Bird-Species-Classification-Using-Transfer-Learning

This project implements bird species classification using transfer learning (VGG16bn and ResNet18).

Dataset

The dataset contains 12,000 images of 200 bird species. We will be working on a small subset of this dataset with 20 bird species having 743 training images and 372 images for validation.

Caltech-UCSD Birds-200-2011 (CUB-200-2011): https://sites.google.com/visipedia.org/index

• Download here: http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz

This directory contains a folder CUB_200_2011 with all the images and two files: train.csv and val.csv. Each line of these files correponds to a sample described by the file path of the image, the bounding box values surrounding the bird, and the respective class label for each species from 0 to 19 (separated by commas). Given the very small size of this subset, we will rely on transfer learning (otherwise we will be facing the curse of dimensionality).

Testing Environment

• Pytorch version: 1.0.0
• CUDA version: 9.0.176
• Python version: 3.6.8
• CPU: Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz
• GPU: GeForce GTX 1080 Ti (11172MB GRAM)
• RAM: 32GB

Usage

1. Clone this repository

git clone https://github.com/lychengr3x/Bird-Species-Classification-Using-Transfer-Learning.git

2. Download dataset

cd Bird-Species-Classification-Using-Transfer-Learning/dataset
wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz
tar xvzf CUB_200_2011.tgz
rm CUB_200_2011.tgz

3. Train the model

cd ../src
python main.py

PS: Read argument.py to see what parameters that you can change.

Demonstration and tutorial

Please see demo.ipynb for demonstration, and tutorial.ipynb for tutorial.