Objective
- Continue gaining experience with PyTorch and helper libraries
- Understand the VOC Object Detection Dataset
- Train and evaluate the SSD neural network architecture
- Perform an ablation study testing a different base network and learning rate schedule
- Learn the Non-Maximum Suppression (NMS) algorithm
Resources and Instructions Environment Setup:
We recommend using Google Colab to complete this assignment.
1. Create a folder called “ece495_assignment4” within your Google Colab “Colab Notebooks” folder.
2. Upload the assignment ipynb, utils.py and json files to the Google Colab “ece495_assignment4” folder
3. Open the assignment
• Runtime -> change runtime type
• Set hardware accelerator to GPU
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Assignment:
2. Ablation study on using a different network base
• Model A: Train and evaluate the SSD network with the default VGG base.
• Model B: Implement the ResNetBase class. Then train and evaluate this model.
3. Ablation study on updating the learning rate
• Model C: Train and evaluate the SSD network with the default VGG base but also with a PyTorch learning rate scheduler.
4. Answer 2 questions on the differences from the NMS pseudo code described in the lectures / tutorial and the implemented version in the code.
Deliverable HTML output:
In the Jupyter notebook, go to File > Download as > HTML (.html) Submit a ZIP file containing the HTML output. Please follow the naming convention of your zip file: a4_<user_id>.zip
