"Train a ResNet-n neural network to classify 250 Bird Species dataset with 35215 training images, 1250 test images, and 1250 validation images. Follow along as the students use Jovian library and Google Colab to prepare and explore the dataset."
In this project, I trained a
ResNet-n (n=9) neural networks architecture with a different layers to classify a diverse set of 250 Bird Species from the Kaggle dataset. In this project, I used the 250 Birds Species Dataset, which consists of 250 bird species. 35215 training images, 1250 test images(5 X 250 species) and 1250 validation images(5 X 250 species). All images are 224 X 224 X 3 color images in jpg format. Also includes a “consolidated” image set that combines the training, test and validation images into a single data set.
Let's start by installing jovian library for link my notebook with my profile.
# Jovian Commit Essentials # Please retain and execute this cell without modifying the contents for `jovian.commit` to work !pip install jovian --upgrade -q import jovian # jovian.utils.colab.set_colab_file_id('1lgTTsO6CuYS49m24tKeRN6uSpkipyE3Q') jovian.utils.colab.set_colab_file_id('1VYHHrXA8mAi2fQi5tTmjiTLCa0bc_WjL')
Mounting the google drive