Exploration of Deep Learning in Computer Vision with Transfer Learning
Task 7 Task: Exploration of Deep Learning in Computer Vision with Transfer Learning Deadline for Presentation: Thursday, May 2nd Team Members: Maximum four allowed Objective: This task aims to familiarize trainees with deep learning techniques in computer vision, with a focus on transfer learning. Trainees will work on a computer vision problem, utilize transfer learning with pre-trained models, and evaluate the performance of the transferred models. 1. Dataset Selection (15 marks): • • Choose a computer vision dataset suitable for classification or object detection tasks. The dataset should be publicly available and appropriate for transfer learning experiments. Ensure the dataset contains a sufficient number of images and corresponding labels for training and evaluation. 2. Transfer Learning Implementation (35 marks): • • • • • Preprocess the selected dataset, including data augmentation techniques such as rotation, flipping, and resizing. Choose a pre-trained deep learning model (e.g., VGG, ResNet, Inception) suitable for transfer learning. Implement transfer learning by loading the pre-trained model and fine-tuning it on the selected dataset. Train the transferred model on the dataset and monitor its performance during training. Evaluate the performance of the transferred model on a separate validation set and compare it with the performance of a model trained from scratch. 3. Model Comparison and Evaluation (30 marks): • • • Compare the performance of the transferred model with that of a model trained from scratch using appropriate evaluation metrics (e.g., accuracy, precision, recall). Analyze the strengths and weaknesses of the transferred model compared to the model trained from scratch. Discuss the implications of transfer learning in computer vision tasks and its potential benefits for real-world applications. 4. Fine-tuning and Hyperparameter Tuning (15 marks): • • • Fine-tune the hyperparameters of the transferred model to optimize its performance further. Experiment with different hyperparameters such as learning rate, batch size, and optimizer settings. Report on the impact of hyperparameter tuning on the performance of the transferred model. 5. Presentation (5 marks): • • Prepare a concise presentation summarizing the dataset, transfer learning implementation, model comparison, and evaluation. Present the findings to peers, highlighting key insights, challenges encountered, and lessons learned during the experimentation process. Total Marks: 100 Note: • • • Trainees are encouraged to seek guidance from instructors and peers, experiment with different pre-trained models, and explore additional techniques for improving model performance. Transfer learning offers a powerful approach to leverage pre-trained models and adapt them to new tasks, reducing the need for extensive training data and computational resources. The presentation aims to provide a clear and comprehensive overview of the experimentation process and findings related to transfer learning in computer vision tasks.
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