https://github.com/jjonescz/ibb-assignment3
Ear recognition using CNN based on EfficientNet-B0 (Assignment 3 for Image Based Biometry course at University of Ljubljana)
https://github.com/jjonescz/ibb-assignment3
cnn ear-recognition efficientnet tensorflow
Last synced: about 1 year ago
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Ear recognition using CNN based on EfficientNet-B0 (Assignment 3 for Image Based Biometry course at University of Ljubljana)
- Host: GitHub
- URL: https://github.com/jjonescz/ibb-assignment3
- Owner: jjonescz
- Created: 2021-01-13T17:54:55.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-01-15T21:24:48.000Z (over 5 years ago)
- Last Synced: 2025-06-08T16:42:42.527Z (about 1 year ago)
- Topics: cnn, ear-recognition, efficientnet, tensorflow
- Language: TeX
- Homepage:
- Size: 29.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IBB Assignment 3
This repository contains source code of my solution of Assignment 3 for Image
Based Biometry course at University of Ljubljana.
Report with IMRAD structure is available as [release asset](https://github.com/jjonescz/ibb-assignment3/releases).
## Requirements
Python 3.8.2 was used with the following packages installed:
```txt
matplotlib==3.3.2
numpy==1.18.5
pandas==1.1.3
tensorflow==2.3.1
```
Additionally, folder `data` must contain [AWE dataset](http://awe.fri.uni-lj.si/downloads/AWEDataset.zip) unzipped so that e.g. `data/001/01.png` is a valid path.
## Loading and training
Script `train.py` can be run as-is.
It will download EfficientNet-B0 weights and load saved weights of our CNN model trained without image augmentations.
To switch to model with image augmentations, change parameters near top of the file to contain:
```py
EXP_ID = "model-b"
AUGMENTATIONS = True
```
To enable model training, change parameters to:
```py
TRAIN = True
```
## Evaluation
Script `evaluate.py` plots figures (to folder `figures`) and prints performance metrics to console.
It uses state of models provided in folder `out`.
This state can be recomputed by executing script `train.py` (once with augmentations and once without them) as described in previous section.
## Report
Source code of LaTeX report is contained in `report/jj1712.tex`.
Before compiling it, make sure you have generated figures as described in [Evaluation](#evaluation).