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Computer Vision And Deep Learning

  • Lab1
    This is an introductory session to Python and a few libraries that are frequently used in this course (numpy, matplotlib, opencv, keras).
  • Lab2
    Implementation from scratch of a softmax classifier, trained on CIFAR-10.

    Weights visualization


    Accuracy: 41.65% on validation set
  • Lab3
    Convolution implementation and practice with tensorflow on CIFAR-10.
    • serialization
    • use ReLu activations and He initializer
    • use regularization
    • use dropout
    • use cutout (custom layer implementation)
    Accuracy: 79.3% on validation set
  • Lab4
    "The main objective of this laboratory is to familiarize you with the training process of a neural network. More specifically, you'll follow this "recipe" for training neural networks proposed by Andrew Karpathy. You'll go through all the steps of training, data preparation, debugging, hyper-parameter tuning.

    In the second part of the laboratory, you'll experiment with transfer learning and fine-tuning. Transfer learning is a concept from machine learning which allows you to reuse the knowledge gained while solving a problem (in our case the CNN weights) and applying it to solve a similar problem. This is useful when you are facing a classification problem with a small training dataset."

    • custom data generator
    • dataset used: GTSRB - German Traffic Sign Recognition Benchmark
    • experiment with ResNet blocks
    • transfer learning and fine-tuning with MobileNet as base model
    • data augmentation
    • custom implementation of cosine annealing scheduler
    • top 3 ensemble: 98.33% accuracy on validation set

  • Lab5
    "In this laboratory we'll work with a semantic segmentation model. The task of semantic segmentation implies the labeling/classification of all the pixels in the input image.

    You'll build and train a fully convolutional neural network inspired by U-Net. Also, you will learn about how you can use various callbacks during the training of your model.

    Finally, you'll implement several metrics suitable for evaluating segmentation models."

    • image segmentation on OxfordPets dataset
    • U-Net downsample and upsample path
    • skip connection
    • checkpoints, terminate on NaN, early stopping
    • mean pixel accuracy: 92.32%
    • intersection over union: 70.28%
    • frequency weighted intersection over union: 86.83%

  • Lab6
    "Visualizing what neural networks learn"

    • Display the filters in the first and second convolutional layer

    • Saliency map via image occlusions

    • Saliency maps via image derivative

    • Deep-dream or how to make a network hallucinate

Babeș-Bolyai University
Faculty of Mathematics and Computer Science
Computer Vision and Deep Learning course
Third year