Inclass Kaggle competition for Bird Classification

Bird Classification

This Kaggle competition, with the rest of the Object recognition and computer vision MVA Class 2018/2019, was on a subset of the Caltech-UCSD Birds-200-2011 bird dataset.

Link : https://www.kaggle.com/c/mva-recvis-2018

Rank on the leaderbord : 4th

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. The training/validation/test images used for this model can be downloaded from here. The test image labels are not provided.

Training and validating the model

For the model, we used two pretrained models (ResNet152 and InceptionV3) to extract two features vectors. A classifier is added to classify the images using the stacked extracted features. See model.py and the attached paper for more details.

To train the model with default parameters, run the following command :

python main.py

By default :

• The images are loaded and resized to 331x331 pixels and normalized to zero-mean and standard deviation of 1. See data.py for the data_train_transforms. In order to preserve the ratio of the images, a padding option is available by running the following command (this option is disabled by default) :

python main.py --pad

• The data is augmented by preprocessing the images using YoloV3 to detect birds and add cropped images centered on the birds. The outputed images are saved at bird_dataset_output. See YOLO_model.py for the YoloV3 code. To deactivate the detection process and train on the original training and test sets, run the following command :

python main.py --no-crop

An other option for training the model on the training and validation sets is available by running the following command (this option is disabled by default):

python main.py --merge

See attached paper for default training parameters.

Evaluating the model on the test set

As the model trains, model checkpoints are saved to files such as model_x.pth to the current working directory. You can take one of the checkpoints and run:

python evaluate.py --data [data_dir] --model [model_file]

That generates a file kaggle.csv that you can upload to the private kaggle competition website.

By default, the cropped images (bird_dataset_ouput) are used as the default directory.

Acknowledgments

Adapted from Rob Fergus and Soumith Chintala https://github.com/soumith/traffic-sign-detection-homework for the third assignment of the Object recognition and computer vision MVA Class 2018/2019, taught by J. Ponce, I. Laptev, C. Schmid, and J. Sivic