Animal Classification

Introduction

Hi there, I am Thanh Minh Truong - A student studying Deep Learning/AI/Computer Vision.

This is my project about classify 10 types of animal ( introduced in Categories)

Here I used Pytorch framework for this project, this includes:

1. Preprocessing and validating input images and labels
2. Build CNN architecture
3. Model training
4. Model evaluation
5. Testing

I will demo the results in two formats: a list of images and a video containing images of different types of pets

Dataset

In this project, I have 10 classes and each class contains about 2000 images of animals ( cat, dog, elephant,...)

Directory structure of this folder, like this :

Dataset
  |__ train  
      |_ butterfly 
          |__ pic1.jpg 
          |__ pic2.jpg        
          |__ ....
      |__ cat
          |__ pic1.jpg   
          |__ pic2.jpg    
          |__ ....     
      |__.....  
  |__test
      |__ butterfly
          |__ pic1.jpg
          |__ pic2.jpg     
          |__ ....
      |__ cat
          |__ pic1.jpg   
          |__ pic2.jpg    
          |__ ....        
      |__ .....

The input (images) for training be changed to Tensor Pytorch [Batch, Channel, Height, Width] where Height = Width = 224 and Channel = 3. Values should be between 0 and 1

Categories

butterfly	cat
chicken	cow
dog	elephant
horse	sheep
spider	squirrel

Model

With this topic, I will build model based on VGG16 architecture, like this:

However, I have changed some parameters, especially the top layer ( Fully connected, Dropout, ....) to match my dataset.

We will see the model configuration in src/model.py

Training

I will run this script on the terminal :

python train.py -b 32 -e 50 -o Adam -l 0.001

Checkpoints

I save the checkpoint to the checkpoint/best.pt folder folder after the training process

Experiments

Confusion matrix

Test

After training process, We were have a checkpoint save model parameters, I will test with some images I downloaded in Google and use matplotlib to show results:

Test with videos

Besides testing with images, I also tried to test with animal videos I downloaded on YouTube

This is my results:

Requirements

• python 3.10
• torch 2.1.1
• opencv-python 4.8.1
• matplotlib 3.8.2
• tqdm 4.66.1
• numpy 1.26.2
• scikit-learn 1.3.2
• tensorboard 2.15.1
• torchvision 0.16.1
• Pillow 10.1.0