This repository serves as a roadmap to my advanced Computer Vision projects implemented with TensorFlow.
Please feel free to contribute in these repositories.
-
Transfer Learning for binary classification (link)
This repository provides a practical guide on using transfer learning for binary classification tasks using TensorFlow.
-
Transfer Learning for multi-class classification (link)
This repository contains code and resources for performing multi-class classification on the CIFAR-10 dataset using transfer learning.
-
Object Localization and Classification with One Network on MNIST Dataset (link)
In this notebook, you'll build a CNN from scratch to:
-
classify the main subject in an image localize it by drawing bounding boxes around it. You'll use the MNIST dataset to synthesize a custom dataset for the task:
-
Place each "digit" image on a black canvas of width 75 x 75 at random locations. Calculate the corresponding bounding boxes for those "digits". The bounding box prediction can be modelled as a "regression" task, which means that the model will predict a numeric value (as opposed to a category)
-
Calculate the IOU (Intersection Over Union) metric to evaluate the model's performance
-
-
Predicting Bounding Boxes for Object Detection (link)
- In this repository we use tensorflow hub pretrained modules to detect objects in images and draw bounding boxes around the detected objects using the outputs.
-
Interactive Eager Few Shot Od Training Colab (link)
- In this repository we demonstrate fine tuning of a (TF2 friendly) RetinaNet architecture on very few examples of a novel class after initializing from a pre-trained COCO checkpoint.
-
Few-Shot Learning with Only 5 Images (link)
- In this repository, we leverage the power of few-shot learning combined with a transfer learning approach to tackle the task of object detection.