Welcome to my Computer Vision Portfolio, showcasing a collection of projects that leverage the power of computer vision to solve real-world challenges. As an AI engineer deeply passionate about machine learning and deep learning, I've dedicated my efforts to mastering Python and Rust programming languages. Each project reflects a commitment to efficiency, fundamentals, and innovative solutions.
Feel free to explore the diverse projects presented here, each designed to demonstrate the application of computer vision in solving unique problems. Whether it's image classification, disease detection in crops, or aiding farmers with pest control, these projects highlight my dedication to pushing the boundaries of AI technology.
1. Kenyan Sign Language Classification
Overview: Classifying various Kenyan Sign Languages into different categories.
Aim: Enhancing communication and accessibility for the deaf community.
Technologies: Python,Pytorch, Fastai, Gradio.
2. Makerere Fall Armyworm Disease Detection
Overview: Detecting fall armyworm infestation in crops using machine learning.
Aim: Addressing the impact of fall armyworm on maize production in Africa.
Technologies: Python, Fastai, Pytorch
3. Makerere Passion Fruit Disease Detection Challenge
Overview: Developing a model to classify disease status in passion fruit plants.
Aim: Empowering smallholder farmers with a diagnostic tool for crop management.
Technologies: Python, Fastai, Pytorch
4. Microsoft Rice Prediction Challenge
Overview: Predicting the disease of a rice plant using RGB and Infrared images.
Aim: Early detection of rice blast disease to aid farmers in crop management.
Technologies: Python, Fastai, Pytorch
5. Digital Africa Plantation Counting Challenge
Overview: Developing a semi-supervised algorithm to count palm oil trees in images.
Aim: Facilitating precision agriculture and aiding farmers in crop yield estimation.
Technologies: Python, Fastai, Pytorch
6. Wadhwani AI Bollworm Classification Challenge
Overview: Improving a pest control app by classifying images with bollworm moths.
Aim: Enhancing accuracy in bollworm pest detection to guide informed pest control decisions.
Technologies: Python, Fastai, Pytorch
7. Computer Vision for License Plate Recognition
Overview: Developing a model to detect and recognize license plates in images.
Aim: Enhancing security and surveillance systems with automated license plate recognition.
Technologies: Python, OpenCV, PyTorch.
Continuing to explore innovative solutions in computer vision, I am committed to advancing AI technology for impactful applications. Stay tuned for updates and new projects that push the boundaries of what's possible in the realm of computer vision.
Thank you for visiting my Computer Vision Portfolio!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent