Facial Expression Recognition (FER) for Mental Health Detection applies AI models like Swin Transformer, CNN, and ViT for detecting emotions linked to anxiety, depression, PTSD, and OCD. It focuses on AI for mental health, emotion detection using OpenCV Python, and real-time applications in healthcare and HR systems.

Classification system for identifying different breeds of dogs using cutting-edge vision transformer models. The models used include ViT, Swin, BEiT, DeiT, and LeViT.

Upload pics version of emotion detection model which detects faces using haarscascade and passes the image(converted to numpy array) to the ViT transformer model which in turn predicts the emotions. There are 5 classes or Emotions that the model was trained on. Happy, Sad, Angry, Surprised and Neutral.

This repository offers a straightforward implementation of Vision Transformers (ViT), specifically designed for computer vision tasks using PyTorch. Dive into efficient and practical transformer applications for image recognition.

pytorch implement vit transformer

Extracting Pictures from csv to ViT model with only 61% accuracy(self reference)

This repository contains the implementation of a Diabetic Retinopathy Classification project using three state-of-the-art deep learning models: Swin Transformer, Vision Transformer (ViT), and YOLOv11m. The goal of this research is to detect and classify diabetic retinopathy from fundus images into five distinct classes (Class 0 to Class 4).

[E-Commerce] Meesho Hackathon on Predicting attributes from supplier uploaded images as part of cataloging is extremely crucial for any E-commerce platform