Object recognition and computer vision 2018/2019

Assignment 3: Image classification

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

Run the script main_yolo.py to train your model.

• By default the images are loaded and resized to 299x299 pixels and normalized to zero-mean and standard deviation of 1. See data.py for the data_transforms.
• The default arguments of main_yolo.py will git clone and use the yolo_v3 architecture to crop the image datasets and create a new folder and then train the model.

Evaluating your 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.

Acknowledgments

Adapted from Rob Fergus and Soumith Chintala https://github.com/soumith/traffic-sign-detection-homework.

This was an assignment part of the Objec Recognition class in the MVA masters by : Jean Ponce, Ivan Laptev, Cordelia Schmid and Josef Sivic.

Class link : https://www.di.ens.fr/willow/teaching/recvis18/