- Implemented a self-supervised learning model on CIFAR10 using PyTorch, achieving a test accuracy of 78% for rotation prediction
- Conducted fine-tuning experiments, comparing performance between initializing from the rotation model and random weights, targeting a test set accuracy of 60%
- Trained a full ResNet18 model on supervised CIFAR10 classification, reaching an expected accuracy of 80%
- Explored advanced models and replicated a plot demonstrating the benefits of pre-training on the Rotation task with limited labeled data
- Implemented a YOLO-like object detector on the PASCAL VOC 2007 dataset, leveraging a pre-trained DetNet-inspired network structure
- Achieved objectives in line with optimal accuracy while minimizing computational expense