Animalica

Multiclass Classification | Transfer Learning | Class Imbalance | Model Serving

What Is It?

Animalica is an multiclass image classification project with 10 output classes, i.e., 10 classes of 10 different animals. The project uses Animals-10 dataset. According to the information given by the dataset provider, the data is collected from Google Images and verified by a human. The dataset contains around 26,000 images.

Challenges:

1.  Classifying more than 26,000 images into 10 classes results in fewer images per category.
2.  The dataset is imbalanced, i.e., few categories have a large number of images compared to others.
3.  Images are varying in size and medium in quality.

Aim of the project is to handle class imbalance in multiclass image classification and serve the best model using flask API.

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Summary

At first, the image data is analyzed to understand the structure of the dataset, the number of classes and images per class, as well as the quality of the images (data_analysis.ipynb). Image sizes are analyzed seprately (image_size_analysis.ipynb). Image sizes are found to be of varying sizes, thus suggesting that progressive resizing could lead to better results.

Shallownet (shallownet.py), Lenet (lenet.py), and miniVGGnet (minivggnet.py) model architectures are implemented from scratch. Whereas, MobileNetV3Large (mobilenetv3l.py) and ModelNetV3Small (mobilenetv3s.py) model architecture are specifically chosen for transfer learning. Why? What could be the application of identifying animals through images?

• In forests, to send alerts when specific animal is identified through surveillance camera or drones.
• Common users, using a mobile application to identify animals.

In both the mentioned scenarios, mobile and embedded vision system is used and MobileNet model architecture is specifically desined for it. Resnet50v2 (resnet50v2.py) model architecture is used to compare performance with MobileNetV3 model architectures.

Class imbalance issue is handled using class weights.

Training results, and observations from various experiments (using both transfer learning and fron scratch) carried out are explained in depth in training files (notebooks).

Summarizing top results:

Rank	Model Architecture	Model Number	Trainable Parameters	Training Loss	Training Acc	Validation Loss	Validation Acc
1	MobileNetV3Large	1	9,610	0.1080	0.9818	0.1134	0.9716
2	MobileNetV3Large	2	124,298	0.0939	0.9842	0.1100	0.9731
3	ResNet50V2	-	20,490	0.1240	0.9780	0.1517	0.9593
4	MobileNetV3Small	2	75,146	0.1769	0.9687	0.1780	0.9459

NOTE: 'Model Number' is the number given to a particular configuration of the given model architecture. Refer training notebooks for in depth experiments, their configurations, and results.

NOTE: Trainable parameters are result of the model configuration. Therefore, high number of trainable parameters is seen in MobileNetV3Small Model 2.

Although, MobileNetV3Large Model 2 performs slightly better than MobileNetV3Large Model 1, however, the later one uses approximately 12X fewer trainable parameters compared to the former. Therefore, MobileNetV3Large Model 2 is chosen as the best model.

All the four models are saved in H5 format in Models directory.

Model is served using flask API. Predictions are done by loading the saved MobileNetV3Large Model 1. All the required html, css, and python files for model serving are in Model Serving folder.

Upload Image	Predicted Label

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Directory Structure

├── Data Analysis                         # Data analysis files
    ├── data_analysis.ipynb               # Data analysis (notebook) 
    └── image_size_analysis.ipynb         # Image size analysis (notebook)
├── Model Serving                         # Model serving files
    ├── Static                            # Staic files
        ├── css                           # CSS files
            ├── ...      
        ├── uploads                       # Contains uploaded images
            ├── ...      
    ├── templates                         # All HTML files
        ├── ...                           
    ├── app.py                            # Runs flask
    ├── app_helper.py                     # Gets prediction 
├── Models                                # Saved trained model files
    ├── mobilenetv3l_basic_10_0.113.h5    # MobileNetV3Large model 1 
    ├── mobilenetv3l_dense_04_0.110.h5    # MobileNetV3Large model 2
    ├── mobilenetv3s_dense_07_0.178.h5    # MobileNetV3Small model 2
    └── resnet50v2_basic_06_0.152.h5      # ResNet50V2 model
├── Scripts                               # Scripts 
    ├── helper_functions.py               # Functions to make life easy
    ├── lenet.py                          # LeNet model architecture (from scratch)
    ├── minivggnet.py                     # MiniVGGNet model architecture (from scratch)
    ├── mobilenetv3l.py                   # MobileNetV3Large model architecture (transfer learning)
    ├── mobilenetv3s.py                   # MobileNetV3Small model architecture (transfer learning)
    ├── resnet50v2.py                     # ResNet50V2 model architecture
    └── shallownet.py                     # ShallowNet model architecture (from scratch, baseline)
├── 1_training_shallownet.ipynb           # Shallownet training (notebook) 
├── 2_training_lenet.ipynb                # Lenet training (notebook)
├── 3_training_miniVGGnet.ipynb           # MiniVGGnet training (notebook)
├── 4_training_mobileNetV3Small.ipynb     # MobileNetV3Small training (notebook)
├── 5_training_mobileNetV3Large.ipynb     # MobileNetV3Large training (notebook)
├── 6_training_resnet50v2.ipynb           # Resnet50V2 training (notebook)

NOTE: Download the dataset from here.

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Languge and Libraries

• Language: Python
• Libraries: Tensorflow, Keras, Scikit-Learn, Matplotlib, Seaborn, Numpy, Pandas, Flask, Shutil, PIL, OS, Imutils.

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Final Notes

To run the entire project use JupyterLab or similar IDE.

Notebooks can also be run directly on google colab (make sure to upload required .py files in working directory if required).

To run the flask script:

$ python app_helper.py

NOTE: Notebooks may use python scripts to run.

NOTE: High RAM is required to run the project. GPU can make life easier.

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