms), silence trimming, multi-head CNN model, Urdu translation support, and training with PyTorch

In this repository I have built and trained a multimodal deep neural network for emotion detection using tf.keras/pytorch. Here, I worked with the FER2013 dataset, which contains more than 28000 images. The images are automatically gathered, so there can be mislabeled or bad quality samples as well. Every image has a single label from the following list: Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral.