I wanted to further explores the possibilities with machine learning and Python
Role: Code: Built a tiny web app that uses the web cam to recognize images using the Mobilenet dataset
Tools: Python, Deep learning, DLIB API, OpenCV and facial_recognition model
Credits: Tensorflow.js, face_recognition module
solution
I built a tiny web app that uses the web cam to recognize images using the Mobilenet dataset and Tensorflow.js. I also wrote a Python script that uses pre-trained facial recognition networks via the face_recognition_module built on DLIB API to recognize my boyfriend in photos. It can recognize each person's face in the photo as a face encoding and compare the encoding to a sample photo I provided.
process
I first tested the Tensorflow API by building my own dataset of vegetable photos to train the classifier to classify different vegetables on the terminal. I then discovered the Tensorflow Javascript library and tested Tensorflow and the Mobilenet dataset on the browser against pictures of my dogs using transfer learning.
Research
Prototyping
Development
