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"Train a ResNet-n neural network with different layers to classify 250 Bird Species from Kaggle dataset with over 95% accuracy. Install and import required libraries, download and explore the dataset using code snippets."

250 Bird Species Image Classification

In this Deep Neural Network project, we will be training 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 with over 95% accuracy. For this project, I used the 250 Birds Species Dataset, which consists of 250 bird species. 35215 training images, 1250 test images(5 per species) and 12500 validation images(5 per 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.

!pip install jovian --upgrade --quiet

Let's begin by installing and importing the required libraries.

# Uncomment and run the appropriate command for your operating system, if required
# No installation is reqiured on Google Colab / Kaggle notebooks

# Linux / Binder / Windows (No GPU)
# !pip install numpy matplotlib torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f

# Linux / Windows (GPU)
# pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f
# MacOS (NO GPU)
# !pip install numpy matplotlib torch torchvision torchaudio