Project Background:
The Vision Transformer (ViTs) revolves around a groundbreaking approach to image recognition tasks, challenging the dominance of convolutional neural networks (CNNs). Traditionally, CNNs have been the go-to architecture for computer vision tasks introduce a novel paradigm by applying the transformer architecture, originally developed for natural language processing (NLP) to images. This shift stems from the recognition that while CNNs excel at capturing spatial hierarchies in images struggle with capturing long-range dependencies. ViTs address this limitation by treating images as sequences of patches, which are then processed by transformer blocks, allowing for global context awareness. This approach has garnered significant attention due to its promising results in various image recognition benchmarks, offering potential improvements in accuracy and efficiency.
Problem Statement
- In this work, the traditional convolutional neural networks (CNNs) struggle to capture long-range dependencies in images, which are crucial for understanding context and relationships between distant regions.
- As the size and complexity of datasets increase, CNNs encounter challenges in scaling due to limitations in computational resources and memory requirements.
- Pre-trained CNNs may not transfer well to tasks with significantly different data distributions or domains, necessitating extensive fine-tuning or retraining efforts.
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CNNs typically operate on local image patches, which may limit their ability to capture global context and semantic relationships across the entire image.
Adapting the transformer architecture, originally designed for sequential data in natural language processing (NLP), to handle spatial data in images requires addressing unique challenges, such as handling 2D structures and preserving spatial information.
It is essential to establish standardized benchmarks and evaluation metrics for ViTs across various computer vision tasks to assess their performance and compare them with existing approaches. Aim and Objectives
To advance the field of computer vision by leveraging Vision Transformers (ViTs) for improved image understanding and recognition.
- Develop and optimize ViT architectures to efficiently handle spatial data and capture long-range image dependencies.
- Explore methods for scaling ViTs to large-scale datasets while maintaining computational efficiency.
- Evaluate the performance of ViTs across various computer vision tasks, including image classification, object detection, and semantic segmentation.
- Investigate transfer learning techniques to effectively adapt pre-trained ViT models to new domains and tasks.
- Foster collaboration and knowledge sharing within the computer vision community by establishing standardized benchmarks and evaluation protocols for ViTs.
Contributions to Vision Transformers
- ViTs offers a new paradigm for image recognition, leveraging transformer architectures to capture long-range dependencies and global context in images more effectively than traditional CNNs.
- Provide scalable solutions for handling large-scale datasets, enabling efficient training and inference on diverse and complex visual data.
- ViTs facilitate transfer learning by leveraging pre-trained models to efficiently adapt to new domains and tasks, reducing the need for extensive retraining efforts.
- Contribute to improved model interpretability, allowing for better understanding and analysis of model predictions through visualization techniques and attention mechanisms.
- Drive the establishment of standardized benchmarks and evaluation protocols in the computer vision community, fostering fair comparisons and advancing the state-of-the-art in image recognition tasks.
Deep Learning Algorithms for Vision Transformers
- Self-Attention Mechanism
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Multi-Head Attention
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Positional Encoding
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Feedforward Neural Networks (FNNs)
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Layer Normalization
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Residual Connections
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Dropout Regularization
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Stochastic Depth
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Adaptive Gradient Methods
Datasets for Vision Transformers
- ImageNet
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COCO (Common Objects in Context)
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CIFAR-10
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CIFAR-100
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Pascal VOC (Visual Object Classes)
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SUN397
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Caltech-256
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Oxford Flowers
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Stanford Cars
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MNIST
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
