This project’s focus is a multiclass image classification task involving six distinct categories: buildings, forests, mountains, seas, streets, and glaciers.
The primary aim is to develop a robust deep learning model capable of accurately identifying and classifying images of landscapes and urban environments.
The importance of this problem lies in its applicability and relevance in various domains. Accurate image classification has practical applications:
- Urban planning: By accurately identifying buildings and streets from aerial or satellite images, city planners can analyze urban infrastructure, identify areas for improvement, and make informed decisions regarding city development and expansion.
- Environmental monitoring: Classifying images of forests, mountains, and glaciers can aid in monitoring ecosystem health, studying deforestation patterns, assessing climate change impacts, and supporting conservation efforts.
- Tourism and travel: Efficiently categorizing images of seas, beaches, mountains, and buildings can enhance travel experiences by providing accurate and visually appealing recommendations for tourists.
- Remote sensing: Interpreting satellite imagery, enabling analysis of large-scale landscapes, and assisting in disaster management, agriculture, and forestry.
Assumption 1: Sufficient and Representative Data
The dataset contains a large and diverse collection of images representing various lightings and environmental settings for each class, ensuring the model learns robust features and can generalize well to unseen images.
Assumption 2: Class Separability
The classes in the dataset are reasonably separable based on visual features, allowing our model to learn distinctive patterns and accurately classify images into the correct categories.
Hypothesis 1: CNNs for Image Feature Extraction
Convolutional neural networks (CNNs) will be effective in extracting relevant features from images for multiclass image classification. CNNs have proven to be highly successful in image-related tasks due to their ability to capture spatial hierarchies of features.
Hypothesis 2: Model Generalization
The trained model will generalize well to unseen images from the same classes. By leveraging techniques such as regularization and data augmentation during training, the model will learn meaningful and robust features that can accurately classify images.
Description:
This is image data of Natural Scenes around the world. In this project, the data used was 7k images of size 150x150 pixels. Data distributed under 6 category labels: 'mountain', 'street', 'glacier', 'building', 'sea', 'forest'
Source:
Initially published on the Datahack Analytics Vidhya Website by Intel to host a Image classification Challenge.
Balanced Class Distribution:
Image Transformation:
Image Class Label Transformation:
dense_units:
- Determines the number of units/neurons in the dense (fully connected) layer of the model.
- It is an integer value chosen from a range defined by min_value=128, max_value=512, and step=64.
dropout_rate:
- Controls the dropout rate, which is the fraction of input units to drop during training.
- It is a float value chosen from a range defined by min_value=0.2, max_value=0.5, and step=0.1.
learning_rate:
- Represents the learning rate of the optimizer used for training the model.
- It is a choice value selected from the options [0.001, 0.01, 0.1].
Best Hyperparameters: dense_units: 128 dropout_rate: 0.30000000000000004 learning_rate: 0.001
Regularization:
- Weight Regularization (L2 Regularization):
- Set the base_model.trainable attribute to False, which freezes the weights of the pre-trained InceptionResNetV2 model. Prevent the weight from being updated during training.
- Batch Normalization:
- Used Batch normalization to normalize the activations of the previous layer, stabilizing the training process. It reduces the effect of internal covariate shift.
- Dropout Regularization:
- Applied Dropout regularization to the dense layer in the model. Dropout randomly sets a fraction of the input units to 0 during training, which helps in preventing overfitting, which helps in preventing overfitting.
Epochs:
- Specifies the number of times the entire training dataset will be passed through the model during training.
- Set it to 10 since, at 10, it was providing optimal results.
Train Accuracy: 0.956 Test Accuracy: 0.903
No Overfitting or Underfitting High Accuracy and Low Loss. Similar Values for Train and Test Sets.
The model with the Inception ResNet V2 base was the best model based on accuracy and loss, making it the best model for the multi-class image classification task. Image Feature Transformations of the data are crucial for model building, enhancing the model’s ability to capture relevant patterns of the images. Transfer learning enhances image classification by leveraging pre-trained models' knowledge. It can reduce training time, improve performance, and facilitate effective feature extraction for a specific domain, in my case multi-class image classification.
Resource Requirements and Computational Speed
- Loading images, as well as training and testing image classification models demanded significant computational resources. With limited computational resources, the models and images took significant time to run and load, respectively. Domain-Specific Generalization
- For transfer learning, specific pre-trained models are needed for a particular domain. For example, models trained on medical images might struggle to classify natural scenes accurately, requiring specific domain adaptation or additional training on domain-specific data.
Experiment with more model advancement tools:
- Incorporate Data Augmentation
- Incorporate other fine-tuning methods Exploration of Advanced Models:
- Investigate the potential of more advanced deep learning architectures. For example, architectures like EfficientNet, ResNeXt, or DenseNet. Evaluation on External Datasets:
- Validate the model's performance and generalization capabilities on external datasets related to landscape and urban image classification.
- Urban planning: Identify buildings and streets from aerial or satellite images to analyze urban infrastructure.
- Environmental monitoring: Classifying images of forests, mountains, and glacier to aid in monitoring ecosystem health.
- Tourism and travel: Categorizing images of seas, beaches, mountains, and buildings can enhance travel experiences by providing accurate and visually appealing recommendations for tourists.
- Remote sensing: Interpreting satellite imagery and enabling analysis of large-scale landscapes can assist in disaster management.