https://github.com/bernas670/artwork-recognition-feup-vcom
https://github.com/bernas670/artwork-recognition-feup-vcom
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/bernas670/artwork-recognition-feup-vcom
- Owner: bernas670
- Created: 2021-06-02T15:20:14.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-07-12T16:41:51.000Z (almost 4 years ago)
- Last Synced: 2025-01-22T13:39:56.630Z (5 months ago)
- Language: Python
- Homepage:
- Size: 17.3 MB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Artwork detection (Met)
**2020/2021** - 4th Year, 2nd Semester
**Course:** *Visão por Computador* ([VCOM](https://sigarra.up.pt/feup/en/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=384980)) | Computer Vision
**Authors:** Bernardo Santos([bernas670](https://github.com/bernas670)), David Silva ([daviddias99](https://github.com/daviddias99)), Laura Majer ([m-ajer](https://github.com/m-ajer)) Luís Cunha ([luispcunha](https://github.com/luispcunha))
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**Description:** In this work we explore some common tasks of modern Computer Vision. We apply classic machine learning algorithms (SVMs) with bag-of-words descriptors and state-of-the-art deep learning architectures to a multi-class classification problem of objects in a Museum (the Met). Since the dataset is highly unbalanced dataset, we explore techniques such as data augmentation. The architectures we use are then adapted to use in a multi-label classification problem. Finally we explore the usage of CAMs (class activation mapping) to tackle a painting object detection task.
For more information on the specification see `docs/specification.pdf` and for a detailed report on our work see `docs/report.pdf`.
**Technologies:** Python, OpenCV, Jupyter Notebooks, Scikit Learn, Tensorflow, Keras
**Skills:** Object detection, artwork classificiation (single and multi-label), bag-of-words, SVM, CNNs, data augmentation, CAM (class activation maps), machine learning, deep learning
**Grade:** 19.3/20
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