/Deeper-Networks-for-Image-Classification

DLCV - CW3: Performing and evaluating image classification tasks with deep CNN networks

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Deep Learning and Computer Vision (Assignment 3):

Deeper Networks for Image Classification: Performing and evaluating image classification tasks with deep CNN networks

Module Code: ECS795P

Module Leader: Shaogang Gong

Semester: 2

Submission Date: 12th May 2022

Final Grade: ?/100

Task Requirements

  1. You shall use at least two of VGG, ResNet, or GoogleNet networks. You can use more than two including other networks.
  2. You MUST use MNIST dataset for the image classification task. Moreover, we encourage you to use extra datasets (such as CIFAR, Tiny-Imagenet) to further evaluate your chosen networks.
  3. You shall submit a 6-page report (a research paper) including
    1. Critical analysis of the models;
    2. Implementation of model training and test settings, including the model training/testing process (the loss changing during training period, the train/test accuracy, etc.), to support your experimental results;
    3. Quantitative evaluation on your experimental results;
    4. Run-time screenshots.
    5. Report format: Please use the same LaTeX style as required for your MSc final project report (double-column, 11pt font size)
  4. You should submit (a) your code for model building, data loading & processing, training, evaluation, and visualisation; (b) evidence of model training and inference/test including text logs, tensorboard logs, run-time screenshots, any other logs demonstrating the training process with explicit timestamps recorded in a file/files (no need to submit the trained weights); (c) your six pages report.