None ||| Skills: Computer Vision, Machine Learning ||| URL: Link to Notebook
This Computer Vision project focuses on developing a model for image segmentation using deep learning techniques.
The design leverages several advanced tools and libraries, including TensorFlow, Keras, and Albumentations, to build and train the model effectively.
Design
Data Handling and Preprocessing: The project starts by mounting a Google Drive to access the dataset. Images are loaded and preprocessed using libraries like PIL and NumPy. Albumentations is used for augmenting the training data to improve the model's robustness and generalization.
Model Architecture: A custom deep learning model is built using the VGG16 architecture as the base. The VGG16 model is known for its efficiency in image classification tasks and is fine-tuned for segmentation in this project. The model incorporates several layers, including convolutional, batch normalization, and dropout layers to prevent overfitting. Early stopping and model checkpointing are used to ensure the best model performance during training.
Training and Evaluation: The dataset is split into training and validation sets using train_test_split. Metrics like OneHotMeanIoU are used to evaluate the model's performance, providing insights into its accuracy in segmenting images.
Outcome: The project successfully demonstrates the capability of deep learning models in performing image segmentation tasks. The use of VGG16, combined with effective data augmentation and robust training strategies, results in a model that can accurately segment images, showcasing significant potential for applications in various fields such as medical imaging and autonomous driving.The final model's performance metrics indicate a high level of precision and recall, validating the effectiveness of the design and implementation choices.