Computer Vision Projects
Explore the Future with Our AI Projects
Welcome to our AI Projects section, where innovation meets intelligence!
Build a Face Recognition System Using FaceNet in Python
A powerful face recognition system leveraging MTCNN for detection and InceptionResnetV1 for embedding extraction, offering reliable face matching and similarity detection.
Automatic Eye Cataract Detection Using YOLOv8
Automatic Eye Cataract Detection is an AI-based tool leveraging YOLOv8 for precise and quick cataract diagnosis, enhancing efficiency and accuracy in eye care.
Crop Disease Detection Using YOLOv8
This project utilizes YOLOv8 to build a crop disease detection and classification system in Google Colab. The system processes images and videos to identify diseases, providing an interactive interface for real-time analysis using Gradio.
Banana Leaf Disease Detection using Vision Transformer model
Banana Leaf Disease Detection leverages Deep Learning and Computer Vision to identify plant diseases early, helping farmers protect their crops, improve cultivation, and reduce losses with smarter, more sustainable farming methods.
Leaf Disease Detection Using Deep Learning
Our project uses deep learning to detect leaf diseases from images. By training models like VGG16 and EfficientNet on a robust dataset, we accurately diagnose plant conditions, aiding farmers in early disease detection and promoting healthier crops.
Image Generation Model Fine Tuning With Diffusers Models
Diffusers and stable diffusion models can be used to improve image production. This project enables realistic synthesis with advanced deep learning techniques, interactive image creation via Gradio UI, and customizable training.
Real-Time Human Pose Detection With YOLOv8 Models
YOLOv8 is used in this project to identify human poses in real time. As the COCO dataset is used to train the model, its performance is checked, and poses in photos and videos are predicted. Pose recognition compresses the video output so that it can be s
Real-Time License Plate Detection Using YOLOv8 and OCR Model
YOLOv8 and OCR models are used for accurate and quick results in automated license plate identification and recognition.
Medical Image Segmentation With UNET
You can improve UNet training by using checkpoints, LR adjustments, label encoding, and seeing examples to make sure they work.