Object Detection with CNNs, ViT, and YOLO
This project aims to implement and compare the performance of several object detection algorithms, specifically Faster R-CNN with a Vision Transformer (ViT) backbone, and You Only Look Once (YOLO), using the Pascal VOC 2007 dataset. The goal is to evaluate these models based on their accuracy, efficiency, and speed in detecting objects across various categories in the dataset.
Source: The Pascal Visual Object Classes (VOC) 2007 dataset.
Content: The dataset contains images across 20 categories with annotations, including object labels and bounding boxes.
Task: Object detection, requiring the model to predict both the classes and the locations of objects in images.
Resize images to a uniform size (e.g., 800x800 pixels) for model input.
Normalize pixel values to a range suitable for neural network inputs.
Convert annotations to a format compatible with the models, including class labels and bounding box coordinates.
Augmentation (Optional): Apply data augmentation techniques such as flipping, rotation, and scaling to increase the diversity of training data and improve model robustness.
Data Loader Setup: Implement data loaders with efficient batching, shuffling, and parallel processing to streamline training and evaluation.
Loss Functions: Utilize appropriate loss functions for object detection, combining classification and bounding box regression losses.
Optimization: Apply optimizers like SGD or Adam, with learning rate schedules and regularization techniques to improve training outcomes.
Evaluate model performance using metrics such as mean Average Precision (mAP), precision, recall, IoU and F1 score. Measure inference speed and computational efficiency to compare model practicality in real-world applications.
Conduct a thorough analysis of the models' performance across different object categories and under varying conditions (e.g., object sizes, occlusion). Compare the models qualitatively by visualizing detection results, highlighting strengths and weaknesses.
Results: Compile detailed results, including quantitative metrics and qualitative assessments, in a structured report or presentation.
Insights: Discuss insights gained from the comparison, including the impact of architectural choices on performance and potential areas for further research or application.
Code Documentation: Ensure the project code is well-documented, with clear explanations of the implementation details and usage instructions.
Note: ReadMe generated with GPT
Summarize the key findings from the comparison, emphasizing practical implications for object detection tasks. Suggest avenues for future research, such as exploring hybrid models, applying the algorithms to other datasets, or integrating additional enhancements to improve accuracy and efficiency