AdrienBenamira / bird_classification_MVAchallenge

Kaggle competition Caltech-UCSD Birds 20 classes classification assignment for MVA computer vision's course

Object recognition and computer vision 2018/2019

Assignment 3: Image classification

This is an implementation that achieves 91.712 % in the Kaggle challenge RecVis-MVA course 2018-2019 (1st place solution).

Report is avalaible here

Requirements

1. Install PyTorch from http://pytorch.org

2. Run the following command to install additional dependencies

pip install -r requirements.txt

Dataset

We will be using a dataset containing 200 different classes of birds adapted from the CUB-200-2011 dataset. Download the training/validation/test images from here. The test image labels are not provided.

Training and validating your model

Crop the image

Run the jupyter crop_bird

Extract the features

Git clone the repo https://github.com/richardaecn/cvpr18-inaturalist-transfer

Run it for the global images and the cropped images

Evaluate

Run main&evaluation_Regression.py

Resultats are :

	Linear Regression	Perceptron
Validation set	0.94175	0.95151
Test set	0.90322	0.90322

Acknowledgments

Adapted from https://github.com/richardaecn/cvpr18-inaturalist-transfer

@inproceedings{Cui2018iNatTransfer,
  title = {Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning},
  author = {Yin Cui, Yang Song, Chen Sun, Andrew Howard, Serge Belongie},
  booktitle={CVPR},
  year={2018}
}

and https://github.com/chainer/chainercv

@inproceedings{ChainerCV2017,
    author = {Niitani, Yusuke and Ogawa, Toru and Saito, Shunta and Saito, Masaki},
    title = {ChainerCV: a Library for Deep Learning in Computer Vision},
    booktitle = {ACM Multimedia},
    year = {2017},
}