Object Detection and Face Recognition model

I developed an Object Detection and Face Recognition Model using deep learning to detect and identify objects and human faces in images and videos. This project combines computer vision techniques with machine learning to achieve high accuracy in real-time detection and recognition.

Key Features & Capabilities:

Object Detection: Uses YOLO (You Only Look Once), Faster R-CNN, or SSD to detect multiple objects in an image with high speed and accuracy.

Face Recognition: Implements FaceNet, OpenCV, or DeepFace to recognize and classify individual faces based on facial embeddings.

Real-Time Processing: Optimized for real-time detection in video streams.

Data Preprocessing & Augmentation: Improved model performance by cleaning datasets, augmenting images, and normalizing data.

Model Evaluation: Used metrics like mAP (Mean Average Precision) and accuracy to measure detection and recognition performance.

Technologies Used:

• Deep Learning Frameworks: TensorFlow, Keras, PyTorch

• Computer Vision Libraries: OpenCV, Dlib

• Object Detection Models: YOLO, Faster R-CNN, SSD

• Face Recognition Models: FaceNet, DeepFace

• Python for Implementation & Integration